diff --git a/README.md b/README.md
index 2e73dc27600a27e843171099d2506af0114f9958..8211bbea11a6600af683a9429358b21906c70d53 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ HPVM is currently at version 0.5. For more about what HPVM is, see [our website]
 The following components are required to be installed on your machine to build HPVM.
 
 * GCC (>=5.1.0)
-* CMake (>=3.4.3)
+* CMake (>=3.17.0)
 * Python (>=2.7)
 * GNU Make (>=3.79.1)
 * OpenCL (>=1.0.0) or CUDA (>=9.1, only required for GPU support)
@@ -49,6 +49,17 @@ git clone https://gitlab.engr.illinois.edu/llvm/hpvm-release.git/
 cd hpvm-release/hpvm
 ```
 
+Before installing HPVM, some paths must be set for installation to succeed. The following variables in set_paths.sh must be set:
+
+* CUDA_TOOLKIT_PATH --- Path to the CUDA toolkit
+* CUDA_INCLUDE_PATH --- Path to the CUDA headers
+* CUDA_LIB_PATH -- Path to CUDA libraries 
+
+Once the aforementioned variables in set_paths.sh have been specified, run the script.
+```shell
+source set_paths.sh
+```
+
 HPVM installer script can be used to download, configure and build HPVM along with LLVM and Clang. 
 ```shell
 bash install.sh
diff --git a/hpvm/include/FuseHPVMTensorNodes/FuseHPVMTensorNodes.h b/hpvm/include/FuseHPVMTensorNodes/FuseHPVMTensorNodes.h
index ce6725b930cefb56d23cad2799bee27c97e44783..a76b63caa4897de2aa6fe358774e32835b809eae 100644
--- a/hpvm/include/FuseHPVMTensorNodes/FuseHPVMTensorNodes.h
+++ b/hpvm/include/FuseHPVMTensorNodes/FuseHPVMTensorNodes.h
@@ -138,7 +138,7 @@ public:
 
   FindFusionTargetsTraversal(Module &_M, builddfg::BuildDFG &_DFG) :
     CodeGenTraversal(_M, _DFG) {
-/*    FPs[hpvm::PROMISE_TARGET] = { {Intrinsic::visc_tensor_conv,
+/*    FPs[hpvm::TENSOR_TARGET] = { {Intrinsic::visc_tensor_conv,
                                    Intrinsic::hpvm_tensor_add,
                                    Intrinsic::hpvm_tensor_relu,
                                    Intrinsic::hpvm_tensor_pooling
diff --git a/hpvm/include/SupportHPVM/DFG2LLVM.h b/hpvm/include/SupportHPVM/DFG2LLVM.h
index 533cad17aae26b7006d16efada7378d83a9bc840..fb1e35033eda0445f10423beb69aab5f07c093f0 100644
--- a/hpvm/include/SupportHPVM/DFG2LLVM.h
+++ b/hpvm/include/SupportHPVM/DFG2LLVM.h
@@ -174,7 +174,7 @@ bool CodeGenTraversal::checkPreferredTarget(DFNode *N, hpvm::Target T) {
   case hpvm::CPU_TARGET:
     HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cpu");
     break;
-  case hpvm::PROMISE_TARGET:
+  case hpvm::TENSOR_TARGET:
     HintNode = M->getOrInsertNamedMetadata("hpvm_hint_promise");
     break;
   default:
@@ -211,7 +211,7 @@ bool CodeGenTraversal::preferredTargetIncludes(DFNode *N, hpvm::Target T) {
   case hpvm::CUDNN_TARGET:
     HintNode.push_back(M->getOrInsertNamedMetadata("hpvm_hint_cudnn"));
     break;
-  case hpvm::PROMISE_TARGET:
+  case hpvm::TENSOR_TARGET:
     HintNode.push_back(M->getOrInsertNamedMetadata("hpvm_hint_promise"));
     break;
   case hpvm::CPU_OR_GPU_TARGET:
diff --git a/hpvm/include/SupportHPVM/DFGraph.h b/hpvm/include/SupportHPVM/DFGraph.h
index 5674aa4fc67665d9db208e317b0d936803de3c82..3da7c0b01a79d52f668795eb072fdcb6381813a9 100644
--- a/hpvm/include/SupportHPVM/DFGraph.h
+++ b/hpvm/include/SupportHPVM/DFGraph.h
@@ -357,7 +357,7 @@ public:
     case hpvm::GPU_TARGET:
       GenFuncInfo.gpu_hasCPUFunc = isCPUFunc;
       break;
-    case hpvm::PROMISE_TARGET:
+    case hpvm::TENSOR_TARGET:
       GenFuncInfo.promise_hasCPUFunc = isCPUFunc;
       break;
     case hpvm::CUDNN_TARGET:
@@ -382,7 +382,7 @@ public:
       return GenFuncInfo.gpu_hasCPUFunc;
     case hpvm::CUDNN_TARGET:
       return GenFuncInfo.cudnn_hasCPUFunc;
-    case hpvm::PROMISE_TARGET:
+    case hpvm::TENSOR_TARGET:
       return GenFuncInfo.promise_hasCPUFunc;
     case hpvm::CPU_OR_GPU_TARGET:
       assert(false && "Single target expected (CPU/GPU/SPIR/CUDNN/PROMISE)\n");
@@ -419,7 +419,7 @@ public:
       GenFuncs.CUDNNGenFunc = F;
       GenFuncInfo.cudnn_hasCPUFunc = isCPUFunc;
       break;
-    case hpvm::PROMISE_TARGET:
+    case hpvm::TENSOR_TARGET:
       if (GenFuncs.PROMISEGenFunc != NULL) {
         DEBUG(errs() << "Warning: Second generated PROMISE function for node "
                      << FuncPointer->getName() << "\n");
@@ -447,7 +447,7 @@ public:
       return GenFuncs.GPUGenFunc;
     case hpvm::CUDNN_TARGET:
       return GenFuncs.CUDNNGenFunc;
-    case hpvm::PROMISE_TARGET:
+    case hpvm::TENSOR_TARGET:
       return GenFuncs.PROMISEGenFunc;
     case hpvm::CPU_OR_GPU_TARGET:
       assert(false &&
@@ -475,7 +475,7 @@ public:
       GenFuncs.CUDNNGenFunc = NULL;
       GenFuncInfo.cudnn_hasCPUFunc = false;
       break;
-    case hpvm::PROMISE_TARGET:
+    case hpvm::TENSOR_TARGET:
       GenFuncs.PROMISEGenFunc = NULL;
       GenFuncInfo.promise_hasCPUFunc = false;
       break;
diff --git a/hpvm/include/SupportHPVM/HPVMHint.h b/hpvm/include/SupportHPVM/HPVMHint.h
index 7677b01ae2b05e74c6d0609df9ea5bbaf6e14ea9..25020e82016b8b3320abb8ddf94b78f24bc91acd 100644
--- a/hpvm/include/SupportHPVM/HPVMHint.h
+++ b/hpvm/include/SupportHPVM/HPVMHint.h
@@ -20,7 +20,7 @@ enum Target {
   CPU_TARGET,
   GPU_TARGET,
   CUDNN_TARGET,
-  PROMISE_TARGET,
+  TENSOR_TARGET,
   CPU_OR_GPU_TARGET,
   //    ALL_TARGETS,
   NUM_TARGETS
diff --git a/hpvm/include/SupportHPVM/HPVMUtils.h b/hpvm/include/SupportHPVM/HPVMUtils.h
index ff47bc0fe494a232b4b8438a0babd8bd6a507aef..9a91494a41d6109cda8a8b9b885919fd197fb768 100644
--- a/hpvm/include/SupportHPVM/HPVMUtils.h
+++ b/hpvm/include/SupportHPVM/HPVMUtils.h
@@ -395,27 +395,27 @@ bool tagIncludesTarget(hpvm::Target Tag, hpvm::Target T) {
       return true;
     return false;
   case hpvm::CUDNN_TARGET:
-    if (T == hpvm::CUDNN_TARGET)
-      return true;
-    return false;
-  case hpvm::PROMISE_TARGET:
-    if (T == hpvm::PROMISE_TARGET)
-      return true;
-    return false;
+      if (T == hpvm::CUDNN_TARGET)
+        return true;
+      return false;
+  case hpvm::TENSOR_TARGET:
+      if (T == hpvm::TENSOR_TARGET)
+        return true;
+      return false;
   default:
     assert(false && "Unknown Target\n");
   }
 }
 
 bool isSingleTargetTag(hpvm::Target T) {
-  return ((T == hpvm::CPU_TARGET) || (T == hpvm::GPU_TARGET) ||
-          (T == hpvm::CUDNN_TARGET) || (T == hpvm::PROMISE_TARGET));
+  return ((T == hpvm::CPU_TARGET) || (T == hpvm::GPU_TARGET) 
+       || (T == hpvm::CUDNN_TARGET) || (T == hpvm::TENSOR_TARGET));
 }
 
 // Add the specified target to the given tag
 hpvm::Target getUpdatedTag(hpvm::Target Tag, hpvm::Target T) {
-  assert(((T == hpvm::CPU_TARGET) || (T == hpvm::GPU_TARGET) ||
-          (T == hpvm::CUDNN_TARGET) || (T == hpvm::PROMISE_TARGET)) &&
+  assert(((T == hpvm::CPU_TARGET) || (T == hpvm::GPU_TARGET) 
+       || (T == hpvm::CUDNN_TARGET) || (T == hpvm::TENSOR_TARGET)) &&
          "The target is only allowed to be a single target: CPU, GPU, SPIR, "
          "CUDNN, PROMISE\n");
 
@@ -423,25 +423,22 @@ hpvm::Target getUpdatedTag(hpvm::Target Tag, hpvm::Target T) {
   case hpvm::None:
     return T;
   case hpvm::CPU_TARGET:
-    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::PROMISE_TARGET) &&
-           "Unsupported target combination\n");
+    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::TENSOR_TARGET) && "Unsupported target combination\n");
     if (T == hpvm::CPU_TARGET)
       return hpvm::CPU_TARGET;
     if (T == hpvm::GPU_TARGET)
       return hpvm::CPU_OR_GPU_TARGET;
     return T;
   case hpvm::GPU_TARGET:
-    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::PROMISE_TARGET) &&
-           "Unsupported target combination\n");
+    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::TENSOR_TARGET) && "Unsupported target combination\n");
     if (T == hpvm::CPU_TARGET)
       return hpvm::CPU_OR_GPU_TARGET;
     if (T == hpvm::GPU_TARGET)
       return hpvm::GPU_TARGET;
     return T;
   case hpvm::CPU_OR_GPU_TARGET:
-    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::PROMISE_TARGET) &&
-           "Unsupported target combination\n");
-    return hpvm::CPU_OR_GPU_TARGET;
+    assert((T != hpvm::CUDNN_TARGET) && (T != hpvm::TENSOR_TARGET) && "Unsupported target combination\n");
+    return hpvm::CPU_OR_GPU_TARGET; 
   default:
     assert(false && "Unknown Target\n");
   }
@@ -471,14 +468,14 @@ void addHint(Function *F, hpvm::Target T) {
     HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cpu_gpu");
     break;
   case hpvm::CUDNN_TARGET:
-    DEBUG(errs() << "CUDNN Target\n");
-    HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cudnn");
-    break;
-  case hpvm::PROMISE_TARGET:
-    DEBUG(errs() << "PROMISE Target\n");
-    errs() << "PROMISE\n";
-    HintNode = M->getOrInsertNamedMetadata("hpvm_hint_promise");
-    break;
+      DEBUG(errs() << "CUDNN Target\n");
+      HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cudnn");
+      break;
+  case hpvm::TENSOR_TARGET:
+      DEBUG(errs() << "PROMISE Target\n");
+      errs() << "PROMISE\n";
+      HintNode = M->getOrInsertNamedMetadata("hpvm_hint_promise");
+      break;
   default:
     llvm_unreachable("Unsupported Target Hint!");
     break;
@@ -510,11 +507,11 @@ void removeHint(Function *F, hpvm::Target T) {
     HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cpu");
     break;
   case hpvm::CUDNN_TARGET:
-    HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cudnn");
-    break;
-  case hpvm::PROMISE_TARGET:
-    HintNode = M->getOrInsertNamedMetadata("hpvm_hint_promise");
-    break;
+      HintNode = M->getOrInsertNamedMetadata("hpvm_hint_cudnn");
+      break;
+  case hpvm::TENSOR_TARGET:
+      HintNode = M->getOrInsertNamedMetadata("hpvm_hint_promise");
+      break;
   default:
     llvm_unreachable("Unsupported Target Hint!");
     break;
@@ -565,7 +562,7 @@ hpvm::Target getPreferredTarget(Function *F) {
   if (FoundPrefTarget("hpvm_hint_cudnn"))
     return hpvm::CUDNN_TARGET;
   if (FoundPrefTarget("hpvm_hint_promise"))
-    return hpvm::PROMISE_TARGET;
+    return hpvm::TENSOR_TARGET;
   return hpvm::None;
 }
 
diff --git a/hpvm/lib/Transforms/CMakeLists.txt b/hpvm/lib/Transforms/CMakeLists.txt
index bb044cd756883449dc775fd7742ac575aa5815a1..b18cd4551ba33e0c315a416164b45e6282098aeb 100644
--- a/hpvm/lib/Transforms/CMakeLists.txt
+++ b/hpvm/lib/Transforms/CMakeLists.txt
@@ -5,9 +5,6 @@ add_subdirectory(DFG2LLVM_CPU)
 add_subdirectory(GenHPVM)
 add_subdirectory(LocalMem)
 add_subdirectory(DFG2LLVM_WrapperAPI)
-add_subdirectory(ReplaceIntrinsics)
 add_subdirectory(DFG2LLVM_CUDNN)
-add_subdirectory(ExtractHPVMLeafNodes)
 add_subdirectory(FuseHPVMTensorNodes)
-add_subdirectory(InlineTensorCalls)
 add_subdirectory(InPlaceDFG)
diff --git a/hpvm/lib/Transforms/DFG2LLVM_CPU/DFG2LLVM_CPU.cpp b/hpvm/lib/Transforms/DFG2LLVM_CPU/DFG2LLVM_CPU.cpp
index a1cdfc113d3384f54836b0da715a9f42d1058486..104b667fa76abac9eeb33cf82e6d4fdcd7734cb8 100644
--- a/hpvm/lib/Transforms/DFG2LLVM_CPU/DFG2LLVM_CPU.cpp
+++ b/hpvm/lib/Transforms/DFG2LLVM_CPU/DFG2LLVM_CPU.cpp
@@ -1465,15 +1465,35 @@ void CGT_CPU::codeGen(DFLeafNode *N) {
         Ftmp = addIdxDimArgs(Ftmp);
       }
 
-      N->setTag(hpvm::None);
-      N->removeGenFuncForTarget(hpvm::PROMISE_TARGET);
-      N->addGenFunc(Ftmp, hpvm::CPU_TARGET, true);
-      N->setTag(hpvm::CPU_TARGET);
-      break;
-    }
-    default: {
-      break;
-    }
+        N->removeGenFuncForTarget(hpvm::CUDNN_TARGET);
+        N->setTag(hpvm::None);
+        N->addGenFunc(Ftmp, hpvm::CPU_TARGET, true);
+        N->setTag(hpvm::CPU_TARGET);
+        break; 
+     }
+     case hpvm::TENSOR_TARGET: 
+     {
+       errs() << "Promise hint found. Store PROMISE function as CPU funtion.\n";
+       // Make sure there is a generated x86 function for promise
+       assert(N->getGenFuncForTarget(hpvm::TENSOR_TARGET) && "");
+       assert(N->hasCPUGenFuncForTarget(hpvm::TENSOR_TARGET) && "");
+       // Store the PROMISE x86 function as the CPU generated function
+       Function *Ftmp = N->getGenFuncForTarget(N->getTag());
+       // after adding the required number of arguments
+       if (!N->getParent()->isChildGraphStreaming()) {
+         Ftmp = addIdxDimArgs(Ftmp);
+       }
+
+       N->setTag(hpvm::None);
+       N->removeGenFuncForTarget(hpvm::TENSOR_TARGET);
+       N->addGenFunc(Ftmp, hpvm::CPU_TARGET, true);
+       N->setTag(hpvm::CPU_TARGET);
+       break;
+     }
+     default:
+     {
+       break;
+     }
     }
 
     return;
diff --git a/hpvm/lib/Transforms/DFG2LLVM_WrapperAPI/DFG2LLVM_WrapperAPI.cpp b/hpvm/lib/Transforms/DFG2LLVM_WrapperAPI/DFG2LLVM_WrapperAPI.cpp
index 4adde2f2b7f7c0be11d65dd1c2f5086b397f7f65..b400c12021d2df712ea0bbd04f03dbe8724abc75 100644
--- a/hpvm/lib/Transforms/DFG2LLVM_WrapperAPI/DFG2LLVM_WrapperAPI.cpp
+++ b/hpvm/lib/Transforms/DFG2LLVM_WrapperAPI/DFG2LLVM_WrapperAPI.cpp
@@ -27,6 +27,8 @@
 
 #include "SupportHPVM/DFG2LLVM.h"
 #include "InPlaceDFG/InPlaceDFGAnalysis.h"
+#include "Config.h"
+
 #include <sstream>
 #include <fstream>
 
@@ -1325,7 +1327,7 @@ void CGT_WrapperAPI::initRuntimeAPI() {
                          GlobalValue::ExternalLinkage, ConstArray2, "");
   Constant *ConfsGEPConst = ConstantExpr::getGetElementPtr(
       GV2->getType()->getPointerElementType(), GV2, GEPIndices);
-  ArrayRef<Value *> RTCInitArgs = {ConfsGEPConst, QRangesGEPConst};
+  Value *RTCInitArgs[] = {ConfsGEPConst, QRangesGEPConst};
   CallInst::Create(llvm_hpvm_initializeRuntimeController, RTCInitArgs, "",
                    InitCall);
 
@@ -1367,7 +1369,7 @@ void CGT_WrapperAPI::codeGen(DFLeafNode *N) {
   // Look up if we have visited this function before. If we have, then just
   // get the cloned function pointer from DFNode. Otherwise, create the cloned
   // function and add it to the DFNode GenFunc.
-  Function *F_wrapper_api = N->getGenFuncForTarget(hpvm::PROMISE_TARGET);
+  Function *F_wrapper_api = N->getGenFuncForTarget(hpvm::TENSOR_TARGET);
 
   assert((F_wrapper_api == NULL) &&
          "Error: Visiting a node for which code already generated");
@@ -1381,7 +1383,7 @@ void CGT_WrapperAPI::codeGen(DFLeafNode *N) {
   F_wrapper_api->removeFromParent();
   M.getFunctionList().push_back(F_wrapper_api);
 
-  N->addGenFunc(F_wrapper_api, hpvm::PROMISE_TARGET, true);
+  N->addGenFunc(F_wrapper_api, hpvm::TENSOR_TARGET, true);
 
   /* Removing HPVM in/out/inout function attributes */
   for (Function::arg_iterator ai = F_wrapper_api->arg_begin(),
diff --git a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/CMakeLists.txt b/hpvm/lib/Transforms/ExtractHPVMLeafNodes/CMakeLists.txt
deleted file mode 100644
index bb943f9100e628c87c865dfbdce80fc094ebb23e..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/CMakeLists.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-if(WIN32 OR CYGWIN)
-  set(LLVM_LINK_COMPONENTS Core Support)
-endif()
-
-add_llvm_library( ExtractHPVMLeafNodes
-  MODULE
-  ExtractHPVMLeafNodes.cpp
-
-  DEPENDS
-  intrinsics_gen
-  PLUGIN_TOOL
-  opt
-  )
-
diff --git a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.cpp b/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.cpp
deleted file mode 100644
index 031503adeddd6c070ca06f3012fa0c2e5362f92c..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.cpp
+++ /dev/null
@@ -1,248 +0,0 @@
-//===------------------- ExtractHPVMLeafNodeGenFunctions.cpp -----------------===//
-//
-//                     The LLVM Compiler Infrastructure
-//
-// This file is distributed under the University of Illinois Open Source
-//
-// License. See LICENSE.TXT for details.
-//
-//===----------------------------------------------------------------------===//
-
-#define DEBUG_TYPE "ExtractHPVMLeafNodes"
-
-#include "llvm/Support/SourceMgr.h"
-#include "llvm/Pass.h"
-
-#include "SupportHPVM/DFGTreeTraversal.h"
-#include "ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.h"
-
-#include "llvm/Transforms/Utils/BasicBlockUtils.h"
-#include "llvm/IR/IRPrintingPasses.h"
-#include "llvm/IR/LegacyPassManager.h"
-#include "llvm/Support/ToolOutputFile.h"
-#include "llvm/Support/FileSystem.h"
-
-using namespace llvm;
-using namespace builddfg;
-using namespace extracthpvmleaf;
-using namespace dfg2llvm;
-
-namespace {
-
-class PrintLeafNodes : public DFGTreeTraversal {
-  public:
-  virtual void process(DFInternalNode* N) override;
-  virtual void process(DFLeafNode* N) override;
-
-  // Constructor
-  PrintLeafNodes(Module &_M, BuildDFG &_DFG) : DFGTreeTraversal(_M, _DFG) {}
-
-};
-
-}
-
-void PrintLeafNodes::process(DFInternalNode* N) {
-  DEBUG(errs() << "Analysing Node: " << N->getFuncPointer()->getName() << "\n");
-  return; // nothing to do
-}
-
-void PrintLeafNodes::process(DFLeafNode* N) {
-  DEBUG(errs() << "Analysing Node: " << N->getFuncPointer()->getName() << "\n");
-  if((N->isDummyNode())) {
-    DEBUG(errs() << "Skipping Dummy Node: " << N->getFuncPointer()->getName() << "\n");
-    return;
-  }
-
-  // Find function generated for node
-  Function *F = N->getGenFuncForTarget(hpvm::CPU_TARGET);
-  assert(F != NULL
-         && "This pass is invoked after code generation for x86 is completed.\nFound leaf node for which code generation has not happened!\n");
-  assert(N->hasCPUGenFuncForTarget(hpvm::CPU_TARGET) &&
-         "The generated function from x86 pass is not an x86 function\n");
-
-  std::string module_name = std::string("./build/") + std::string(F->getName().str().c_str()) + std::string("_module.ll");
-  Twine tw(module_name);
-  // Create a new module for the node function
-  //Twine tw = Twine(F->getName()).concat(Twine("_module.ll"));
-  Module *m = new Module(tw.str(), F->getParent()->getContext());
-  // Create a new function for F. It will be written to a new module.
-  ValueToValueMapTy VMap;
-  Function *ClonedF = CloneFunction(F, VMap);
-  // Remove it from current module
-  ClonedF->removeFromParent();
-  // Insert it to the newly created module for it
-  m->getFunctionList().push_back(ClonedF);
-
-  std::vector<Instruction*> ItoRemove;
-
-  for (inst_iterator i = inst_begin(ClonedF), e = inst_end(ClonedF); i != e; ++i) {
-    Instruction *I = &(*i);
-    errs() << *I << "\n";
-
-    if (CallInst *CI = dyn_cast<CallInst>(I)) {
-      errs() << "Found call instruction\n";
-
-      Function *CalledF = CI->getCalledFunction();
-      StringRef CallName = CalledF->getName();
-      errs() << "CallName: " << CallName << "\n";
-
-//      if (CallName.startswith("llvm_hpvm")) { //TODO
-      if ((CallName.startswith("llvm_hpvm")) || (CallName.startswith("tensor"))) { //TODO
-//        errs() << "This is an HPVM runtime call. Include its declaration.\n";
-        errs() << "This is an HPVM runtime call or tensor. Include its declaration.\n";
-
-        FunctionType *CalledFType = CalledF->getFunctionType();
-
-        std::vector<Value*> Fargs;
-        for (unsigned argno = 0; argno < CI->getNumArgOperands(); argno++) {
-          Fargs.push_back(CI->getArgOperand(argno));
-        }
-        Function *FDecl = dyn_cast<Function>((m->getOrInsertFunction(CallName, CalledFType)).getCallee());
-        CallInst *NewCI = CallInst::Create(CalledFType, FDecl, Fargs, CallName, CI);
-        errs() << "NewCI: " << *NewCI << "\n";
-        CI->replaceAllUsesWith(NewCI);
-        ItoRemove.push_back(CI);
-      }
-    }
-  }
-
-  for (unsigned i = 0; i < ItoRemove.size() ; i++) {
-    ItoRemove[i]->eraseFromParent();
-  }
-
-  ItoRemove.clear();
-
-  // Print new module
-  legacy::PassManager Passes;
-
-  errs() << "Writing to File --- " << tw.str() << "\n";
-  std::error_code EC;
-  ToolOutputFile Out(tw.str(), EC, sys::fs::F_None);
-  if (EC) {
-    errs() << EC.message() << '\n';
-  }
-
-  Passes.add(createPrintModulePass(Out.os()));
-  Passes.run(*m);
-  // Declare success.
-  Out.keep();
-
-  // Any call that is to F, needs to call the new external function
-  // Edit initial module to do so
-  // This is the name with which the function is called now
-  StringRef FName = ClonedF->getName();
-  FunctionType *FType = F->getFunctionType();
-
-  // This is a node function, so it is only called through the dataflow graph
-  assert(F->hasOneUse() && "F is an HPVM node function\n");
-
-/*
-  errs() << "F uses: " << F->getNumUses()  << "\n" ;
-  for(Value::user_iterator ui = F->user_begin(),
-      ue = F->user_end(); ui!=ue; ++ui) {
-    errs() << "use : "<< **ui << "\n";
-  }
-*/
-
-  // Get the parent node's generated x86 function
-  DFInternalNode *ParentNode = N->getParent();
-  Function *PGenF = ParentNode->getGenFuncForTarget(hpvm::CPU_TARGET);
-  assert(PGenF != NULL
-         && "This pass is invoked after code generation for x86 is completed.\nFound node for which code generation has not happened!\n");
-  assert(ParentNode->hasCPUGenFuncForTarget(hpvm::CPU_TARGET) &&
-         "The generated function from x86 pass is not an x86 function\n");
-
-  for (inst_iterator i = inst_begin(PGenF), e = inst_end(PGenF); i != e; ++i) {
-    Instruction *I = &(*i);
-    errs() << *I << "\n";
-
-    if (CallInst *CI = dyn_cast<CallInst>(I)) {
-      errs() << "Found call instruction\n";
-
-      StringRef CallName = CI->getCalledFunction()->getName();
-      errs() << "CallName: " << CallName << "\n";
-      errs() << "F->getName(): " << F->getName() << "\n";
-
-      if (CallName == F->getName()) {
-        // Found the call to the leaf node function we moved to the other module.
-        // Replace the call
-        std::vector<Value*> Fargs;
-        for (unsigned argno = 0; argno < CI->getNumArgOperands(); argno++) {
-          Fargs.push_back(CI->getArgOperand(argno));
-        }
-        Function *FDecl = dyn_cast<Function>(M.getOrInsertFunction(FName, FType).getCallee());
-        CallInst *NewCI = CallInst::Create(FType, FDecl, Fargs, FName, CI);
-        errs() << "NewCI: " << *NewCI << "\n";
-        CI->replaceAllUsesWith(NewCI);
-        ItoRemove.push_back(CI);
-      }
-    }
-  }
-  
-  for (unsigned i = 0; i < ItoRemove.size() ; i++) {
-    ItoRemove[i]->eraseFromParent();
-  }
-
-  // Clean up
-  ClonedF->eraseFromParent();
-  delete m;
-
-  F->replaceAllUsesWith(UndefValue::get(F->getType()));
-  F->eraseFromParent();
-
-  return;
-}
-
-void ExtractHPVMLeafNodeFunctions::run(Module &M, BuildDFG &DFG) {
-
-  errs() << "\nEXTRACT HPVM LEAF NODE FUNCTIONS PASS\n";
-
-  std::vector<DFInternalNode*> Roots = DFG.getRoots();
-
-  // Visitor for Graph Traversal
-  PrintLeafNodes *LeafVisitor = new PrintLeafNodes(M, DFG);
-
-  // Iterate over all the DFGs
-  // Analyse the edges for parameters that are valid to be used in place
-  for (auto rootNode: Roots) {
-    LeafVisitor->visit(rootNode);
-  }
-
-  delete LeafVisitor;
-  return;
-}
-
-namespace {
-struct ExtractHPVMLeafNodeGenFunctionsWrapper : public ModulePass {
-  static char ID;
-  ExtractHPVMLeafNodeGenFunctionsWrapper() : ModulePass(ID) {}
-
-  bool runOnModule(Module &) override;
-
-  void getAnalysisUsage(AnalysisUsage &AU) const override;
-};
-} // end anonymous namespace
-
-void ExtractHPVMLeafNodeGenFunctionsWrapper::getAnalysisUsage(AnalysisUsage &AU) const {
-  AU.addRequired<BuildDFG>();
-  AU.addPreserved<BuildDFG>();
-}
-
-bool ExtractHPVMLeafNodeGenFunctionsWrapper::runOnModule(Module &M) {
-  // Get the BuildDFG Analysis Results:
-  // - Dataflow graph
-  BuildDFG &DFG = getAnalysis<BuildDFG>();
-
-  ExtractHPVMLeafNodeFunctions ELNF;
-  ELNF.run(M, DFG);
-
-  return false;
-}
-
-char ExtractHPVMLeafNodeGenFunctionsWrapper::ID = 0;
-static RegisterPass<ExtractHPVMLeafNodeGenFunctionsWrapper> X(
-         "hpvm-extract-leaf-gen",
-         "Pass to extract leaf nodes to modules in HPVM",
-         false /* does not modify the CFG */,
-true /* transformation, not just analysis */);
-
diff --git a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.exports b/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.exports
deleted file mode 100644
index 139597f9cb07c5d48bed18984ec4747f4b4f3438..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/ExtractHPVMLeafNodes.exports
+++ /dev/null
@@ -1,2 +0,0 @@
-
-
diff --git a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/LLVMBuild.txt b/hpvm/lib/Transforms/ExtractHPVMLeafNodes/LLVMBuild.txt
deleted file mode 100644
index 73ac540f06e86e9e7f0201b993d2c1e11270158e..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ExtractHPVMLeafNodes/LLVMBuild.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-;===- ./lib/Transforms/DFG2LLVM_WrapperAPI/LLVMBuild.txt -------*- Conf -*--===;
-;
-;                     The LLVM Compiler Infrastructure
-;
-; This file is distributed under the University of Illinois Open Source
-; License. See LICENSE.TXT for details.
-;
-;===------------------------------------------------------------------------===;
-;
-; This is an LLVMBuild description file for the components in this subdirectory.
-;
-; For more information on the LLVMBuild system, please see:
-;
-;   http://llvm.org/docs/LLVMBuild.html
-;
-;===------------------------------------------------------------------------===;
-
-[component_0]
-type = Library
-name = ExtractHPVMLeafNodes
-parent = Transforms
diff --git a/hpvm/lib/Transforms/FuseHPVMTensorNodes/FuseHPVMTensorNodes.cpp b/hpvm/lib/Transforms/FuseHPVMTensorNodes/FuseHPVMTensorNodes.cpp
index d27c6d9dce977c68d24f30e8b6db159153b57e7b..131a291a5b5a5f153985239effb97f5cf7f8e049 100644
--- a/hpvm/lib/Transforms/FuseHPVMTensorNodes/FuseHPVMTensorNodes.cpp
+++ b/hpvm/lib/Transforms/FuseHPVMTensorNodes/FuseHPVMTensorNodes.cpp
@@ -812,7 +812,7 @@ void FindFusionTargetsTraversal::codeGen(DFLeafNode *N) {
   }
   errs() << "THIS IS NOT A DUMMY NODE\n";
   errs() << "INTRINSIC: " << *isValidHPVMTensorNode(N) << "\n";
-  if (!preferredTargetIncludes(N, hpvm::PROMISE_TARGET)) {
+  if(!preferredTargetIncludes(N, hpvm::TENSOR_TARGET)) {
     // Only fuse if we plan to target PROMISE/Layers API
     // The CUDNN backend would be able to generate calls for the fused node,
     // but not the other way around
diff --git a/hpvm/lib/Transforms/InlineTensorCalls/CMakeLists.txt b/hpvm/lib/Transforms/InlineTensorCalls/CMakeLists.txt
deleted file mode 100644
index 29dd2c8431362b28a1d5683eadb2c9eb867696ff..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/InlineTensorCalls/CMakeLists.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-if(WIN32 OR CYGWIN)
-  set(LLVM_LINK_COMPONENTS Core Support)
-endif()
-
-add_llvm_library( InlineTensorCalls
-  MODULE
-  InlineTensorCalls.cpp
-
-  DEPENDS
-  intrinsics_gen
-  PLUGIN_TOOL
-  opt
-  )
-
diff --git a/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.cpp b/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.cpp
deleted file mode 100644
index d31434341cf65939768d0acb7a0051d453909971..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.cpp
+++ /dev/null
@@ -1,77 +0,0 @@
-//=== InlineApproxHPVMCalls.cpp ===//
-//
-//                     The LLVM Compiler Infrastructure
-//
-// This file is distributed under the University of Illinois Open Source
-// License. See LICENSE.TXT for details.
-//
-//===----------------------------------------------------------------------===//
-#define ENABLE_ASSERTS
-
-#define DEBUG_TYPE "INLINE_APPROXHPVM_CALLS"
-#include "llvm/IR/Function.h"
-#include "llvm/IR/Module.h"
-#include "llvm/Pass.h"
-
-#include "llvm/IR/InstIterator.h"
-
-#include "llvm/Support/raw_ostream.h"
-#include "llvm/Analysis/InlineCost.h"
-
-#include "llvm/Transforms/Utils/BasicBlockUtils.h"
-#include "llvm/Transforms/Utils/Cloning.h"
-#include "llvm/IRReader/IRReader.h"
-#include "llvm/Linker/Linker.h"
-#include "llvm/Support/SourceMgr.h"
-#include "llvm/IR/CallSite.h"
-#include "llvm/ADT/SetVector.h"
-#include <sstream>
-
-using namespace llvm;
-
-
-namespace {
-
-  struct InlineApproxHPVMCalls : public ModulePass {
-    static char ID; // Pass identification, replacement for typeid
-    InlineApproxHPVMCalls() : ModulePass(ID) {}
-
-    bool runOnModule(Module &M) override {
-
-      InlineFunctionInfo IFI;
-      SmallSetVector<CallSite, 16> Calls;
-      bool Changed = false;
-      SmallVector<Function *, 16> InlinedFunctions;
-      for (Function &F : M){
-	if (!F.isDeclaration() && F.getName().startswith("tensor") ) {
-	  //errs()<<"Function = "<<*&F<<"\n";
-	  Calls.clear();
-
-	  for (User *U : F.users())
-	    if (auto CS = CallSite(U))
-	      if (CS.getCalledFunction() == &F)
-		Calls.insert(CS);
-
-	  for (CallSite CS : Calls)
-	    // FIXME: We really shouldn't be able to fail to inline at this point!
-	    // We should do something to log or check the inline failures here.
-	    Changed |= InlineFunction(CS, IFI);
-
-	}
-      }
-
-      return true;
-    }
-
-  };
-
-
-} // End of namespace
-
-char InlineApproxHPVMCalls::ID = 0;
-static RegisterPass<InlineApproxHPVMCalls> X("inline-tensor-calls",
-					     "Inline ApproxHPVM tensor library function calls (CPU version)",
-					     true /* modifies the CFG */,
-					     true /* transformation,   *
-						   * not just analysis */);
-
diff --git a/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.exports b/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.exports
deleted file mode 100644
index 139597f9cb07c5d48bed18984ec4747f4b4f3438..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/InlineTensorCalls/InlineTensorCalls.exports
+++ /dev/null
@@ -1,2 +0,0 @@
-
-
diff --git a/hpvm/lib/Transforms/InlineTensorCalls/LLVMBuild.txt b/hpvm/lib/Transforms/InlineTensorCalls/LLVMBuild.txt
deleted file mode 100644
index c160516a6477d367893495e39f5fd4d00366f6f0..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/InlineTensorCalls/LLVMBuild.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-;===- ./lib/Transforms/DFG2LLVM_WrapperAPI/LLVMBuild.txt -------*- Conf -*--===;
-;
-;                     The LLVM Compiler Infrastructure
-;
-; This file is distributed under the University of Illinois Open Source
-; License. See LICENSE.TXT for details.
-;
-;===------------------------------------------------------------------------===;
-;
-; This is an LLVMBuild description file for the components in this subdirectory.
-;
-; For more information on the LLVMBuild system, please see:
-;
-;   http://llvm.org/docs/LLVMBuild.html
-;
-;===------------------------------------------------------------------------===;
-
-[component_0]
-type = Library
-name = InlineTensorCalls
-parent = Transforms
diff --git a/hpvm/lib/Transforms/ReplaceIntrinsics/CMakeLists.txt b/hpvm/lib/Transforms/ReplaceIntrinsics/CMakeLists.txt
deleted file mode 100644
index 460aabcc27b51a2d94dabee3e9c4c60d14803ea9..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ReplaceIntrinsics/CMakeLists.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-if(WIN32 OR CYGWIN)
-  set(LLVM_LINK_COMPONENTS Core Support)
-endif()
-
-add_llvm_library( ReplaceIntrinsics
-  MODULE
-  ReplaceIntrinsics.cpp
-
-  DEPENDS
-  intrinsics_gen
-  PLUGIN_TOOL
-  opt
-  )
-
diff --git a/hpvm/lib/Transforms/ReplaceIntrinsics/LLVMBuild.txt b/hpvm/lib/Transforms/ReplaceIntrinsics/LLVMBuild.txt
deleted file mode 100644
index 95739b3d4d1c3a68cc5014dc85fb26d3b1fc6ac5..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ReplaceIntrinsics/LLVMBuild.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-;===- ./lib/Transforms/DFG2LLVM_WrapperAPI/LLVMBuild.txt -------*- Conf -*--===;
-;
-;                     The LLVM Compiler Infrastructure
-;
-; This file is distributed under the University of Illinois Open Source
-; License. See LICENSE.TXT for details.
-;
-;===------------------------------------------------------------------------===;
-;
-; This is an LLVMBuild description file for the components in this subdirectory.
-;
-; For more information on the LLVMBuild system, please see:
-;
-;   http://llvm.org/docs/LLVMBuild.html
-;
-;===------------------------------------------------------------------------===;
-
-[component_0]
-type = Library
-name = ReplaceIntrinsics
-parent = Transforms
diff --git a/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.cpp b/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.cpp
deleted file mode 100644
index 45ad0ece23568a41fbf532b92918a582ebbae505..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.cpp
+++ /dev/null
@@ -1,495 +0,0 @@
-//=== ReplaceApproxHPVMIntrinsicsWithFCalls.cpp ===//
-//
-//                     The LLVM Compiler Infrastructure
-//
-// This file is distributed under the University of Illinois Open Source
-// License. See LICENSE.TXT for details.
-//
-//===----------------------------------------------------------------------===//
-#define ENABLE_ASSERTS
-
-#define DEBUG_TYPE "REPLACE_APPROXHPVM_INTRINSICS_WITH_FCALLS"
-
-#include "llvm/IR/DataLayout.h"
-#include "llvm/IR/IRBuilder.h"
-#include "llvm/IR/Module.h"
-#include "llvm/Pass.h"
-#include "llvm/IR/InstIterator.h"
-#include "llvm/Transforms/Utils/ValueMapper.h"
-#include "llvm/Transforms/Utils/BasicBlockUtils.h"
-#include "llvm/Transforms/Utils/Cloning.h"
-#include "llvm/IRReader/IRReader.h"
-#include "llvm/Linker/Linker.h"
-#include "llvm/Support/SourceMgr.h"
-#include "llvm/Support/FileSystem.h"
-#include "llvm/IR/Attributes.h"
-#include "llvm-c/Core.h"
-
-#include "SupportHPVM/DFG2LLVM.h"
-#include "InPlaceDFG/InPlaceDFGAnalysis.h"
-
-#include <sstream>
-
-using namespace llvm;
-using namespace builddfg;
-using namespace dfg2llvm;
-
-// TODO: We still need in place analysis, if calls have the same interface
-using namespace inplacedfg;
-
-namespace {
-// Helper class declarations
-
-// Replace ApproxHPVM intrinsics with LLVM function calls.
-// aiming to go through the CPU backend code generation.
-
-struct DFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls : public DFG2LLVM {
-  static char ID; // Pass identification, replacement for typeid
-  DFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls() : DFG2LLVM(ID) {}
-
-private:
-public:
-  void getAnalysisUsage(AnalysisUsage &AU) const {
-    AU.addRequired<BuildDFG>();
-    AU.addRequired<InPlaceDFGAnalysisWrapper>();
-    AU.addPreserved<BuildDFG>();
-    AU.addPreserved<InPlaceDFGAnalysisWrapper>();
-  }
-
-  bool runOnModule(Module &M);
-};
-
-// Visitor for Code generation traversal (tree traversal for now)
-class CGT_ReplaceApproxHPVMIntrinsicsWithFCalls : public CodeGenTraversal {
-
-private:
-  // Member variables
-  InPlaceDFGAnalysis::InPlaceDFGParameter *IPP;
-
-  // VISC Runtime API and Tensor runtime API
-
-  /* TODO: I believe that TensorRt is not needed, since we will have llvm
-   implementations linked in, so init and cleanup calls can be removed and
-   relevant code also, but I leave in in for now until verified. */
-  FunctionCallee llvm_hpvm_initTensorRt;
-  FunctionCallee llvm_hpvm_cleanupTensorRt;
-  //  Constant* hpvm_request_tensor; DONE: request tensor will not be used
-
-  // Functions
-  bool isValidOperandForInPlaceOperation(Value *Op, Function *Fgen, DFNode *N);
-
-  // Virtual Functions
-  void init();
-  void initRuntimeAPI();
-  void codeGen(DFInternalNode *N);
-  void codeGen(DFLeafNode *N);
-
-public:
-  // Constructor
-  CGT_ReplaceApproxHPVMIntrinsicsWithFCalls(
-      Module &_M, BuildDFG &_DFG, InPlaceDFGAnalysis::InPlaceDFGParameter &_IPP)
-      : CodeGenTraversal(_M, _DFG), IPP(&_IPP) {
-    initRuntimeAPI();
-  }
-};
-
-bool CGT_ReplaceApproxHPVMIntrinsicsWithFCalls::
-    isValidOperandForInPlaceOperation(Value *Op, Function *Fgen, DFNode *N) {
-  // We only expect the if branch to be taken
-  if (Argument *Arg = dyn_cast<Argument>(Op)) {
-    DEBUG(errs() << *Arg << "\t: argument, candidate for in place\n");
-    assert((Arg->getParent() == Fgen) &&
-           "Extra Parameter in body of Function\n");
-    // Candidae parameter is a function argument
-    // In this case, consult the result of in place analysis
-    // Find position in arg list
-    unsigned pos = Arg->getArgNo();
-    // If this parameter cannot be used for in place operation
-    // code gen cannot continue
-    if (IPP->at(N)[pos]) {
-      DEBUG(errs() << *Arg << "\t: argument, suitable for in place\n");
-      return true;
-    } else {
-      DEBUG(errs() << *Arg << "\t: argument, not suitable for in place\n");
-      return false;
-    }
-  } else {
-    // If it is not an argument, then it needs to be the result of
-    // another intrinsic. These are new objects that are allocated,
-    // and consumed by next intrinsic. Alternatively, the intrinsic
-    // could have been replaced by a call to an LLVM function.
-    // We do not expect a merge pass to have run before the replacement pass,
-    // therefore we do not expect to go in the else branch.
-    DEBUG(errs() << *Op << "\t: Test for result of intrinsic operation\n");
-    if (dyn_cast<IntrinsicInst>(Op)) {
-      DEBUG(errs() << *Arg << "\t: local, suitable for in place\n");
-      return true;
-    } else if (CallInst *CI = dyn_cast<CallInst>(Op)) {
-      if ((CI->getCalledFunction()->getName()).startswith("tensor"))
-        return true;
-      else
-        return false;
-    } else {
-      DEBUG(errs() << *Arg << "\t: local, not suitable for in place\n");
-      return false;
-    }
-  }
-}
-
-void CGT_ReplaceApproxHPVMIntrinsicsWithFCalls::init() {}
-
-// Initialize the VISC runtime API. This makes it easier to insert these calls
-void CGT_ReplaceApproxHPVMIntrinsicsWithFCalls::initRuntimeAPI() {
-
-  // Load Runtime API Module
-  SMDiagnostic Err;
-  runtimeModule = parseIRFile(TENSOR_RT_LL, Err, M.getContext());
-  if (runtimeModule == nullptr)
-    DEBUG(errs() << Err.getMessage());
-  else
-    DEBUG(errs() << "Successfully loaded hpvm-tensor-rt API module\n");
-
-  // Get or insert Global declarations for
-  // - initialization
-  // - cleanup
-  // - request a tensor
-  DECLARE(llvm_hpvm_initTensorRt);
-  DECLARE(llvm_hpvm_cleanupTensorRt);
-  //  DECLARE(hpvm_request_tensor);
-
-  // Find hpvm.init and visc.cleanup calls, and add placeholder methods
-  // for initialization and cleanup of the hpvm tensor runtime
-
-  Function *VI = M.getFunction("llvm.hpvm.init");
-  assert(VI->getNumUses() == 1 && "__hpvm__init should only be used once\n");
-  InitCall = cast<Instruction>(*VI->user_begin());
-  CallInst::Create(
-      llvm_hpvm_initTensorRt,
-      ArrayRef<Value *>(ConstantInt::get(Type::getInt32Ty(M.getContext()), 0)),
-      "", InitCall);
-
-  Function *VC = M.getFunction("llvm.hpvm.cleanup");
-  assert(VC->getNumUses() == 1 && "__hpvm__clear should only be used once\n");
-  CleanupCall = cast<Instruction>(*VC->user_begin());
-  CallInst::Create(llvm_hpvm_cleanupTensorRt, ArrayRef<Value *>(), "",
-                   CleanupCall);
-}
-
-void CGT_ReplaceApproxHPVMIntrinsicsWithFCalls::codeGen(DFInternalNode *N) {
-  errs() << "Inside node: " << N->getFuncPointer()->getName() << "\n";
-  errs() << "Skipping internal node\n";
-}
-
-void CGT_ReplaceApproxHPVMIntrinsicsWithFCalls::codeGen(DFLeafNode *N) {
-
-  // Skip if it is a dummy node
-  if (N->isDummyNode()) {
-    DEBUG(errs() << "Skipping dummy node\n");
-    return;
-  }
-
-  // Abort if it is an allocation node
-  if (N->isAllocationNode()) {
-    assert(false && "Allocation Node not expected in ApproxHPVM");
-    return;
-  }
-
-  // Search for intrinsic only if it has the right hint
-  if (!checkPreferredTarget(N, hpvm::CPU_TARGET)) {
-    errs() << "Skipping node: " << N->getFuncPointer()->getName() << "\n";
-    return;
-  }
-
-  // Get the function associated with the dataflow node
-  Function *F = N->getFuncPointer();
-  errs() << "function name = " << F->getName() << "\n";
-
-  std::vector<IntrinsicInst *> IItoRemove;
-
-  for (inst_iterator i = inst_begin(F), e = inst_end(F); i != e; ++i) {
-    Instruction *I = &(*i);
-    if (BuildDFG::isHPVMIntrinsic(I)) {
-      IntrinsicInst *II = dyn_cast<IntrinsicInst>(I);
-      assert(
-          (II->getCalledFunction()->getName()).startswith("llvm.hpvm.tensor") &&
-          "Only HPVM tensor intrinsics allowed in ApproxHPVM leaf nodes\n");
-      /********************* Handle VISC Tensor intrinsics ********************/
-      // We replace them with calls to functions with implementations at the
-      // LLVM level
-      switch (II->getIntrinsicID()) {
-
-      case Intrinsic::hpvm_tensor_convolution: { /* llvm.hpvm.tensor.convolution
-                                                  */
-        DEBUG(errs() << F->getName() << "\t: Handling tensor convolution \n");
-
-        // Argument list for the runtime call
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-        Args.push_back(II->getOperand(1));
-        Args.push_back(II->getOperand(2));
-        Args.push_back(II->getOperand(3));
-        Args.push_back(II->getOperand(4));
-        Args.push_back(II->getOperand(5));
-
-        Constant *conv_mode =
-            ConstantInt::get(Type::getInt32Ty(M.getContext()), 1);
-        Constant *conv_precision =
-            ConstantInt::get(Type::getInt32Ty(M.getContext()), 0);
-
-        Args.push_back(conv_mode);
-        Args.push_back(conv_precision);
-
-        // Create function call
-        FunctionCallee tensorConvolutionCPU;
-        DECLARE(tensorConvolutionCPU);
-
-        CallInst *CI = CallInst::Create(tensorConvolutionCPU, Args, "", II);
-        // We can replace the call to hpvm.tensor.mul with the LLVM call
-        II->replaceAllUsesWith(CI);
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      case Intrinsic::hpvm_tensor_mul: { /* llvm.hpvm.tensor.mul */
-        DEBUG(errs() << F->getName() << "\t: Handling tensor mul\n");
-
-        // Argument list for the runtime call
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-        Args.push_back(II->getOperand(1));
-
-        // Create function call
-        FunctionCallee tensorGemmCPU;
-        DECLARE(tensorGemmCPU);
-
-        CallInst *CI = CallInst::Create(tensorGemmCPU, Args, "", II);
-        // We can replace the call to hpvm.tensor.mul with the LLVM call
-        II->replaceAllUsesWith(CI);
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      case Intrinsic::hpvm_tensor_add: { /* llvm.hpvm.tensor.add */
-        DEBUG(errs() << F->getName() << "\t: Handling tensor add\n");
-        // Tensor add(a,b) is in place for argument a.
-        Value *Op = II->getOperand(0);
-
-        // Test the intrinsic operand for in place operation.
-        bool inplace = isValidOperandForInPlaceOperation(Op, F, N);
-        // Code generation cannot continue if this is false, because the target
-        // only provides an in place operation
-
-        // FIXME: remove this comment - must check for in-place
-        // assert(inplace &&
-        //       "Operand not valid for in place operation. Code gen
-        //       aborted.\n");
-
-        // Argument list for the runtime call
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-        Args.push_back(II->getOperand(1));
-
-        // Create function call
-        FunctionCallee tensorAddCPU;
-        DECLARE(tensorAddCPU);
-        CallInst::Create(tensorAddCPU, Args, "", II);
-        // We can replace the call to hpvm.tensor.add with the 1st argument
-        // that, due to in place operation, now contains the result
-        II->replaceAllUsesWith(II->getOperand(0));
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      case Intrinsic::hpvm_tensor_pool_max:
-      case Intrinsic::hpvm_tensor_pool_mean: { /* llvm.hpvm.tensor.relu */
-        DEBUG(errs() << F->getName() << "\t: Handling tensor_pool_max\n");
-        // Tensor relu(a) is in place for argument a.
-        Value *Op = II->getOperand(0);
-
-        // Test the intrinsic operand for in place operation.
-        bool inplace = isValidOperandForInPlaceOperation(Op, F, N);
-        // Code generation cannot continue if this is false, because the target
-        // only provides an in place operation
-        assert(inplace &&
-               "Operand not valid for in place operation. Code gen aborted.\n");
-
-        // Argument list - tensorPooling(input, poolFunction, window_height,
-        // window_width, vertical_pad, horizontal_pad,
-        //                               vertical_stride, horizontal_stride);
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-
-        int pool_type = 0;
-        if (II->getIntrinsicID() == Intrinsic::hpvm_tensor_pool_max) {
-          pool_type = 0;
-        }
-        if (II->getIntrinsicID() == Intrinsic::hpvm_tensor_pool_mean) {
-          pool_type = 1;
-        }
-
-        Constant *constPoolType =
-            ConstantInt::get(Type::getInt32Ty(M.getContext()), pool_type);
-        Args.push_back(constPoolType); // ID for max pool. Min/Avg have
-                                       // different IDs (non-zero)
-        Args.push_back(II->getOperand(1));
-        Args.push_back(II->getOperand(2));
-        Args.push_back(II->getOperand(3));
-        Args.push_back(II->getOperand(4));
-        Args.push_back(II->getOperand(5));
-        Args.push_back(II->getOperand(6));
-
-        // Create function call
-        FunctionCallee tensorPoolingCPU;
-        DECLARE(tensorPoolingCPU);
-        CallInst *CI = CallInst::Create(tensorPoolingCPU, Args, "", II);
-
-        // Replacing intrinsic result uses with the result of the LLVM call
-        II->replaceAllUsesWith(CI);
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      case Intrinsic::hpvm_tensor_relu:
-      case Intrinsic::hpvm_tensor_clipped_relu:
-      case Intrinsic::hpvm_tensor_tanh: { /* llvm.hpvm.tensor.relu */
-        DEBUG(errs() << F->getName()
-                     << "\t: Handling tensor activation functions \n");
-        // Tensor relu(a) is in place for argument a.
-        Value *Op = II->getOperand(0);
-
-        // Test the intrinsic operand for in place operation.
-        bool inplace = isValidOperandForInPlaceOperation(Op, F, N);
-        // Code generation cannot continue if this is false, because the target
-        // only provides an in place operation
-        assert(inplace &&
-               "Operand not valid for in place operation. Code gen aborted.\n");
-
-        // Argument list for the runtime call
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-
-        if (II->getIntrinsicID() == Intrinsic::hpvm_tensor_relu) {
-          // Create function call
-          FunctionCallee tensorReluCPU;
-          DECLARE(tensorReluCPU);
-          CallInst::Create(tensorReluCPU, Args, "", II);
-        } else if (II->getIntrinsicID() ==
-                   Intrinsic::hpvm_tensor_clipped_relu) {
-          // Create function call
-          //-- FunctionCallee tensorClippedRelu;
-          FunctionCallee tensorRelu2CPU;
-          DECLARE(tensorRelu2CPU);
-          CallInst::Create(tensorRelu2CPU, Args, "", II);
-        } else if (II->getIntrinsicID() == Intrinsic::hpvm_tensor_tanh) {
-          // Create function call
-          FunctionCallee tensorTanhCPU;
-          errs() << "tensorTanh Call = \n\n";
-          DECLARE(tensorTanhCPU);
-          // errs()<<"tensorTanh Call = "<<*tensorTanh<<"\l";
-          CallInst::Create(tensorTanhCPU, Args, "", II);
-        }
-
-        // We can replace the call to hpvm.tensor.relu with the 1st argument
-        // that, due to in place operation, now contains the result
-        II->replaceAllUsesWith(II->getOperand(0));
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      case Intrinsic::hpvm_tensor_softmax: { /* llvm.hpvm.tensor.softmax */
-        DEBUG(errs() << F->getName() << "\t: Handling tensor softmax\n");
-        // Tensor relu(a) is in place for argument a.
-        Value *Op = II->getOperand(0);
-
-        // Test the intrinsic operand for in place operation.
-        bool inplace = isValidOperandForInPlaceOperation(Op, F, N);
-        // Code generation cannot continue if this is false, because the target
-        // only provides an in place operation
-        assert(inplace &&
-               "Operand not valid for in place operation. Code gen aborted.\n");
-
-        // Argument list for the runtime call
-        std::vector<Value *> Args;
-        Args.push_back(II->getOperand(0));
-
-        // Create function call
-        FunctionCallee tensorSoftmaxCPU;
-        DECLARE(tensorSoftmaxCPU);
-        CallInst::Create(tensorSoftmaxCPU, Args, "", II);
-        // We can replace the call to hpvm.tensor.softmax with the 1st argument
-        // that, due to in place operation, now contains the result
-        II->replaceAllUsesWith(II->getOperand(0));
-
-        // Mark to remove at the end
-        IItoRemove.push_back(II);
-      } break;
-
-      default:
-        llvm_unreachable("Unknown VISC Intrinsic!");
-        break;
-      }
-    }
-  }
-
-  // We need to do this explicitly: DCE pass may not remove them.
-  // Traverse the vector backwards, otherwise definitions are deleted while
-  // their subsequent uses are still around.
-  for (std::vector<IntrinsicInst *>::reverse_iterator ri = IItoRemove.rbegin(),
-                                                      re = IItoRemove.rend();
-       ri != re; ++ri) {
-    DEBUG(errs() << "Erasing: " << **ri << "\n");
-    errs() << "Erasing: " << **ri << "\n";
-    (*ri)->eraseFromParent();
-  }
-
-  return;
-}
-
-bool DFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls::runOnModule(Module &M) {
-  errs() << "\nDFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls PASS\n";
-
-  // Get the BuildDFG Analysis Results:
-  // - Dataflow graph
-  BuildDFG &DFG = getAnalysis<BuildDFG>();
-
-  // Get the In Place Analysis Results
-  InPlaceDFGAnalysis::InPlaceDFGParameter IPP =
-      (getAnalysis<InPlaceDFGAnalysisWrapper>()).getIPP();
-  // Print results
-  printInPlaceDFGParameter(IPP);
-
-  std::vector<DFInternalNode *> Roots = DFG.getRoots();
-
-  // Visitor for Code Generation Graph Traversal
-  CGT_ReplaceApproxHPVMIntrinsicsWithFCalls *CGTVisitor =
-      new CGT_ReplaceApproxHPVMIntrinsicsWithFCalls(M, DFG, IPP);
-
-  // Iterate over all the DFGs and produce code for each one of them
-  for (auto rootNode : Roots) {
-    // Initiate code generation for root DFNode
-    CGTVisitor->visit(rootNode);
-  }
-
-  // TODO: Edit module epilogue to remove the VISC intrinsic declarations
-  delete CGTVisitor;
-
-  return true;
-}
-
-/******************************************************************************
- *                              Helper functions                              *
- ******************************************************************************/
-
-} // End of namespace
-
-char DFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls::ID = 0;
-static RegisterPass<DFG2LLVM_ReplaceApproxHPVMIntrinsicsWithFCalls> X("replace-intrinsics",
-                                      "Replace ApproxHPVM intrinsics with LLVM calls",
-                                      false /* does not modify the CFG */,
-                                      true /* transformation,   *
-                                            * not just analysis */);
diff --git a/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.exports b/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.exports
deleted file mode 100644
index 139597f9cb07c5d48bed18984ec4747f4b4f3438..0000000000000000000000000000000000000000
--- a/hpvm/lib/Transforms/ReplaceIntrinsics/ReplaceIntrinsics.exports
+++ /dev/null
@@ -1,2 +0,0 @@
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/CMakeLists.txt b/hpvm/projects/hpvm-tensor-rt/CMakeLists.txt
index 2feeaa2fefeb5b1a7dd937816e785a1dd641a5e4..d28868892f6d45e6905594e143a13aa83b1db9d6 100644
--- a/hpvm/projects/hpvm-tensor-rt/CMakeLists.txt
+++ b/hpvm/projects/hpvm-tensor-rt/CMakeLists.txt
@@ -78,7 +78,7 @@ set(
   RUNTIME_SRCS_FILENAME
   approx_simulation.cu
   group_conv.cu
-  approx_techniques2.cu
+  approx_techniques.cu
   common.cpp
   configuration.cpp
   debug.cc
@@ -178,158 +178,72 @@ add_dependencies(tensor_runtime_online tensor_runtime)
 target_link_libraries(tensor_runtime_online ${LINK_LIBS}) 
 target_compile_definitions(tensor_runtime_online PRIVATE -DONLINE_PROFILING=true -DFP16_tuning=false)
 
-# Adding new rule for building a cuDNN runtime library
 
-#-- find_package(OpenMP REQUIRED)
-#-- cuda_add_library(tensor_cpu_runtime tensor_runtime/src/tensor_cpu_runtime.cc)
-#-- target_compile_options(tensor_cpu_runtime PRIVATE ${OpenMP_CXX_FLAGS})
-#-- target_link_libraries(tensor_cpu_runtime PRIVATE ${OpenMP_CXX_FLAGS})
 
-### TODO: Remove unsued CMake rules after careful consideration
+# --------------  Unit Test Source ----------------
 
-# Adding rule for the debugging source
 add_executable(unit_tests   dnn_sources/src/unit_tests.cc)
 target_link_libraries(unit_tests  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
 
-#**************** FP32 Source Builds *********** 
-
-add_executable(lenet_mnist  dnn_sources/src/lenet_mnist.cc)
-target_link_libraries(lenet_mnist  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet_cifar10  dnn_sources/src/alexnet_cifar10.cc)
-target_link_libraries(alexnet_cifar10  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_cifar10  dnn_sources/src/alexnet2_cifar10.cc)
-target_link_libraries(alexnet2_cifar10  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10  dnn_sources/src/vgg16_cifar10.cc)
-target_link_libraries(vgg16_cifar10  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_cifar10  dnn_sources/src/resnet18_cifar10.cc)
-target_link_libraries(resnet18_cifar10  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100  dnn_sources/src/vgg16_cifar100.cc)
-target_link_libraries(vgg16_cifar100  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet  dnn_sources/src/mobilenet.cc)
-target_link_libraries(mobilenet  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_shallow  dnn_sources/src/mobilenet_shallow.cc)
-target_link_libraries(mobilenet_shallow  tensor_runtime_online ${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})
-
-
-
-
-#********* FP16 Source Builds ****** 
-
-add_executable(lenet_half   dnn_sources/src/half/lenet_mnist_half.cc)
-target_link_libraries(lenet_half  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet_half   dnn_sources/src/half/alexnet_cifar10_half.cc)
-target_link_libraries(alexnet_half  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_half  dnn_sources/src/half/alexnet2_cifar10_half.cc)
-target_link_libraries(alexnet2_half  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_half  dnn_sources/src/half/resnet18_cifar10_half.cc)
-target_link_libraries(resnet18_half  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10_half  dnn_sources/src/half/vgg16_cifar10_half.cc)
-target_link_libraries(vgg16_cifar10_half  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_half  dnn_sources/src/half/vgg16_cifar100_half.cc)
-target_link_libraries(vgg16_cifar100_half  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_half  dnn_sources/src/half/mobilenet_half.cc)
-target_link_libraries(mobilenet_half  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_shallow_half  dnn_sources/src/half/mobilenet_shallow_half.cc)
-target_link_libraries(mobilenet_shallow_half   tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-
-#********* Promise API sources - Used with the Autouner
-
-add_executable(lenet_promise  dnn_sources/src/promise/lenet_promise.cc)
-target_link_libraries(lenet_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-add_executable(alexnet_promise  dnn_sources/src/promise/alexnet_promise.cc)
-target_link_libraries(alexnet_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_promise  dnn_sources/src/promise/alexnet2_promise.cc)
-target_link_libraries(alexnet2_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_promise  dnn_sources/src/promise/resnet18_promise.cc)
-target_link_libraries(resnet18_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_promise  dnn_sources/src/promise/vgg16_cifar100_promise.cc)
-target_link_libraries(vgg16_cifar100_promise  tensor_runtime_online   ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10_promise  dnn_sources/src/promise/vgg16_cifar10_promise.cc)
-target_link_libraries(vgg16_cifar10_promise  tensor_runtime_online   ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_promise  dnn_sources/src/promise/mobilenet_promise.cc)
-target_link_libraries(mobilenet_promise  tensor_runtime_online   ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_shallow_promise  dnn_sources/src/promise/mobilenet_shallow_promise.cc)
-target_link_libraries(mobilenet_shallow_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
+#**************** FP32 TensorRT Source Builds *********** 
 
-add_executable(vgg16_imagenet_promise  dnn_sources/src/promise/vgg16_imagenet_promise.cc)
-target_link_libraries(vgg16_imagenet_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(lenet_mnist_fp32  dnn_sources/src/fp32/lenet_mnist.cc)
+target_link_libraries(lenet_mnist_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(resnet50_imagenet_promise  dnn_sources/src/promise/resnet50_imagenet_promise.cc)
-target_link_libraries(resnet50_imagenet_promise  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(alexnet_cifar10_fp32  dnn_sources/src/fp32/alexnet_cifar10.cc)
+target_link_libraries(alexnet_cifar10_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(alexnet2_cifar10_fp32  dnn_sources/src/fp32/alexnet2_cifar10.cc)
+target_link_libraries(alexnet2_cifar10_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(vgg16_cifar10_fp32  dnn_sources/src/fp32/vgg16_cifar10.cc)
+target_link_libraries(vgg16_cifar10_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(resnet18_cifar10_fp32  dnn_sources/src/fp32/resnet18_cifar10.cc)
+target_link_libraries(resnet18_cifar10_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(vgg16_cifar100_fp32  dnn_sources/src/fp32/vgg16_cifar100.cc)
+target_link_libraries(vgg16_cifar100_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(mobilenet_cifar10_fp32  dnn_sources/src/fp32/mobilenet.cc)
+target_link_libraries(mobilenet_cifar10_fp32  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-# OpenTuner Piped Sources
-add_executable(alexnet_piped  dnn_sources/src/promise/alexnet_piped.cc)
-target_link_libraries(alexnet_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(alexnet_imagenet_fp32  dnn_sources/src/fp32/alexnet_imagenet.cc)
+target_link_libraries(alexnet_imagenet_fp32  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(alexnet2_piped  dnn_sources/src/promise/alexnet2_piped.cc)
-target_link_libraries(alexnet2_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(vgg16_imagenet_fp32  dnn_sources/src/fp32/vgg16_imagenet.cc)
+target_link_libraries(vgg16_imagenet_fp32  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(lenet_piped  dnn_sources/src/promise/lenet_piped.cc)
-target_link_libraries(lenet_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(resnet50_imagenet_fp32  dnn_sources/src/fp32/resnet50_imagenet.cc)
+target_link_libraries(resnet50_imagenet_fp32  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(resnet18_piped  dnn_sources/src/promise/resnet18_piped.cc)
-target_link_libraries(resnet18_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(vgg16_cifar10_piped  dnn_sources/src/promise/vgg16_cifar10_piped.cc)
-target_link_libraries(vgg16_cifar10_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(vgg16_cifar100_piped  dnn_sources/src/promise/vgg16_cifar100_piped.cc)
-target_link_libraries(vgg16_cifar100_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(mobilenet_piped  dnn_sources/src/promise/mobilenet_piped.cc)
-target_link_libraries(mobilenet_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+#********* FP16 TensorRT Source Builds ****** 
 
-add_executable(mobilenet_shallow_piped  dnn_sources/src/promise/mobilenet_shallow_piped.cc)
-target_link_libraries(mobilenet_shallow_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(lenet_mnist_fp16   dnn_sources/src/fp16/lenet_mnist_half.cc)
+target_link_libraries(lenet_mnist_fp16  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(vgg16_imagenet_piped  dnn_sources/src/promise/vgg16_imagenet_piped.cc)
-target_link_libraries(vgg16_imagenet_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(alexnet_cifar10_fp16   dnn_sources/src/fp16/alexnet_cifar10_half.cc)
+target_link_libraries(alexnet_cifar10_fp16  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(resnet50_imagenet_piped  dnn_sources/src/promise/resnet50_imagenet_piped.cc)
-target_link_libraries(resnet50_imagenet_piped  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(alexnet2_cifar10_fp16  dnn_sources/src/fp16/alexnet2_cifar10_half.cc)
+target_link_libraries(alexnet2_cifar10_fp16  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(resnet18_cifar10_fp16  dnn_sources/src/fp16/resnet18_cifar10_half.cc)
+target_link_libraries(resnet18_cifar10_fp16  tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(vgg16_cifar10_fp16  dnn_sources/src/fp16/vgg16_cifar10_half.cc)
+target_link_libraries(vgg16_cifar10_fp16  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
+add_executable(vgg16_cifar100_fp16  dnn_sources/src/fp16/vgg16_cifar100_half.cc)
+target_link_libraries(vgg16_cifar100_fp16  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-#### Image Processing Benchmarks
+add_executable(mobilenet_cifar10_fp16  dnn_sources/src/fp16/mobilenet_half.cc)
+target_link_libraries(mobilenet_cifar10_fp16  tensor_runtime_online  ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
-add_executable(alexnet2_canny dnn_sources/src/alexnet2_canny.cc)
-target_link_libraries(alexnet2_canny tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
 
 
 
diff --git a/hpvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt b/hpvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt
deleted file mode 100644
index cff0129c2aa02b9776ed7bba8e92029d2c2560e8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-cmake_minimum_required (VERSION 2.6)
-project (approxhpvm-tensorRt-cpu)
-
-
-# Addresses a bug where code is not compiled as C++11 in non-CUDA code and older g++ versions
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 " )
- 
-
-# Adding new rule for building a cuDNN runtime library
-add_library(tensor_cpu_runtime tensor_runtime/src/tensor_cpu_runtime.cc)
-target_link_libraries(tensor_cpu_runtime)
-
-
-#**** CPU sources
-add_executable(fc2_cpu  dnn_sources/src/fc2_cpu.cc)
-target_link_libraries(fc2_cpu  tensor_cpu_runtime)
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/knobs.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/knobs.txt
deleted file mode 100644
index 1be644441769e8544901010586bc9842d8b14289..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/knobs.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-perf_fp16,120,Baseline
-perf_fp16,151,Col perf 50% offset=0
-perf_fp16,152,Col perf 50% offset=1
-perf_fp16,153,Row perf 50% offset=0
-perf_fp16,154,Row perf 50% offset=1
-perf_fp16,155,Col perf 33% offset=0
-perf_fp16,156,Col perf 33% offset=1
-perf_fp16,157,Col perf 33% offset=2
-perf_fp16,158,Row perf 33% offset=0
-perf_fp16,159,Row perf 33% offset=1
-perf_fp16,160,Row perf 33% offset=2
-perf_fp16,161,Col perf 25% offset=0
-perf_fp16,162,Col perf 25% offset=1
-perf_fp16,163,Col perf 25% offset=2
-perf_fp16,164,Col perf 25% offset=3
-perf_fp16,165,Row perf 25% offset=0
-perf_fp16,166,Row perf 25% offset=1
-perf_fp16,167,Row perf 25% offset=2
-perf_fp16,168,Row perf 25% offset=3
-samp_fp16,261,Samp 50% offset=0
-samp_fp16,262,Samp 50% offset=1
-samp_fp16,263,Samp 33% offset=0
-samp_fp16,264,Samp 33% offset=1
-samp_fp16,265,Samp 33% offset=2
-samp_fp16,266,Samp 25% offset=0
-samp_fp16,267,Samp 25% offset=1
-samp_fp16,268,Samp 25% offset=2
-samp_fp16,269,Samp 25% offset=3
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_120.txt
deleted file mode 100644
index f3e1be03b607bcf404a6cb809f1e231d497b19c6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_120.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_151.txt
deleted file mode 100644
index 891ef4648247e6a7879f0a8c974b3b9b59193105..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_151.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_152.txt
deleted file mode 100644
index 0e1dc661467bd46ffab34faf5f85ee1254068b7c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_152.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_153.txt
deleted file mode 100644
index 211011c1c8c194c7c81ec30abe02b85d03de8f95..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_153.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_154.txt
deleted file mode 100644
index 4e4718b997d2acdad6386541c328fd778daf9f92..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_154.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_155.txt
deleted file mode 100644
index 394d24cf90f4313068db4ecf05cdc388d4178799..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_perf_fp16_155.txt
+++ /dev/null
@@ -1,72 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet2_cifar10/alexnet2_cifar10_fp16_samp_fp16_261.txt
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deleted file mode 100644
index 46cb2c584851b98944162e561e68c378332f13db..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp16/alexnet_canny_fp16_red_samp_fp16_46.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp16/alexnet_canny_fp16_red_samp_fp16_46.txt
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
index 070e97bed17bb07898d33e45d7ea90d12f52461f..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp16/alexnet_canny_samp_236.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp16/alexnet_canny_samp_236.txt
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
index dbc124748794072b323ece1525a773ec28ea633e..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp32/alexnet_canny_samp_232.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_canny_fp32/alexnet_canny_samp_232.txt
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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index ff28fccb9e6a9464df2a3ecf2e0f2f450508f710..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
index 391282ad11099343f49a83189f68acbb8bd1942d..0000000000000000000000000000000000000000
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deleted file mode 100644
index 7b36fde13801276c18018adc16ef4890b4eb4890..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
index 05d5be47e55158369efbdde8f14412a2b4ba1c5d..0000000000000000000000000000000000000000
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index 64684f24133f6f23242ceb1029b6c6dacf73755e..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
index da47c26710f5b8303f9535b705bf97daad8ab2f5..0000000000000000000000000000000000000000
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deleted file mode 100644
index 5ceaa1add158ee879b8884d085de3430cd1efcc2..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
index 87a8b5b753cca9b81ea1f28369712055c2902022..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
index d0e6e9a17ef4b3dfb59b8f9974ae1d8f215aae00..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_imagenet/alexnet_imagenet_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/alexnet_imagenet/alexnet_imagenet_fp16_samp_fp16_267.txt
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_151.txt
deleted file mode 100644
index f61512071680cf0864c0c16a8e7b21a8a6aee39c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_151.txt
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deleted file mode 100644
index a3c51be3cffa7c274431e82fad78197aa6d1836f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_152.txt
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deleted file mode 100644
index 2dd6e9a88a82649c5b2ab418e598432bec261bf6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_153.txt
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deleted file mode 100644
index 389e60070dd2731939dde771502b55c5a437486f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_154.txt
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deleted file mode 100644
index ca03841e4af51b8e30ce93734eda3674e7eca805..0000000000000000000000000000000000000000
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deleted file mode 100644
index 955744472efb7e677ce14f28f589aa25087e4766..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_161.txt
deleted file mode 100644
index 092b6f5d79acc04e4622f26006ed1a8de3d1ccea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_161.txt
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deleted file mode 100644
index 3ca4277de1d0258709f4d44faf64d9e59937a90f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_162.txt
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deleted file mode 100644
index dbfbf37ebc34a0e18fe7b33d296b0e60811e87d5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_163.txt
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index 99b2446d3c9a86427679948b7869c3ce4cd645c7..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
index 55d8d4862bceea092624fef4be804238cafe2d6e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_perf_fp16_168.txt
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deleted file mode 100644
index ceeccbe71a1ee4bc7891d2898389c43ffaaae102..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_samp_fp16_261.txt
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deleted file mode 100644
index ae7893234b4a854afdf2ab6a93804b472d7bde8d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_samp_fp16_262.txt
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deleted file mode 100644
index bd428223fa0b68dc3eb8d0ec94c2abd03e912b98..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_samp_fp16_263.txt
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deleted file mode 100644
index f6809874580e3592b06fd43812ddf02a388428b2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp16_samp_fp16_264.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp32_perf_fp32_120.txt
deleted file mode 100644
index df787165b74a3bb97015b4f0bb9c7afa56c5c6d8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/lenet_keras/lenet_keras_fp32_perf_fp32_120.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_120.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_151.txt
deleted file mode 100644
index dc27bd9b0cefcba92a6e611bbd04e7abdcf043e4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_151.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_152.txt
deleted file mode 100644
index 8964f1786a46b940e964e341614251865b47d4e2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_152.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_153.txt
deleted file mode 100644
index 39c3c4bfc3c45ad536dd32d24c599bbfa1eca85b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_153.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_154.txt
deleted file mode 100644
index af20d986d021bcf29c10ad5a89faede4cf16115b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_154.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_155.txt
deleted file mode 100644
index 3cd198c77ec69951041ad8d1e114a5a5df5072c8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_155.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_156.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_156.txt
deleted file mode 100644
index 8019eb1b69a25d2c87a036fe7cac6864eaecedea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_156.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_157.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_157.txt
deleted file mode 100644
index d8489f200d082fa1d51b5c5d12c1827951a4c4b3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_157.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_158.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_158.txt
deleted file mode 100644
index 90ffe2f12a60802fc29a7089ae60cd65af0bf6dc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_158.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_159.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_159.txt
deleted file mode 100644
index f516e0ae4c0a2d7f01b2ed15147ad8d2d14a6eff..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_159.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_160.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_160.txt
deleted file mode 100644
index c5475d1abb9ac9341738791863861927e48b615e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_160.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_161.txt
deleted file mode 100644
index ec4bf136fd09893c177a6bf1002292e43a45cb36..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_161.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_162.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_162.txt
deleted file mode 100644
index 02527d21f3d48f8563ff990061c3c0e2e95f794a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_162.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_163.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_163.txt
deleted file mode 100644
index bfaefe1f93f0c06c121c2d40d7a95c0bec41c7d4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_163.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_164.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_164.txt
deleted file mode 100644
index 82957f7e574b11d69bfd9f4026c65dc9dade14e1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_164.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_165.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_165.txt
deleted file mode 100644
index cd027ffd1de79aaa5146212bd23f0020c997acc9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_165.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_166.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_166.txt
deleted file mode 100644
index 0a1f2f2a19baf9ac339ff93c32462d20bec705ce..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_166.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_167.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_167.txt
deleted file mode 100644
index 1b522abbf0d1936a04e32f08c37c4a49a59702b9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_167.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_168.txt
deleted file mode 100644
index feaab7025af2fa86071c5a370adbdaa04b0154c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_perf_fp16_168.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_261.txt
deleted file mode 100644
index 5764c923a5d3282cdcb947fdb7362031fdbc155a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_261.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_262.txt
deleted file mode 100644
index 9d6d32c0c29b32303dc7183f7bda8f31d6325deb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_262.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_263.txt
deleted file mode 100644
index 5bd59d30a06f2727acb186f27e93fe96da2ddf1b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_263.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_264.txt
deleted file mode 100644
index 6ae91b073a0abf842fa52c2091f88f9416c66b21..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_264.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_265.txt
deleted file mode 100644
index c74cb8bdba74b5c918d855de74d436dbb2277f5b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_265.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_266.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_266.txt
deleted file mode 100644
index 6c3baca1485041072a6b52c3b76f0cf7a79130b3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_266.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_267.txt
deleted file mode 100644
index e9b0012ca0bcc9f50fc86cc2e7d51e48ae00e410..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_267.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_268.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_268.txt
deleted file mode 100644
index 805f15081a124b40c4b8269fe8ccd2fa1f5eab6a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_268.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_269.txt
deleted file mode 100644
index f64ae15dfc2b9bbe95895df76765388b57c3bde4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp16_samp_fp16_269.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp32_perf_fp32_120.txt
deleted file mode 100644
index a32a995a8279896b28f6a55ddebb630edc916ba4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/mobilenet_cifar10/mobilenet_cifar10_fp32_perf_fp32_120.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_120.txt
deleted file mode 100644
index e5768fe5ea99f84981077939f7b7946785db1f65..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_120.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_151.txt
deleted file mode 100644
index 786d8dbd8e2975e5dcf200279de9ad4f5ee9d9d2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_151.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_152.txt
deleted file mode 100644
index 600dc88cbedd2c88a6cb6cfa2f84bda1e54ebe7e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_152.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_153.txt
deleted file mode 100644
index a8c00d45766dd61abbccb3447c9843273e812b23..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_153.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_154.txt
deleted file mode 100644
index 6480cd1c9857412d9edfc4452aa8c9ade5d16561..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_154.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_155.txt
deleted file mode 100644
index 516dfb5b2e62961dae843a98e0d497c345ee5f04..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_155.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_156.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_156.txt
deleted file mode 100644
index 1f5bb7ca4479bd2929694021aa192e83bae5df94..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_156.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_157.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_157.txt
deleted file mode 100644
index 17b884ece43f34f3a1adc72ca028d28e06277f69..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_157.txt
+++ /dev/null
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deleted file mode 100644
index e98ad1dac3c713b97c23585b7ba1b320e4d4bc94..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_158.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_159.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_159.txt
deleted file mode 100644
index 84b449752cbe908ff0271b75f6a0bfe8f2cae235..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_159.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_160.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_160.txt
deleted file mode 100644
index ba5a39afb52f7a01ac475b342a5d6868d04fb006..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_160.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_161.txt
deleted file mode 100644
index 84970f1fe409bfade732399b96dcca33f74287d5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_161.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_162.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_162.txt
deleted file mode 100644
index 2f1f3b1e5436717e32c0807e70f2d21972b753a4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_162.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_163.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_163.txt
deleted file mode 100644
index cd96db7497bcf1ae4672492cd0c9b31424348439..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_163.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_164.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_164.txt
deleted file mode 100644
index 7f3da6f1d981d3f5ad957abc0318ae1ece976cf9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_164.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_165.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_165.txt
deleted file mode 100644
index f535f820c943898d9f59a276eab1f036f793a172..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_165.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_166.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_166.txt
deleted file mode 100644
index 4568dca5cc198e32be6a44e7ef13e0a7a0b089e7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_166.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_167.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_167.txt
deleted file mode 100644
index 3a5e9b50bc4f7e1d36243820a746abfff29e080d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_167.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_168.txt
deleted file mode 100644
index 80dc433310b114fa54345faac279f4532cf8b4c8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_perf_fp16_168.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_261.txt
deleted file mode 100644
index 6caeadddfdd40b8638bb3ef555563ea32760ed07..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_261.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_262.txt
deleted file mode 100644
index 9194ee12f9f9d142886a9915e8705f649b0f80bd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_262.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_263.txt
deleted file mode 100644
index c5025fd8696135af2d5ddcd46afef3c19fa2cfaa..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_263.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_264.txt
deleted file mode 100644
index 951b8c465bdacd5d15f980305c97861b76160712..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_264.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_265.txt
deleted file mode 100644
index 49017013dc5f934f9f238c0a687f8cadf1f64283..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_265.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_266.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_266.txt
deleted file mode 100644
index 595da5c025a8b2d21a8859f7b81759e0b119fa09..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_266.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_267.txt
deleted file mode 100644
index df31c8040e2bee945a9f413ea6c2fe1bab857755..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_267.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_268.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_268.txt
deleted file mode 100644
index 0fdf2c3e3f76b6851c39755e368bd4ac9013fbda..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_268.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_269.txt
deleted file mode 100644
index b7d2ecb6a5df940372f102775172e8c560ffd7c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp16_samp_fp16_269.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp32_perf_fp32_120.txt
deleted file mode 100644
index 2323ecc3dacbed4c5b302a388e99a12e7836a20b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet18_cifar10/resnet18_cifar10_fp32_perf_fp32_120.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_120.txt
deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_120.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_151.txt
deleted file mode 100644
index 17423a25717e5f1ef4224d5c5c1fdf2d49e757fd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_151.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_152.txt
deleted file mode 100644
index 65b63cbff6ee7eb4de969593d130343c438ab6d6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_152.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_153.txt
deleted file mode 100644
index 5b0c6da1e9835f283fdb03412acded28d054a5e6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_153.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_154.txt
deleted file mode 100644
index 067c920d312c59f9744c6b44995cba3dc9da1698..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_154.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_155.txt
deleted file mode 100644
index 37a7257c20638a86157576ba9927b5aaa0c3ffbe..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_155.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_156.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_156.txt
deleted file mode 100644
index f9f71a75a2e78e34be922ae773bfa9c791c2a3c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_156.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_157.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_157.txt
deleted file mode 100644
index d83b30ed36748e31285c98b911c2d0f051f16b0b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_157.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_158.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_158.txt
deleted file mode 100644
index e2b008c23901b088b1280c581a89c8234995fc71..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_158.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_159.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_159.txt
deleted file mode 100644
index 71d4f29c81dff8609f20a5f860737044d1f35efa..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_159.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_160.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_160.txt
deleted file mode 100644
index bd610058084cfde81ae8bd5522f782c71fc495aa..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_160.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_161.txt
deleted file mode 100644
index 393a1d43f9c935e9e0c166595c3a969961908f2f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_161.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_162.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_162.txt
deleted file mode 100644
index fb4626bdf9e07cf25b1ca67575ea1754dc11292e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_162.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_163.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_163.txt
deleted file mode 100644
index aeaff59555bf4dd4488057ebe107d423e452a728..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_163.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_164.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_164.txt
deleted file mode 100644
index 2d2d83329218d03a220a795739867f212389b0ce..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_164.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_165.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_165.txt
deleted file mode 100644
index 1b536588b3a9bb1d3c390b99f8694ba3f35a9114..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_165.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_166.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_166.txt
deleted file mode 100644
index 36738891fb7bdaf914c3df108c203d821697398e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_166.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_167.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_167.txt
deleted file mode 100644
index 023a48c70f216caab996a5f7754998e9daee720f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_167.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_168.txt
deleted file mode 100644
index 22ccbe9e1149bf3c0fbf39844e932b17acfcb7cb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_perf_fp16_168.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_261.txt
deleted file mode 100644
index 45af9a24777fe9eaecf431e5a0d510e295491e31..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_261.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_262.txt
deleted file mode 100644
index afd86204a851f474212cc8a01710567f273029fc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_262.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_263.txt
deleted file mode 100644
index 49071a42cb0be1bbfc0c55e8c370b45addc02c69..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_263.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_264.txt
deleted file mode 100644
index ed370c72a35a37cf5558de4eb8e00f30a5d396d0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_264.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_265.txt
deleted file mode 100644
index 50304795f436db23f1b47f41b544d664fa71eac9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_265.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_266.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_266.txt
deleted file mode 100644
index 2fec11a0373a923cedd956091368a97e10916b03..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_266.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_267.txt
deleted file mode 100644
index 93685543e165975790b21ed3b63feb5f731b76e3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_267.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_268.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_268.txt
deleted file mode 100644
index 15ea4be9c331557db770acf1fa4e5439f4e7357c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_268.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_269.txt
deleted file mode 100644
index ffe9c7f43dbf4070056d9c9b3dc7defa01c4ba82..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp16_samp_fp16_269.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp32_perf_fp32_120.txt
deleted file mode 100644
index 8d9d8fee56451e2961b8d7b53aed70aef75d0acb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/resnet50_imagenet/resnet50_imagenet_fp32_perf_fp32_120.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_120.txt
deleted file mode 100644
index 35b105d97a68bcbfbff43da403a925200b5f5382..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_120.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_151.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_151.txt
deleted file mode 100644
index 8280ad73a88b4ca9a6b56c4118210a280283cd5e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_151.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_152.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_153.txt
deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_153.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_154.txt
deleted file mode 100644
index d1e7a3a32ff94dcbad5f9812f10e034afca71201..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_154.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_155.txt
deleted file mode 100644
index 6e417397af8fb9593a07eca1c6bb12065668dc51..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_155.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_156.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_156.txt
deleted file mode 100644
index a7a3c079d38e5f5f638c5c65b5f92b01dce1533d..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_157.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_157.txt
deleted file mode 100644
index df8e948520da6185160850f198075fed6ed2d31d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_157.txt
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deleted file mode 100644
index 48144ad26dc7dc140a5edfa180c479cc59d88769..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_158.txt
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deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_159.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_160.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_160.txt
deleted file mode 100644
index 00bd498faf8a57e71a8921395f24aeabc40f2c3d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_160.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_161.txt
deleted file mode 100644
index feb7f4fb58c604f0fefaf03df971cae5992546a6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_161.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_162.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_162.txt
deleted file mode 100644
index 9b5c953995fcee3f5a87294d58c0d8bfbd03b7b5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_162.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_163.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_163.txt
deleted file mode 100644
index d440002ea739909721ab71f6a2637e3e4e5e43ae..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_163.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_164.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_164.txt
deleted file mode 100644
index 97bdfb6ee438be0723f2e206b035d0718c2e3c8a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_164.txt
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deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_165.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_167.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_167.txt
deleted file mode 100644
index ac8c32b7b4bd1794f0611a7602470a84e6e113dd..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_168.txt
deleted file mode 100644
index 33036bcf77a68012a02a3338ee2c58a69038a499..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_perf_fp16_168.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_261.txt
deleted file mode 100644
index 93c47b97c816b1c1dc19a5400efa40dc506985d3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_261.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_262.txt
deleted file mode 100644
index 3068a697954c6bf4d9f86b4a3d24ec31b7d4a496..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_262.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_263.txt
deleted file mode 100644
index 2b5bf1c1e37cf8db751dc4bb033bde819d2bb3ba..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_263.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_264.txt
deleted file mode 100644
index 6c046346913f021ca6bd916ed293565c7fabbf7a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_264.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_265.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_266.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_266.txt
deleted file mode 100644
index 9aa16c022611cdd686be61a166338c885af0a1ed..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_266.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_267.txt
deleted file mode 100644
index d819d236d8e802c02e1de724a58a17b19d4d88a7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_267.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_268.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_268.txt
deleted file mode 100644
index f83aa5a4709dc52995ef15de36e536773af41e2c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_268.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_269.txt
deleted file mode 100644
index c6c446c551e592d1da97339ecb863280e123ac11..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp16_samp_fp16_269.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp32_perf_fp32_120.txt
deleted file mode 100644
index abb7299681b6fb721682e76b110e429fa58b0bff..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar10/vgg16_cifar10_fp32_perf_fp32_120.txt
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deleted file mode 100644
index ecd5f1f273a176bed586d43a30ce54ac3128fc96..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_120.txt
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deleted file mode 100644
index c526f423205cedfa53089441044ee9bbc63d55b2..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_152.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_152.txt
deleted file mode 100644
index 22b1e98bab229ab157903d6bf9fb60615a218d73..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_152.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_153.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_153.txt
deleted file mode 100644
index b3b55cf48c5aa2edccc42166c8a667fa9ba0525d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_153.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_154.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_154.txt
deleted file mode 100644
index 92ec828264303390e919dffb5b8202e7056a7d2e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_154.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_155.txt
deleted file mode 100644
index 189a292990df4a90fcbde996d521074e0caf9aa9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_155.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_156.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_156.txt
deleted file mode 100644
index 04a89e57adad7d4a3efd4d25589a57176222c512..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_156.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_157.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_157.txt
deleted file mode 100644
index 3355365dc018744ad1a13d28b4bcbb9ea9aa3544..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_157.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_158.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_158.txt
deleted file mode 100644
index d519f756fb24c124a78a441beb64ef6b500bda78..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_158.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_159.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_159.txt
deleted file mode 100644
index 84702c96ffc953fd2e1350a3818c1d1dc2ca411c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_159.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_160.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_160.txt
deleted file mode 100644
index 7cca3d0807f2f07b371ed2d7ecc09c46cb98814c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_160.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_161.txt
deleted file mode 100644
index 74007f27b84264ab048cdf0559788f01861c4b3d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_161.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_162.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_162.txt
deleted file mode 100644
index f7efa4236329b869a83bada18c7d6e0886466beb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_162.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_163.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_163.txt
deleted file mode 100644
index 18b0d55b59930fba7d49e229cf26daf519c17c74..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_163.txt
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deleted file mode 100644
index 9f999df508b0097dc35d12b781d61a2db0267ccf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_164.txt
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deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_165.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_166.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_166.txt
deleted file mode 100644
index 743386a5264e4118f7902564f26afdd60c6cb4fd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_166.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_167.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_167.txt
deleted file mode 100644
index 0c0ce45f0e9ca21798834a2dcc5d89480c3aa4b8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_167.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_168.txt
deleted file mode 100644
index b99d2b30cba81cfa2ce0a9d416db8d0e021c7150..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_perf_fp16_168.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_261.txt
deleted file mode 100644
index 2c67cf74bc5de24304a0e61e2b1a29240f169f77..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_261.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_262.txt
deleted file mode 100644
index f14d74e574c91ea99311d1704ce7509fd59bd68e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_262.txt
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_263.txt
deleted file mode 100644
index 247eb7b675a38e9081891c54cacb6c161ed4dbdb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_263.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_264.txt
deleted file mode 100644
index 878f8d87162aa551eb44d6e2c79c6dccccafefea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_264.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_265.txt
deleted file mode 100644
index 36826ba6b9c86fa7510621e4962a02433d1be746..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_265.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_267.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_267.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_268.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_268.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_269.txt
deleted file mode 100644
index b1d756884be10fdeb15b96febf92375c5ffe95aa..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp16_samp_fp16_269.txt
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deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_cifar100/vgg16_cifar100_fp32_perf_fp32_120.txt
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_155.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_155.txt
deleted file mode 100644
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deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_156.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_158.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_158.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_159.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_159.txt
deleted file mode 100644
index 6008d5f448fea1da8a972d0261a0f86c061f4297..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_159.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_161.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_161.txt
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
index c59e479d329481849d8d3dac85ceab6dacb695fd..0000000000000000000000000000000000000000
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_168.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_perf_fp16_168.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_261.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_261.txt
deleted file mode 100644
index e4548f7e02c2297a343cf2900af977e1c3ae1ccb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_261.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_262.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_262.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_263.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_263.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_264.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_264.txt
deleted file mode 100644
index eba44020106d7110831698fe6797c3e08759b3c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_264.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_265.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_265.txt
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_266.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_266.txt
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deleted file mode 100644
index 5f30e7718826a5a44d04258d0a461470bf361dfc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_267.txt
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deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_269.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp16_samp_fp16_269.txt
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diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp32_perf_fp32_120.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp32_perf_fp32_120.txt
deleted file mode 100644
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--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/profiling_results/vgg16_imagenet/vgg16_imagenet_fp32_perf_fp32_120.txt
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-Add2,1.47909,22940.4
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-Relu2,0.578853,20923
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-Pool1,60.6459,467598
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-Conv3,1541.99,1.25939e+07
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-Conv4,2598.25,2.36776e+07
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-Add4,0.799391,29436.6
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-Relu4,0.599462,29567
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-Pool2,21.6318,230524
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-Pool2_h2f,0,0
-Conv5,744.163,7.87985e+06
-Conv5_f2h,0,0
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-Add5,0.431494,32244
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-Add5_h2f,0,0
-Relu5,0.286745,32244
-Relu5_f2h,0,0
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-Conv6,1576.37,1.74915e+07
-Conv6_f2h,0,0
-Conv6_h2f,0,0
-Add6,0.467628,33194.6
-Add6_f2h,0,0
-Add6_h2f,0,0
-Relu6,0.387506,33190.8
-Relu6_f2h,0,0
-Relu6_h2f,0,0
-Conv7,1744.67,1.92649e+07
-Conv7_f2h,0,0
-Conv7_h2f,0,0
-Add7,0.43932,32155.4
-Add7_f2h,0,0
-Add7_h2f,0,0
-Relu7,0.295117,32174
-Relu7_f2h,0,0
-Relu7_h2f,0,0
-Pool3,11.0778,134872
-Pool3_f2h,0,0
-Pool3_h2f,0,0
-Conv8,493.441,5.963e+06
-Conv8_f2h,0,0
-Conv8_h2f,0,0
-Add8,0.433138,35087.4
-Add8_f2h,0,0
-Add8_h2f,0,0
-Relu8,0.380959,35076
-Relu8_f2h,0,0
-Relu8_h2f,0,0
-Conv9,910.678,1.20378e+07
-Conv9_f2h,0,0
-Conv9_h2f,0,0
-Add9,0.450066,38405.2
-Add9_f2h,0,0
-Add9_h2f,0,0
-Relu9,0.407878,38378.6
-Relu9_f2h,0,0
-Relu9_h2f,0,0
-Conv10,897.248,1.2633e+07
-Conv10_f2h,0,0
-Conv10_h2f,0,0
-Add10,0.449388,40364.8
-Add10_f2h,0,0
-Add10_h2f,0,0
-Relu10,0.386828,40322.8
-Relu10_f2h,0,0
-Relu10_h2f,0,0
-Pool4,6.03651,96459
-Pool4_f2h,0,0
-Pool4_h2f,0,0
-Conv11,246.238,3.40541e+06
-Conv11_f2h,0,0
-Conv11_h2f,0,0
-Add11,0.428479,40601.8
-Add11_f2h,0,0
-Add11_h2f,0,0
-Relu11,0.351365,40546
-Relu11_f2h,0,0
-Relu11_h2f,0,0
-Conv12,238.715,3.44638e+06
-Conv12_f2h,0,0
-Conv12_h2f,0,0
-Add12,0.414546,40587.4
-Add12_f2h,0,0
-Add12_h2f,0,0
-Relu12,0.273535,40563.4
-Relu12_f2h,0,0
-Relu12_h2f,0,0
-Conv13,236.791,3.49246e+06
-Conv13_f2h,0,0
-Conv13_h2f,0,0
-Add13,0.413625,40604.8
-Add13_f2h,0,0
-Add13_h2f,0,0
-Relu13,0.31061,40577
-Relu13_f2h,0,0
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-Pool5,3.85133,81098.8
-Pool5_f2h,0,0
-Pool5_h2f,0,0
-Mul1,30.3164,365908
-Mul1_f2h,0,0
-Mul1_h2f,0,0
-Add14,3.40558,74419
-Add14_f2h,0,0
-Add14_h2f,0,0
-Relu14,0.301913,40155.8
-Relu14_f2h,0,0
-Relu14_h2f,0,0
-Mul2,4.51905,81648.2
-Mul2_f2h,0,0
-Mul2_h2f,0,0
-Add15,0.766091,44549.6
-Add15_f2h,0,0
-Add15_h2f,0,0
-Relu15,0.310483,40165.6
-Relu15_f2h,0,0
-Relu15_h2f,0,0
-Mul3,1.77246,50359.2
-Mul3_f2h,0,0
-Mul3_h2f,0,0
-Add16,0.339148,40173.6
-Add16_f2h,0,0
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-Softmax1,65.736,976903
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-Softmax1_h2f,0,0
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet/alexnet_valid_soc.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet/alexnet_valid_soc.txt
deleted file mode 100644
index 1b7aeb981c745717c52c841f99672cfbd532f7cb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet/alexnet_valid_soc.txt
+++ /dev/null
@@ -1,231 +0,0 @@
-2725.121326
-+++++
-conf1 1 1 78.78 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-6 gpu mul fp32 11 add fp32 1
-7 gpu softmax fp32 1
------
-+++++
-conf2 2.1233638648528457 1.6150951710244676 78.3544 0.42560000000000286
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf3 2.051295134864554 1.6122580072322763 78.3278 0.4522000000000048
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf4 2.188609573694276 1.688911612634961 78.30120000000001 0.47879999999999256
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf5 2.0570505767108007 1.6000014977491621 78.2214 0.5585999999999984
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 265 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf6 2.009166522889861 1.5755494376470724 78.1948 0.5852000000000004
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf7 2.0188668300066377 1.5976556515195433 78.06179999999999 0.7182000000000102
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 266 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf8 2.1797184471932716 1.6767378001241562 78.06179999999999 0.7182000000000102
-1 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf9 2.064914192886025 1.6203964986881603 78.06179999999999 0.7182000000000102
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf10 2.2070171560926672 1.7194657877315815 78.0352 0.7447999999999979
-1 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 265 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf11 2.0161469236407057 1.5964768988685245 78.0086 0.7713999999999999
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf12 2.157846755426679 1.6765250202752133 78.0086 0.7713999999999999
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf13 2.0319664118931096 1.6183541826275754 77.98200000000001 0.7979999999999876
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf14 2.354997704376988 1.7779732164691666 77.98200000000001 0.7979999999999876
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv fp16 12 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf15 2.3463673263694 1.8510470086526165 77.98200000000001 0.7979999999999876
-1 gpu conv samp_fp16 264 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf16 2.284714727579521 1.7855758235498087 77.7692 1.0108000000000033
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-5 gpu conv samp_fp16 269 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf17 2.3463673263694 1.8510470086526165 77.68939999999999 1.0906000000000091
-1 gpu conv samp_fp16 264 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf18 2.427840309027486 1.9007943438562696 77.68939999999999 1.0906000000000091
-1 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 263 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf19 2.4671009475732766 1.9246545843862224 77.47659999999999 1.3034000000000106
-1 gpu conv samp_fp16 264 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf20 2.5567127702266332 1.9773019485322874 77.2638 1.5161999999999978
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf21 2.557898283218207 1.9895818051250724 77.2372 1.5427999999999997
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf22 2.557898283218207 1.9895818051250724 77.21060000000001 1.5693999999999875
-1 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
-+++++
-conf23 2.6457265307759883 2.029290916760937 77.1574 1.6226000000000056
-1 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-6 gpu mul fp16 12 add fp16 12
-7 gpu softmax fp16 12
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet2/alexnet2_valid_soc.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet2/alexnet2_valid_soc.txt
deleted file mode 100644
index a888b5ee5a50d140f60d6579a3f6bdb6aa5ddfbd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/alexnet2/alexnet2_valid_soc.txt
+++ /dev/null
@@ -1,188 +0,0 @@
-1129.3450630000002
-+++++
-conf1 1 1 84.76 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1
-6 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-7 gpu mul fp32 11 add fp32 1
-8 gpu softmax fp32 1
------
-+++++
-conf2 2.2258170210610477 1.3875307929727092 84.74 0.020000000000010232
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 151 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf3 2.3673182996864846 1.4566777038051897 84.49999999999999 0.2600000000000193
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf4 2.24614762418964 1.41800542976017 84.25999999999999 0.5000000000000142
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 158 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf5 2.304084258604824 1.4284953488024343 84.228 0.5320000000000107
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 151 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 267 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf6 2.3377766277342653 1.4440340860007412 84.228 0.5320000000000107
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-6 gpu conv fp16 12 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf7 2.24614762418964 1.41800542976017 84.17479999999999 0.5852000000000146
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 158 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf8 2.3673182996864846 1.4566777038051897 84.095 0.6650000000000063
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf9 2.2463714607055545 1.417884448648111 83.8024 0.9575999999999993
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 158 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 266 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf10 2.389025803395913 1.4732901147183992 83.77579999999999 0.9842000000000155
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf11 2.288831273542033 1.435952475412438 83.61619999999999 1.143800000000013
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 158 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf12 2.288831273542033 1.435952475412438 83.58959999999999 1.170400000000015
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 158 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf13 2.389025803395913 1.4732901147183992 83.58959999999999 1.170400000000015
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 268 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf14 2.3892790238475423 1.4731595166090572 83.4566 1.3034000000000106
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 266 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf15 2.390450803781405 1.4707319718833016 83.3768 1.3832000000000022
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 266 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 157 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf16 2.4373708430335537 1.49267343110314 83.3768 1.3832000000000022
-1 gpu conv fp16 11 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
-+++++
-conf17 2.4373708430335537 1.49267343110314 83.2704 1.48960000000001
-1 gpu conv fp16 12 add fp16 12 tanh fp16 12
-2 gpu conv perf_fp16 153 add fp16 12 tanh fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 tanh fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 261 add fp16 12 tanh fp16 12
-6 gpu conv perf_fp16 160 add fp16 12 tanh fp16 12 pool_max fp16 12
-7 gpu mul fp16 12 add fp16 12
-8 gpu softmax fp16 12
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/resnet18/resnet18_valid_soc.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/resnet18/resnet18_valid_soc.txt
deleted file mode 100644
index 942789c1c4defd1139e75209ffbcb073a2b39b30..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/resnet18/resnet18_valid_soc.txt
+++ /dev/null
@@ -1,1576 +0,0 @@
-2593.3013975999997
-+++++
-conf1 1 1 89.42 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1
-3 gpu conv fp32 11 add fp32 1
-4 gpu add fp32 11
-5 gpu relu fp32 11
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1
-8 gpu add fp32 11
-9 gpu relu fp32 11
-10 gpu conv fp32 11 add fp32 1 relu fp32 1
-11 gpu conv fp32 11 add fp32 1
-12 gpu add fp32 11
-13 gpu relu fp32 11
-14 gpu conv fp32 11 add fp32 1 relu fp32 1
-15 gpu conv fp32 11 add fp32 1
-16 gpu conv fp32 11 add fp32 1
-17 gpu add fp32 11
-18 gpu relu fp32 11
-19 gpu conv fp32 11 add fp32 1 relu fp32 1
-20 gpu conv fp32 11 add fp32 1
-21 gpu add fp32 11
-22 gpu relu fp32 11
-23 gpu conv fp32 11 add fp32 1 relu fp32 1
-24 gpu conv fp32 11 add fp32 1
-25 gpu add fp32 11
-26 gpu relu fp32 11
-27 gpu conv fp32 11 add fp32 1 relu fp32 1
-28 gpu conv fp32 11 add fp32 1
-29 gpu conv fp32 11 add fp32 1
-30 gpu add fp32 11
-31 gpu relu fp32 11
-32 gpu conv fp32 11 add fp32 1 relu fp32 1
-33 gpu conv fp32 11 add fp32 1
-34 gpu add fp32 11
-35 gpu relu fp32 11
-36 gpu conv fp32 11 add fp32 1 relu fp32 1
-37 gpu conv fp32 11 add fp32 1
-38 gpu add fp32 11
-39 gpu relu fp32 11
-40 gpu pool_mean fp32 11
-41 gpu mul fp32 11 add fp32 1
-42 gpu softmax fp32 1
------
-+++++
-conf2 1.8227860146926984 1.3592380545823108 88.28 1.1400000000000006
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 162 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 166 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf3 1.772745264351603 1.3340968704252147 88.2 1.2199999999999989
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 166 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf4 1.831301934833889 1.3636544094268177 88.2 1.2199999999999989
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf5 1.7541385118416233 1.323200331238725 88.12 1.2999999999999972
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 166 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf6 1.750881760437994 1.3214899710791683 88.12 1.2999999999999972
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 166 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf7 1.9207420870636576 1.4105446231099241 88.1 1.3200000000000074
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv fp16 12 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf8 1.897654446584276 1.3943617562849198 88.1 1.3200000000000074
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 262 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf9 1.9276001243246026 1.4155139358802007 88.08 1.3400000000000034
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 168 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 155 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf10 1.8877611861107602 1.3945090937373315 88.03999999999999 1.3800000000000097
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 154 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 166 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf11 1.884015904997108 1.386748889441216 87.96000000000001 1.4599999999999937
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 262 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf12 1.815742308450095 1.3541765419789824 87.83999999999999 1.5800000000000125
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 262 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf13 1.928011277898605 1.414528053850526 87.83999999999999 1.5800000000000125
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 155 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf14 1.8702574116471649 1.3838796270391824 87.8 1.6200000000000045
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 269 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf15 1.9390257777318618 1.4193909923193697 87.8 1.6200000000000045
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 155 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf16 1.8505712546542585 1.372601565984325 87.76 1.6599999999999966
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf17 1.931335957581042 1.4149043748735137 87.74 1.6800000000000068
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 157 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf18 1.8390656100510818 1.3668229301466752 87.68 1.7399999999999949
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf19 1.9360126662655235 1.416245073512222 87.64 1.7800000000000011
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 155 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 264 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf20 1.826739398491775 1.3609522133620269 87.62 1.7999999999999972
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 153 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv samp_fp16 262 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 165 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf21 1.8243322012642802 1.3542277148411042 87.62 1.7999999999999972
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf22 1.8245510435946863 1.3601414031759373 87.58 1.8400000000000034
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv samp_fp16 269 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf23 1.9832010015590205 1.4407797001367388 87.56 1.8599999999999994
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 261 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 155 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf24 1.831958859203629 1.3643626254848584 87.5 1.9200000000000017
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 151 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf25 1.827209961997738 1.3576190436536635 87.5 1.9200000000000017
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 262 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf26 1.9532893879837718 1.4253186875342474 87.5 1.9200000000000017
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 168 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 262 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf27 1.8598315807624513 1.376813374656673 87.48 1.9399999999999977
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf28 1.8545931630272876 1.3744725755811524 87.48 1.9399999999999977
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 267 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 152 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf29 1.9088935397779812 1.4033062374488858 87.44 1.980000000000004
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 163 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 267 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf30 1.8306014158563824 1.3613821654101905 87.44 1.980000000000004
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 164 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 265 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 168 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 262 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf31 1.9755297077095708 1.4378811225069261 87.44 1.980000000000004
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 159 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 159 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 155 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf32 1.827200177575606 1.356175543415313 87.38 2.0400000000000063
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 156 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 264 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv perf_fp16 167 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf33 1.8517276001191023 1.3729319418960464 87.38 2.0400000000000063
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-7 gpu conv fp16 12 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv samp_fp16 269 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 157 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 12 relu fp16 12
-24 gpu conv perf_fp16 160 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv samp_fp16 268 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 269 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf34 1.8938192956663813 1.3919348631813433 87.38 2.0400000000000063
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 165 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv fp16 11 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 262 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
-+++++
-conf35 1.8989539669005067 1.3938360809175603 87.36 2.0600000000000023
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv fp16 12 add fp16 12 relu fp16 12
-3 gpu conv fp16 12 add fp16 12
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 263 add fp16 12
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 12 relu fp16 12
-11 gpu conv perf_fp16 154 add fp16 12
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 12 relu fp16 12
-15 gpu conv fp16 12 add fp16 12
-16 gpu conv fp16 11 add fp16 12
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 153 add fp16 12 relu fp16 12
-20 gpu conv perf_fp16 151 add fp16 12
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 157 add fp16 12 relu fp16 12
-24 gpu conv samp_fp16 268 add fp16 12
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-28 gpu conv fp16 12 add fp16 12
-29 gpu conv perf_fp16 154 add fp16 12
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 12 relu fp16 12
-33 gpu conv fp16 12 add fp16 12
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-37 gpu conv samp_fp16 262 add fp16 12
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 12
-42 gpu softmax fp16 12
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar10/vgg16_cifar10_valid_soc.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar10/vgg16_cifar10_valid_soc.txt
deleted file mode 100644
index 789f4e21cf4a778535d1df0f9f7be22c1415d672..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar10/vgg16_cifar10_valid_soc.txt
+++ /dev/null
@@ -1,1027 +0,0 @@
-3994.0731450000017
-+++++
-conf1 1 1 89.22 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.3049904288987464 1.6887800235455193 89.14 0.0799999999999983
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 155 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv fp16 11 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf3 2.357615734902983 1.7226289827534114 89.14 0.0799999999999983
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 155 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf4 2.3831343547359976 1.7374446557158316 88.84 0.37999999999999545
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 162 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf5 2.3696393667573616 1.7284732038695636 88.8 0.4200000000000017
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 162 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 265 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf6 2.4444787116056292 1.7833916898567774 88.58 0.6400000000000006
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf7 2.40209759505425 1.7661661942711917 88.58 0.6400000000000006
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf8 2.528892013058046 1.8332619869789675 88.08 1.1400000000000006
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf9 2.5283008295291105 1.8324605771289624 88.06 1.1599999999999966
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf10 2.5562616043247313 1.847605117430125 88.03999999999999 1.1800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf11 2.5337351216813757 1.836759334487813 88.03999999999999 1.1800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf12 2.556171297969468 1.8482604143790797 88.03999999999999 1.1800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf13 2.5562385363337343 1.8481145682015834 88.03999999999999 1.1800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf14 2.556612910921585 1.8486422226408725 88.03999999999999 1.1800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf15 2.5419253262471346 1.8395765136023223 88.02 1.2000000000000028
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 263 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf16 2.4937721600323406 1.8116328904640306 88.0 1.2199999999999989
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv perf_fp16 162 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf17 2.5545877208248187 1.8465313171321942 88.0 1.2199999999999989
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf18 2.528537397828869 1.8330988121074523 88.0 1.2199999999999989
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf19 2.531670576114998 1.8357132731685366 88.0 1.2199999999999989
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf20 2.5294693760803577 1.8335105878862015 87.98 1.2399999999999949
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 268 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf21 2.5582293136941723 1.8476583031165972 87.98 1.2399999999999949
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 156 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf22 2.556327374925176 1.8481587827658859 87.98 1.2399999999999949
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf23 2.557806470696261 1.8492020211230846 87.98 1.2399999999999949
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf24 2.5545697480449 1.8464092920718178 87.96000000000001 1.259999999999991
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 267 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf25 2.528206406642683 1.832658178797549 87.96000000000001 1.259999999999991
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf26 2.556533707152568 1.8484262997816934 87.96000000000001 1.259999999999991
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf27 2.5393059900815325 1.837123626585959 87.94 1.2800000000000011
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 265 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf28 2.5486219361262235 1.845481069177171 87.94 1.2800000000000011
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf29 2.5485321687357825 1.8461348600374907 87.94 1.2800000000000011
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf30 2.5657339222733015 1.8517901869245543 87.92 1.2999999999999972
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 263 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf31 2.581139532058275 1.860666047394923 87.92 1.2999999999999972
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf32 2.5098654459068945 1.8297655130336108 87.92 1.2999999999999972
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf33 2.528587182046725 1.8312521826965082 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 156 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf34 2.517311952294846 1.8204468250382393 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf35 2.517311952294846 1.8204468250382393 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf36 2.517311952294846 1.8204468250382393 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf37 2.517311952294846 1.8204468250382393 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf38 2.5346932948358267 1.8376287813464989 87.9 1.3199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 265 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf39 2.4914548049246 1.8095620501702707 87.86 1.3599999999999994
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv perf_fp16 162 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 268 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf40 2.5809312104420865 1.8607657818447936 87.86 1.3599999999999994
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf41 2.5120056276901925 1.824277681148882 87.83999999999999 1.3800000000000097
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 268 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf42 2.556168516896762 1.849243225747987 87.83999999999999 1.3800000000000097
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf43 2.512713457130698 1.8053797549107755 87.82 1.4000000000000057
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf44 2.509447559327321 1.8294109824358684 87.82 1.4000000000000057
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf45 2.532043246184595 1.8347717424454622 87.74 1.480000000000004
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv samp_fp16 265 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf46 2.4911011329750212 1.795311376068545 87.68 1.539999999999992
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 155 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 153 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf47 2.549746515565958 1.8283676275816687 87.66000000000001 1.559999999999988
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv fp16 12 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-9 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf48 2.51145215830771 1.8254971754777813 87.64 1.5799999999999983
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf49 2.513356522647888 1.826263067419964 87.58 1.6400000000000006
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf50 2.513356522647888 1.826263067419964 87.53999999999999 1.6800000000000068
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf51 2.4881677905203494 1.8127135485543127 87.4 1.8199999999999932
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf52 2.51145215830771 1.8254971754777813 87.36 1.8599999999999994
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 266 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf53 2.4757784613808234 1.7991027289904775 87.26 1.9599999999999937
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 269 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf54 2.5913526715019284 1.8695479088125426 87.24 1.980000000000004
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv perf_fp16 163 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv perf_fp16 151 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar100/vgg16_cifar100_valid_soc.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar100/vgg16_cifar100_valid_soc.txt
deleted file mode 100644
index ef6509b99bee287bf0e3dfbaa035d51f9e3cb0ea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/runtime_experiments/vgg16_cifar100/vgg16_cifar100_valid_soc.txt
+++ /dev/null
@@ -1,210 +0,0 @@
-3845.438677999999
-+++++
-conf1 1 1 68.42 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.4361074671227554 1.7555866253938424 67.22 1.2000000000000028
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 163 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv fp16 11 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 264 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf3 2.602684148359414 1.8286503060252126 67.10000000000001 1.3199999999999932
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 156 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv fp16 11 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf4 2.661880095451371 1.886369953641946 67.06 1.3599999999999994
-1 gpu conv fp16 12 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 156 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf5 2.5990656605003855 1.8588553950032938 66.84 1.5799999999999983
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 163 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf6 2.5884968081531485 1.8594972115815722 66.8 1.6200000000000045
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 165 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf7 2.4323231936537972 1.8028228076034056 66.8 1.6200000000000045
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf8 2.575472326184571 1.8375078883357683 66.72 1.7000000000000028
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv perf_fp16 161 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv fp16 11 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf9 2.4912510106198957 1.848807665058795 66.58 1.8400000000000034
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 266 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf10 2.4323231936537972 1.8028228076034056 66.53999999999999 1.8800000000000097
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 152 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
-+++++
-conf11 2.4027045398540046 1.7853827712848849 66.47999999999999 1.940000000000012
-1 gpu conv fp16 11 add fp16 12 relu fp16 12
-2 gpu conv samp_fp16 269 add fp16 12 relu fp16 12 pool_max fp16 12
-3 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-4 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-5 gpu conv fp16 12 add fp16 12 relu fp16 12
-6 gpu conv samp_fp16 261 add fp16 12 relu fp16 12
-7 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-8 gpu conv perf_fp16 155 add fp16 12 relu fp16 12
-9 gpu conv samp_fp16 262 add fp16 12 relu fp16 12
-10 gpu conv samp_fp16 262 add fp16 12 relu fp16 12 pool_max fp16 12
-11 gpu conv perf_fp16 160 add fp16 12 relu fp16 12
-12 gpu conv perf_fp16 151 add fp16 12 relu fp16 12
-13 gpu conv samp_fp16 261 add fp16 12 relu fp16 12 pool_max fp16 12
-14 gpu mul fp16 12 add fp16 12 relu fp16 12
-15 gpu mul fp16 12 add fp16 12
-16 gpu softmax fp16 12
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/README.md b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet2_cifar10/alexnet2_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet2_cifar10/alexnet2_cifar10.txt
deleted file mode 100644
index 208f154e02ef37a6ae87904844c826ce72012b32..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet2_cifar10/alexnet2_cifar10.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-1114.3009809999999
-+++++
-conf1 1 1 84.76 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1
-6 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-7 gpu mul fp32 11 add fp32 1
-8 gpu softmax fp32 1
------
-+++++
-conf2 1.678391931801309 1.4393008204786808 84.76 0.0
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_cifar10/alexnet_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_cifar10/alexnet_cifar10.txt
deleted file mode 100644
index eba22e3f01e227041fcb406f87a996837cd5fa2b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_cifar10/alexnet_cifar10.txt
+++ /dev/null
@@ -1,421 +0,0 @@
-2592.187221
-+++++
-conf1 1 1 78.78 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-6 gpu mul fp32 11 add fp32 1
-7 gpu softmax fp32 1
------
-+++++
-conf2 1.7593976485873195 1.6193399031642917 78.78 0.0
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf3 2.081712090729918 1.9102226906341664 78.53999999999999 0.2400000000000091
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf4 2.081712090729918 1.9102226906341664 78.53999999999999 0.2400000000000091
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf5 2.2627828537139263 2.065683616898884 78.34 0.4399999999999977
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf6 2.3527290658539215 2.145832257234814 78.10000000000001 0.6799999999999926
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf7 2.3527290658539215 2.145832257234814 78.10000000000001 0.6799999999999926
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf8 2.3527290658539215 2.145832257234814 78.10000000000001 0.6799999999999926
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf9 2.2247938983110425 2.060416584958474 77.98 0.7999999999999972
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf10 2.2247938983110425 2.060416584958474 77.98 0.7999999999999972
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf11 2.4370818494175888 2.250857540113024 77.98 0.7999999999999972
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf12 2.432854949808342 2.2424500615508003 77.9 0.8799999999999955
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf13 2.432854949808342 2.2424500615508003 77.9 0.8799999999999955
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf14 2.432854949808342 2.2424500615508003 77.9 0.8799999999999955
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf15 2.228328207535687 2.0675123320068267 77.82 0.960000000000008
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf16 2.228328207535687 2.0675123320068267 77.82 0.960000000000008
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf17 2.3417491169395532 2.1355030360671465 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf18 2.3417491169395532 2.1355030360671465 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf19 2.3417491169395532 2.1355030360671465 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf20 2.5243776633638846 2.324968713897418 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf21 2.5243776633638846 2.324968713897418 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf22 2.5243776633638846 2.324968713897418 77.78 1.0
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf23 2.5371416718362823 2.3372173527293847 77.56 1.2199999999999989
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf24 2.5371416718362823 2.3372173527293847 77.56 1.2199999999999989
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf25 2.472472828611022 2.286262888143739 77.48 1.2999999999999972
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf26 2.574475112841438 2.3637004022727544 77.4 1.3799999999999955
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf27 2.1200397577541747 1.951741010849448 77.3 1.480000000000004
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf28 2.1200397577541747 1.951741010849448 77.3 1.480000000000004
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf29 2.5289288699015304 2.334007588396142 77.2 1.5799999999999983
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf30 2.5289288699015304 2.334007588396142 77.2 1.5799999999999983
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf31 2.5289288699015304 2.334007588396142 77.2 1.5799999999999983
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf32 2.541739061163583 2.3463519042470864 77.18 1.5999999999999943
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf33 2.541739061163583 2.3463519042470864 77.18 1.5999999999999943
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf34 2.580258965052788 2.3848508703934153 76.96 1.8200000000000074
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf35 2.580258965052788 2.3848508703934153 76.96 1.8200000000000074
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf36 2.4768386387310675 2.295002745725082 76.94 1.8400000000000034
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf37 2.5713008246729716 2.3684101116633007 76.94 1.8400000000000034
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf38 2.5713008246729716 2.3684101116633007 76.94 1.8400000000000034
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf39 2.5670585645212847 2.3720992406158463 76.92 1.8599999999999994
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf40 2.5670585645212847 2.3720992406158463 76.92 1.8599999999999994
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf41 2.5760229577267673 2.3777906009584133 76.9 1.8799999999999955
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf42 2.5760229577267673 2.3777906009584133 76.9 1.8799999999999955
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_imagenet/alexnet_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_imagenet/alexnet_imagenet.txt
deleted file mode 100644
index 8ae986b90ce53e80d10e19525a51ec32f51397d8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/alexnet_imagenet/alexnet_imagenet.txt
+++ /dev/null
@@ -1,289 +0,0 @@
-2739.950736
-+++++
-conf1 1 1 56.3 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-6 gpu mul fp32 11 add fp32 1 relu fp32 1
-7 gpu mul fp32 11 add fp32 1 relu fp32 1
-8 gpu mul fp32 11 add fp32 1
-9 gpu softmax fp32 1
------
-+++++
-conf2 1.802133644103582 1.8186433204507424 55.76 0.5399999999999991
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf3 2.0227701930718065 2.043112495268932 55.42 0.8799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf4 1.8063132288735129 1.8239088223620996 54.96 1.3399999999999963
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf5 1.8063132288735129 1.8239088223620996 54.96 1.3399999999999963
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf6 1.8063132288735129 1.8239088223620996 54.96 1.3399999999999963
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf7 2.085011755614172 2.122606306624671 54.92 1.3799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf8 2.085011755614172 2.122606306624671 54.92 1.3799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf9 1.8052659214923805 1.8217111622759978 54.82 1.4799999999999969
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf10 2.0146435217865446 2.0367475358800102 54.58 1.7199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf11 1.9101312060368951 1.9552389688678584 54.24 2.059999999999995
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf12 1.9101312060368951 1.9552389688678584 54.24 2.059999999999995
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf13 1.9101312060368951 1.9552389688678584 54.24 2.059999999999995
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf14 2.019868378233057 2.0433540129730265 54.17999999999999 2.1200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf15 2.019868378233057 2.0433540129730265 54.17999999999999 2.1200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf16 2.028037341700216 2.049760395549724 53.98 2.3200000000000003
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf17 2.028037341700216 2.049760395549724 53.98 2.3200000000000003
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf18 2.028037341700216 2.049760395549724 53.98 2.3200000000000003
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf19 1.8052659214923805 1.8217111622759978 53.879999999999995 2.4200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 11 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf20 1.8052659214923805 1.8217111622759978 53.879999999999995 2.4200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 11 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf21 2.0267172350289036 2.046985186681549 53.86 2.4399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf22 2.0267172350289036 2.046985186681549 53.86 2.4399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf23 2.0267172350289036 2.046985186681549 53.86 2.4399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf24 2.0185588815268836 2.0405961127674277 53.559999999999995 2.740000000000002
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/lenet_keras/lenet_keras.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/lenet_keras/lenet_keras.txt
deleted file mode 100644
index da88f7cd26b049fd18644a834e4d34b944149cb2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/lenet_keras/lenet_keras.txt
+++ /dev/null
@@ -1,409 +0,0 @@
-282.5141369999999
-+++++
-conf1 1 1 98.7 0.0
-1 gpu conv fp32 11 add fp32 1 pool_max fp32 1 tanh fp32 1
-2 gpu conv fp32 11 add fp32 1 pool_max fp32 1 tanh fp32 1
-3 gpu mul fp32 11 add fp32 1 tanh fp32 1
-4 gpu mul fp32 11 add fp32 1 tanh fp32 1
-5 gpu softmax fp32 1
------
-+++++
-conf2 1.9343699741206566 2.1183040240042 98.68 0.01999999999999602
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 265 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf3 1.9343699741206566 2.1183040240042 98.68 0.01999999999999602
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 265 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf4 1.8936889628815377 2.139779619692146 98.68 0.01999999999999602
-1 gpu conv perf_fp16 152 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf5 1.8936889628815377 2.139779619692146 98.68 0.01999999999999602
-1 gpu conv perf_fp16 152 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf6 1.6415764141643088 1.8012120076077847 98.66 0.04000000000000625
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 265 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf7 1.9358279784215788 2.1233340385374495 98.66 0.04000000000000625
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf8 1.9358279784215788 2.1233340385374495 98.66 0.04000000000000625
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf9 1.6319327047042609 1.8046853367113418 98.64 0.060000000000002274
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf10 1.6319327047042609 1.8046853367113418 98.64 0.060000000000002274
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf11 1.6319327047042609 1.8046853367113418 98.64 0.060000000000002274
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf12 1.6319327047042609 1.8046853367113418 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf13 1.6319327047042609 1.8046853367113418 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf14 1.5602284338468988 1.7102497386784767 98.61999999999999 0.0800000000000125
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf15 1.5602284338468988 1.7102497386784767 98.61999999999999 0.0800000000000125
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf16 1.5602284338468988 1.7102497386784767 98.61999999999999 0.0800000000000125
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf17 1.8224050632690918 1.9936046569348063 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf18 1.8224050632690918 1.9936046569348063 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf19 1.8224050632690918 1.9936046569348063 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf20 2.2168527051833635 2.453341076720038 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf21 2.2168527051833635 2.453341076720038 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf22 1.9040998718547615 2.1501783570812565 98.61999999999999 0.0800000000000125
-1 gpu conv perf_fp16 151 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf23 1.9040998718547615 2.1501783570812565 98.61999999999999 0.0800000000000125
-1 gpu conv perf_fp16 151 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf24 1.5630416487818 1.7451546885860074 98.6 0.10000000000000853
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf25 1.5630416487818 1.7451546885860074 98.6 0.10000000000000853
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf26 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf27 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf28 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf29 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf30 2.1941568976363475 2.4445764373737644 98.6 0.10000000000000853
-1 gpu conv samp_fp16 269 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf31 2.1941568976363475 2.4445764373737644 98.6 0.10000000000000853
-1 gpu conv samp_fp16 269 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf32 1.5602284338468988 1.7102497386784767 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf33 1.5602284338468988 1.7102497386784767 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf34 1.5602284338468988 1.7102497386784767 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf35 1.9209933607603906 2.123109543083542 98.58 0.12000000000000455
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf36 1.9209933607603906 2.123109543083542 98.58 0.12000000000000455
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf37 1.9209933607603906 2.123109543083542 98.58 0.12000000000000455
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf38 1.8406161850501603 2.037849502542524 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf39 1.8406161850501603 2.037849502542524 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf40 1.8445326456180258 2.087601822059355 98.58 0.12000000000000455
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf41 1.8445326456180258 2.087601822059355 98.58 0.12000000000000455
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf42 1.8649226857257986 2.1076025277601325 98.56 0.14000000000000057
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf43 1.8649226857257986 2.1076025277601325 98.56 0.14000000000000057
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf44 1.8463058650555446 2.067271423078985 98.56 0.14000000000000057
-1 gpu conv perf_fp16 157 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf45 1.8463058650555446 2.067271423078985 98.56 0.14000000000000057
-1 gpu conv perf_fp16 157 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf46 1.9234076467497994 2.1864740913112275 98.56 0.14000000000000057
-1 gpu conv perf_fp16 153 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf47 1.9234076467497994 2.1864740913112275 98.56 0.14000000000000057
-1 gpu conv perf_fp16 153 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf48 1.6319327047042609 1.8046853367113418 98.54 0.1599999999999966
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf49 1.6350106933897723 1.8435952834193967 98.52 0.18000000000000682
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf50 1.6350106933897723 1.8435952834193967 98.52 0.18000000000000682
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf51 1.6510114896409525 1.8591762752048948 98.48 0.21999999999999886
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/mobilenet_cifar10/mobilenet_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/mobilenet_cifar10/mobilenet_cifar10.txt
deleted file mode 100644
index 93ca37c00a73f1a1cfc72bf58e8067906269d813..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/mobilenet_cifar10/mobilenet_cifar10.txt
+++ /dev/null
@@ -1,871 +0,0 @@
-4077.307063200001
-+++++
-conf1 1 1 84.42 0.0
-1 gpu conv fp32 11
-2 gpu batchnorm fp32 11
-3 gpu relu fp32 11
-4 gpu group_conv fp32 11
-5 gpu batchnorm fp32 11
-6 gpu relu fp32 11
-7 gpu conv fp32 11
-8 gpu batchnorm fp32 11
-9 gpu relu fp32 11
-10 gpu group_conv fp32 11
-11 gpu batchnorm fp32 11
-12 gpu relu fp32 11
-13 gpu conv fp32 11
-14 gpu batchnorm fp32 11
-15 gpu relu fp32 11
-16 gpu group_conv fp32 11
-17 gpu batchnorm fp32 11
-18 gpu relu fp32 11
-19 gpu conv fp32 11
-20 gpu batchnorm fp32 11
-21 gpu relu fp32 11
-22 gpu group_conv fp32 11
-23 gpu batchnorm fp32 11
-24 gpu relu fp32 11
-25 gpu conv fp32 11
-26 gpu batchnorm fp32 11
-27 gpu relu fp32 11
-28 gpu group_conv fp32 11
-29 gpu batchnorm fp32 11
-30 gpu relu fp32 11
-31 gpu conv fp32 11
-32 gpu batchnorm fp32 11
-33 gpu relu fp32 11
-34 gpu group_conv fp32 11
-35 gpu batchnorm fp32 11
-36 gpu relu fp32 11
-37 gpu conv fp32 11
-38 gpu batchnorm fp32 11
-39 gpu relu fp32 11
-40 gpu group_conv fp32 11
-41 gpu batchnorm fp32 11
-42 gpu relu fp32 11
-43 gpu conv fp32 11
-44 gpu batchnorm fp32 11
-45 gpu relu fp32 11
-46 gpu group_conv fp32 11
-47 gpu batchnorm fp32 11
-48 gpu relu fp32 11
-49 gpu conv fp32 11
-50 gpu batchnorm fp32 11
-51 gpu relu fp32 11
-52 gpu group_conv fp32 11
-53 gpu batchnorm fp32 11
-54 gpu relu fp32 11
-55 gpu conv fp32 11
-56 gpu batchnorm fp32 11
-57 gpu relu fp32 11
-58 gpu group_conv fp32 11
-59 gpu batchnorm fp32 11
-60 gpu relu fp32 11
-61 gpu conv fp32 11
-62 gpu batchnorm fp32 11
-63 gpu relu fp32 11
-64 gpu group_conv fp32 11
-65 gpu batchnorm fp32 11
-66 gpu relu fp32 11
-67 gpu conv fp32 11
-68 gpu batchnorm fp32 11
-69 gpu relu fp32 11
-70 gpu group_conv fp32 11
-71 gpu batchnorm fp32 11
-72 gpu relu fp32 11
-73 gpu conv fp32 11
-74 gpu batchnorm fp32 11
-75 gpu relu fp32 11
-76 gpu group_conv fp32 11
-77 gpu batchnorm fp32 11
-78 gpu relu fp32 11
-79 gpu conv fp32 11
-80 gpu batchnorm fp32 11
-81 gpu relu fp32 11
-82 gpu pool_mean fp32 11
-83 gpu mul fp32 11 add fp32 1
-84 gpu softmax fp32 1
------
-+++++
-conf2 1.504059255565631 1.4598468219902432 81.86 2.5600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf3 1.5040783418076804 1.459845395800413 81.86 2.5600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 152
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf4 1.5042737817275433 1.4598464522370567 81.74 2.680000000000007
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf5 1.5042737817275433 1.4598464522370567 81.74 2.680000000000007
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf6 1.5070383438802568 1.463241585164149 81.69999999999999 2.720000000000013
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf7 1.5070575058058588 1.463240152333617 81.58 2.8400000000000034
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 152
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf8 1.5039678813445672 1.4598454486222088 81.56 2.8599999999999994
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 152
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf9 1.5038655354281372 1.4599130636549171 81.46 2.960000000000008
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 161
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 152
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 152
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf10 1.4785375660713596 1.4280520288797043 84.42 0.0
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv fp16 12
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv fp16 12
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv fp16 12
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
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-73 gpu conv fp16 12
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv fp16 12
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet18_cifar10/resnet18_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet18_cifar10/resnet18_cifar10.txt
deleted file mode 100644
index d1d75a011e9ada7994dcd5a31ee5d56fc2ee3e2f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet18_cifar10/resnet18_cifar10.txt
+++ /dev/null
@@ -1,91 +0,0 @@
-2484.981244
-+++++
-conf1 1 1 89.42 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1
-3 gpu conv fp32 11 add fp32 1
-4 gpu add fp32 11
-5 gpu relu fp32 11
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1
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-9 gpu relu fp32 11
-10 gpu conv fp32 11 add fp32 1 relu fp32 1
-11 gpu conv fp32 11 add fp32 1
-12 gpu add fp32 11
-13 gpu relu fp32 11
-14 gpu conv fp32 11 add fp32 1 relu fp32 1
-15 gpu conv fp32 11 add fp32 1
-16 gpu conv fp32 11 add fp32 1
-17 gpu add fp32 11
-18 gpu relu fp32 11
-19 gpu conv fp32 11 add fp32 1 relu fp32 1
-20 gpu conv fp32 11 add fp32 1
-21 gpu add fp32 11
-22 gpu relu fp32 11
-23 gpu conv fp32 11 add fp32 1 relu fp32 1
-24 gpu conv fp32 11 add fp32 1
-25 gpu add fp32 11
-26 gpu relu fp32 11
-27 gpu conv fp32 11 add fp32 1 relu fp32 1
-28 gpu conv fp32 11 add fp32 1
-29 gpu conv fp32 11 add fp32 1
-30 gpu add fp32 11
-31 gpu relu fp32 11
-32 gpu conv fp32 11 add fp32 1 relu fp32 1
-33 gpu conv fp32 11 add fp32 1
-34 gpu add fp32 11
-35 gpu relu fp32 11
-36 gpu conv fp32 11 add fp32 1 relu fp32 1
-37 gpu conv fp32 11 add fp32 1
-38 gpu add fp32 11
-39 gpu relu fp32 11
-40 gpu pool_mean fp32 11
-41 gpu mul fp32 11 add fp32 1
-42 gpu softmax fp32 1
------
-+++++
-conf2 1.3617910209460897 1.3866827244386561 89.42 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1
-8 gpu add fp16 12
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-10 gpu conv fp16 12 add fp16 1 relu fp16 1
-11 gpu conv fp16 12 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 12 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12 add fp16 1 relu fp16 1
-20 gpu conv fp16 12 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv fp16 12 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 12 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv fp16 12 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet50_imagenet/resnet50_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet50_imagenet/resnet50_imagenet.txt
deleted file mode 100644
index a045011580adb912289364d35fb85668e74261e7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/resnet50_imagenet/resnet50_imagenet.txt
+++ /dev/null
@@ -1,1233 +0,0 @@
-7161.053769000008
-+++++
-conf1 1 1 75.7 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-2 gpu batchnorm fp32 11
-3 gpu conv fp32 11 add fp32 1
-4 gpu batchnorm fp32 11
-5 gpu relu fp32 11
-6 gpu conv fp32 11 add fp32 1
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-9 gpu conv fp32 11 add fp32 1
-10 gpu batchnorm fp32 11
-11 gpu conv fp32 11 add fp32 1
-12 gpu batchnorm fp32 11
-13 gpu add fp32 11
-14 gpu relu fp32 11
-15 gpu conv fp32 11 add fp32 1
-16 gpu batchnorm fp32 11
-17 gpu relu fp32 11
-18 gpu conv fp32 11 add fp32 1
-19 gpu batchnorm fp32 11
-20 gpu relu fp32 11
-21 gpu conv fp32 11 add fp32 1
-22 gpu batchnorm fp32 11
-23 gpu add fp32 11
-24 gpu relu fp32 11
-25 gpu conv fp32 11 add fp32 1
-26 gpu batchnorm fp32 11
-27 gpu relu fp32 11
-28 gpu conv fp32 11 add fp32 1
-29 gpu batchnorm fp32 11
-30 gpu relu fp32 11
-31 gpu conv fp32 11 add fp32 1
-32 gpu batchnorm fp32 11
-33 gpu add fp32 11
-34 gpu relu fp32 11
-35 gpu conv fp32 11 add fp32 1
-36 gpu batchnorm fp32 11
-37 gpu relu fp32 11
-38 gpu conv fp32 11 add fp32 1
-39 gpu batchnorm fp32 11
-40 gpu relu fp32 11
-41 gpu conv fp32 11 add fp32 1
-42 gpu batchnorm fp32 11
-43 gpu conv fp32 11 add fp32 1
-44 gpu batchnorm fp32 11
-45 gpu add fp32 11
-46 gpu relu fp32 11
-47 gpu conv fp32 11 add fp32 1
-48 gpu batchnorm fp32 11
-49 gpu relu fp32 11
-50 gpu conv fp32 11 add fp32 1
-51 gpu batchnorm fp32 11
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-53 gpu conv fp32 11 add fp32 1
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-56 gpu relu fp32 11
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-60 gpu conv fp32 11 add fp32 1
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-67 gpu conv fp32 11 add fp32 1
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-77 gpu conv fp32 11 add fp32 1
-78 gpu batchnorm fp32 11
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-80 gpu conv fp32 11 add fp32 1
-81 gpu batchnorm fp32 11
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-83 gpu conv fp32 11 add fp32 1
-84 gpu batchnorm fp32 11
-85 gpu conv fp32 11 add fp32 1
-86 gpu batchnorm fp32 11
-87 gpu add fp32 11
-88 gpu relu fp32 11
-89 gpu conv fp32 11 add fp32 1
-90 gpu batchnorm fp32 11
-91 gpu relu fp32 11
-92 gpu conv fp32 11 add fp32 1
-93 gpu batchnorm fp32 11
-94 gpu relu fp32 11
-95 gpu conv fp32 11 add fp32 1
-96 gpu batchnorm fp32 11
-97 gpu add fp32 11
-98 gpu relu fp32 11
-99 gpu conv fp32 11 add fp32 1
-100 gpu batchnorm fp32 11
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-102 gpu conv fp32 11 add fp32 1
-103 gpu batchnorm fp32 11
-104 gpu relu fp32 11
-105 gpu conv fp32 11 add fp32 1
-106 gpu batchnorm fp32 11
-107 gpu add fp32 11
-108 gpu relu fp32 11
-109 gpu conv fp32 11 add fp32 1
-110 gpu batchnorm fp32 11
-111 gpu relu fp32 11
-112 gpu conv fp32 11 add fp32 1
-113 gpu batchnorm fp32 11
-114 gpu relu fp32 11
-115 gpu conv fp32 11 add fp32 1
-116 gpu batchnorm fp32 11
-117 gpu add fp32 11
-118 gpu relu fp32 11
-119 gpu conv fp32 11 add fp32 1
-120 gpu batchnorm fp32 11
-121 gpu relu fp32 11
-122 gpu conv fp32 11 add fp32 1
-123 gpu batchnorm fp32 11
-124 gpu relu fp32 11
-125 gpu conv fp32 11 add fp32 1
-126 gpu batchnorm fp32 11
-127 gpu add fp32 11
-128 gpu relu fp32 11
-129 gpu conv fp32 11 add fp32 1
-130 gpu batchnorm fp32 11
-131 gpu relu fp32 11
-132 gpu conv fp32 11 add fp32 1
-133 gpu batchnorm fp32 11
-134 gpu relu fp32 11
-135 gpu conv fp32 11 add fp32 1
-136 gpu batchnorm fp32 11
-137 gpu add fp32 11
-138 gpu relu fp32 11
-139 gpu conv fp32 11 add fp32 1
-140 gpu batchnorm fp32 11
-141 gpu relu fp32 11
-142 gpu conv fp32 11 add fp32 1
-143 gpu batchnorm fp32 11
-144 gpu relu fp32 11
-145 gpu conv fp32 11 add fp32 1
-146 gpu batchnorm fp32 11
-147 gpu conv fp32 11 add fp32 1
-148 gpu batchnorm fp32 11
-149 gpu add fp32 11
-150 gpu relu fp32 11
-151 gpu conv fp32 11 add fp32 1
-152 gpu batchnorm fp32 11
-153 gpu relu fp32 11
-154 gpu conv fp32 11 add fp32 1
-155 gpu batchnorm fp32 11
-156 gpu relu fp32 11
-157 gpu conv fp32 11 add fp32 1
-158 gpu batchnorm fp32 11
-159 gpu add fp32 11
-160 gpu relu fp32 11
-161 gpu conv fp32 11 add fp32 1
-162 gpu batchnorm fp32 11
-163 gpu relu fp32 11
-164 gpu conv fp32 11 add fp32 1
-165 gpu batchnorm fp32 11
-166 gpu relu fp32 11
-167 gpu conv fp32 11 add fp32 1
-168 gpu batchnorm fp32 11
-169 gpu add fp32 11
-170 gpu relu fp32 11
-171 gpu pool_max fp32 11
-172 gpu mul fp32 11 add fp32 1
-173 gpu softmax fp32 1
------
-+++++
-conf2 1.8254789092281507 1.4527803526239977 75.7 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
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-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
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-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
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-99 gpu conv fp16 12 add fp16 1
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-102 gpu conv fp16 12 add fp16 1
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-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
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-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
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-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
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-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf3 1.8521749055745271 1.472492365706726 75.02 0.6800000000000068
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
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-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv perf_fp16 160 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 11 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
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-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
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-71 gpu batchnorm fp16 12
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-74 gpu batchnorm fp16 12
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-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
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-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
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-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
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-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 11 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv perf_fp16 164 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf4 1.8509087142956673 1.4713858340895483 74.68 1.019999999999996
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv perf_fp16 160 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv fp16 12 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf5 1.8538077719438253 1.4749308494814874 73.82 1.8800000000000097
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv perf_fp16 160 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 11 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv perf_fp16 153 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 11 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv perf_fp16 164 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv samp_fp16 268 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 11 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 11 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf6 1.8538077719438253 1.4749308494814874 73.7 2.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv perf_fp16 160 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 11 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv perf_fp16 153 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 11 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv perf_fp16 164 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv samp_fp16 268 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf7 1.8577902325643394 1.478552049679054 72.82 2.8800000000000097
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv perf_fp16 160 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 11 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 11 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv samp_fp16 268 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 11 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv perf_fp16 164 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv samp_fp16 268 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv perf_fp16 158 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 11 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar10/vgg16_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar10/vgg16_cifar10.txt
deleted file mode 100644
index f4e185f358dbd2282b14c0865d829903d2d270e9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar10/vgg16_cifar10.txt
+++ /dev/null
@@ -1,58 +0,0 @@
-3776.508929999999
-+++++
-conf1 1 1 89.96 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.4192803184847484 2.2393153800931898 89.22 0.7399999999999949
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf3 2.1240075032467187 1.9749367321301132 88.64 1.3199999999999932
-1 gpu conv fp16 11 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar100/vgg16_cifar100.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar100/vgg16_cifar100.txt
deleted file mode 100644
index b55bb668b140ebcc9ee911f728726afed7274f85..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_cifar100/vgg16_cifar100.txt
+++ /dev/null
@@ -1,77 +0,0 @@
-3768.819777999999
-+++++
-conf1 1 1 66.5 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.2793321208062913 2.0502797911533945 66.42 0.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 267 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf3 2.2793321208062913 2.0502797911533945 66.42 0.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 267 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf4 2.664296720624579 2.427276363573644 64.7 1.7999999999999972
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_imagenet/vgg16_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_imagenet/vgg16_imagenet.txt
deleted file mode 100644
index d0a23ffb10367c45ab76e4477f29932a5431e68b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/empirical/vgg16_imagenet/vgg16_imagenet.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-19194.623482
-+++++
-conf1 1 1 72.84 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1 relu fp32 1
-16 gpu mul fp32 11 add fp32 1
-17 gpu softmax fp32 1
------
-+++++
-conf2 1.7719381411481732 1.5850925672384186 72.84 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet2_cifar10/alexnet2_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet2_cifar10/alexnet2_cifar10.txt
deleted file mode 100644
index 6ec4a06d3dbd2e088d6db287d23dd3bd5aad7ddb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet2_cifar10/alexnet2_cifar10.txt
+++ /dev/null
@@ -1,419 +0,0 @@
-1114.3009809999999
-+++++
-conf1 1 1 84.98 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1
-6 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-7 gpu mul fp32 11 add fp32 1
-8 gpu softmax fp32 1
------
-+++++
-conf2 2.4248748377353113 2.0815908534183163 84.5 0.480000000000004
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf3 2.4055188425519614 2.0586265720811823 84.48 0.5
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf4 2.4156140842962985 2.0617867479342706 84.28 0.7000000000000028
-1 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 163 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf5 2.396416918342732 2.0506214971794585 84.02 0.960000000000008
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf6 2.463002582910052 2.1171077568609458 83.84 1.1400000000000006
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 167 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf7 2.360283215266004 2.0255245321874304 83.78 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf8 2.4140791541736157 2.0671513522247653 83.74000000000001 1.2399999999999949
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf9 2.457753689612079 2.1086250651240137 83.7 1.2800000000000011
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 163 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf10 2.459170454055443 2.1111925341396343 83.7 1.2800000000000011
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 164 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf11 2.4135986141645764 2.060453960420927 83.62 1.3599999999999994
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf12 2.4631278039012106 2.1092094797926637 83.58 1.4000000000000057
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf13 2.535761391794481 2.16998336112692 83.58 1.4000000000000057
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf14 2.289006193945062 1.961240158652051 83.54 1.4399999999999977
-1 gpu conv perf_fp16 167 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf15 2.4257674844112573 2.0808440756495563 83.5 1.480000000000004
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 161 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf16 2.458122368488622 2.109531159729078 83.48 1.5
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf17 2.281072202152105 1.9539314420536427 83.46000000000001 1.519999999999996
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf18 2.4572171342078444 2.1088933553775697 83.46000000000001 1.519999999999996
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 163 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf19 2.3017607719030058 1.9782265708150768 83.42 1.5600000000000023
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf20 2.379206814483014 2.047909200292713 83.39999999999999 1.5800000000000125
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 151 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf21 2.4636282705302537 2.1162281156388527 83.39999999999999 1.5800000000000125
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf22 2.461590101374146 2.1108493881199184 83.22 1.7600000000000051
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 161 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf23 2.537054645442804 2.167568834938183 83.22 1.7600000000000051
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf24 2.4631604723407885 2.1099694757102845 83.17999999999999 1.8000000000000114
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf25 2.4636282705302537 2.1162281156388527 83.14 1.8400000000000034
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf26 2.462588899729088 2.109477918791931 83.14 1.8400000000000034
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf27 2.4638085754689025 2.1071960926343603 83.1 1.8800000000000097
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf28 2.4640079766123635 2.110326453157297 83.08 1.9000000000000057
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf29 2.459337622764853 2.107249218450713 83.06 1.9200000000000017
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf30 2.538176340059405 2.173287257415721 83.02000000000001 1.9599999999999937
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 164 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf31 2.3905426931959846 2.044333576277581 83.02000000000001 1.9599999999999937
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf32 2.459337622764853 2.107249218450713 83.0 1.980000000000004
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf33 2.458968579288317 2.1063450826631396 82.89999999999999 2.0800000000000125
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 163 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf34 2.2912974651603877 1.9670210508860688 82.8 2.180000000000007
-1 gpu conv perf_fp16 168 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf35 2.4648489763056327 2.113931670664391 82.66 2.3200000000000074
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf36 2.4599076869402854 2.1077397371200193 82.6 2.3800000000000097
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf37 2.4636282705302537 2.1162281156388527 82.54 2.4399999999999977
-1 gpu conv fp16 11 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 160 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
-+++++
-conf38 2.591814267389778 2.222680944458784 82.26 2.719999999999999
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1
-2 gpu conv perf_fp16 154 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1
-6 gpu conv perf_fp16 157 add fp16 1 tanh fp16 1 pool_max fp16 1
-7 gpu mul fp16 12 add fp16 1
-8 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_cifar10/alexnet_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_cifar10/alexnet_cifar10.txt
deleted file mode 100644
index a9ccba6eb63f620c0e3b6f95fd7c50892018f00f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_cifar10/alexnet_cifar10.txt
+++ /dev/null
@@ -1,511 +0,0 @@
-2592.187221
-+++++
-conf1 1 1 79.28 0.0
-1 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-2 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 tanh fp32 1
-4 gpu conv fp32 11 add fp32 1 tanh fp32 1
-5 gpu conv fp32 11 add fp32 1 tanh fp32 1 pool_max fp32 1
-6 gpu mul fp32 11 add fp32 1
-7 gpu softmax fp32 1
------
-+++++
-conf2 1.7593976485873195 1.6193399031642917 79.23 0.04999999999999716
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf3 2.092625440752526 1.9139078015388271 78.96 0.3200000000000074
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf4 1.8870195448805414 1.7296919053025768 78.8 0.480000000000004
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf5 2.1184804041774554 1.9598989563949536 78.75999999999999 0.5200000000000102
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf6 2.1184804041774554 1.9598989563949536 78.75999999999999 0.5200000000000102
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf7 2.0933825381386364 1.9150743378318535 78.64 0.6400000000000006
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf8 2.081712090729918 1.9102226906341664 78.5 0.7800000000000011
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf9 2.081712090729918 1.9102226906341664 78.5 0.7800000000000011
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf10 2.2662606588487595 2.066560750795139 78.48 0.7999999999999972
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf11 2.121684761285686 1.966318179285323 78.48 0.7999999999999972
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf12 2.3417491169395532 2.1355030360671465 78.38000000000001 0.8999999999999915
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf13 2.2247938983110425 2.060416584958474 78.38000000000001 0.8999999999999915
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf14 2.2247938983110425 2.060416584958474 78.38000000000001 0.8999999999999915
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf15 2.2247938983110425 2.060416584958474 78.38000000000001 0.8999999999999915
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf16 2.2627828537139263 2.065683616898884 78.32000000000001 0.9599999999999937
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf17 2.2627828537139263 2.065683616898884 78.32000000000001 0.9599999999999937
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf18 2.2627828537139263 2.065683616898884 78.32000000000001 0.9599999999999937
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf19 2.146571989407323 1.95711703610764 78.18 1.0999999999999943
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf20 2.303316973793268 2.1036463961913276 78.10000000000001 1.1799999999999926
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf21 2.436875653706139 2.2434837737118056 78.08 1.2000000000000028
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf22 2.436875653706139 2.2434837737118056 78.08 1.2000000000000028
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf23 2.436875653706139 2.2434837737118056 78.08 1.2000000000000028
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf24 2.1106508925330925 1.9419233584234938 78.06 1.2199999999999989
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf25 2.3203534290038634 2.116965679235447 78.06 1.2199999999999989
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf26 2.3527290658539215 2.145832257234814 78.03999999999999 1.240000000000009
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf27 2.3527290658539215 2.145832257234814 78.03999999999999 1.240000000000009
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv fp16 12 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf28 2.432854949808342 2.2424500615508003 78.0 1.2800000000000011
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf29 2.432854949808342 2.2424500615508003 78.0 1.2800000000000011
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf30 2.432854949808342 2.2424500615508003 78.0 1.2800000000000011
-1 gpu conv samp_fp16 263 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf31 2.3137982135449207 2.1281257317083417 77.84 1.4399999999999977
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 265 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf32 2.1198074418988333 1.9522214255218437 77.82 1.460000000000008
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf33 2.246924974355375 2.065289762405701 77.8 1.480000000000004
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 269 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf34 2.263614734554485 2.090777846534249 77.74 1.5400000000000063
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf35 2.263614734554485 2.090777846534249 77.74 1.5400000000000063
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf36 2.263614734554485 2.090777846534249 77.74 1.5400000000000063
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf37 2.5289288699015304 2.334007588396142 77.72 1.5600000000000023
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf38 2.5289288699015304 2.334007588396142 77.72 1.5600000000000023
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf39 2.3117594882585775 2.1152397180868943 77.56 1.7199999999999989
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf40 2.452732477854469 2.264573687601476 77.56 1.7199999999999989
-1 gpu conv perf_fp16 167 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf41 2.452732477854469 2.264573687601476 77.56 1.7199999999999989
-1 gpu conv perf_fp16 167 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf42 2.382518688546389 2.178614303992064 77.5 1.7800000000000011
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf43 2.382518688546389 2.178614303992064 77.5 1.7800000000000011
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf44 2.3900667100485924 2.188128526401265 77.48 1.7999999999999972
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf45 2.3900667100485924 2.188128526401265 77.48 1.7999999999999972
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf46 2.3900667100485924 2.188128526401265 77.48 1.7999999999999972
-1 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf47 2.4835281673276515 2.279527076032239 77.3 1.980000000000004
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf48 2.4835281673276515 2.279527076032239 77.3 1.980000000000004
-1 gpu conv samp_fp16 264 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf49 2.1553694968551302 1.9959124044028933 77.18 2.0999999999999943
-1 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 265 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 268 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf50 2.5877520959724816 2.3763616521050364 77.03999999999999 2.240000000000009
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
-+++++
-conf51 2.5877520959724816 2.3763616521050364 77.03999999999999 2.240000000000009
-1 gpu conv samp_fp16 261 add fp16 1 tanh fp16 1 pool_max fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 267 add fp16 1 tanh fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 tanh fp16 1
-5 gpu conv fp16 12 add fp16 1 tanh fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1
-7 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_imagenet/alexnet_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_imagenet/alexnet_imagenet.txt
deleted file mode 100644
index b0e42a5aaa5d7b5a06b6422a5c33a0047b6eff8d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/alexnet_imagenet/alexnet_imagenet.txt
+++ /dev/null
@@ -1,229 +0,0 @@
-2739.950736
-+++++
-conf1 1 1 56.3 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-6 gpu mul fp32 11 add fp32 1 relu fp32 1
-7 gpu mul fp32 11 add fp32 1 relu fp32 1
-8 gpu mul fp32 11 add fp32 1
-9 gpu softmax fp32 1
------
-+++++
-conf2 1.802133644103582 1.8186433204507424 55.76 0.5399999999999991
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf3 1.7574572103878898 1.7673706184460103 55.58 0.7199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf4 2.0227701930718065 2.043112495268932 55.42 0.8799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf5 1.9872634777043927 2.002789650227035 55.120000000000005 1.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf6 1.8204253918445088 1.843736069756362 54.84 1.4599999999999937
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 154 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf7 1.9308336510645352 1.934889049414224 54.74 1.5599999999999952
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 168 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf8 2.0146435217865446 2.0367475358800102 54.58 1.7199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf9 2.0101709494490696 2.0329911158023064 54.400000000000006 1.8999999999999915
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf10 2.0052132441967916 2.0284931705407003 54.300000000000004 1.999999999999993
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 168 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf11 2.010827434817262 2.036001862538864 54.2 2.0999999999999943
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 154 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf12 2.019868378233057 2.0433540129730265 54.17999999999999 2.1200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf13 1.9923471030291253 2.009177323959059 54.120000000000005 2.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf14 1.9923471030291253 2.009177323959059 54.120000000000005 2.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf15 2.028037341700216 2.049760395549724 54.0 2.299999999999997
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf16 1.9910730364852436 2.006510848093771 53.54 2.759999999999998
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf17 2.1567475543719614 2.159142310265706 53.300000000000004 2.999999999999993
-1 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf18 2.1567475543719614 2.159142310265706 53.300000000000004 2.999999999999993
-1 gpu conv perf_fp16 164 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 166 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
-+++++
-conf19 2.0232690820426464 2.0527698121318476 53.300000000000004 2.999999999999993
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu conv perf_fp16 168 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 11 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-5 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-6 gpu mul fp16 12 add fp16 1 relu fp16 1
-7 gpu mul fp16 12 add fp16 1 relu fp16 1
-8 gpu mul fp16 12 add fp16 1
-9 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/lenet_keras/lenet_keras.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/lenet_keras/lenet_keras.txt
deleted file mode 100644
index b4e51dff426f4d3c5cb7b9572e6aa5940212acbd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/lenet_keras/lenet_keras.txt
+++ /dev/null
@@ -1,409 +0,0 @@
-282.5141369999999
-+++++
-conf1 1 1 98.7 0.0
-1 gpu conv fp32 11 add fp32 1 pool_max fp32 1 tanh fp32 1
-2 gpu conv fp32 11 add fp32 1 pool_max fp32 1 tanh fp32 1
-3 gpu mul fp32 11 add fp32 1 tanh fp32 1
-4 gpu mul fp32 11 add fp32 1 tanh fp32 1
-5 gpu softmax fp32 1
------
-+++++
-conf2 1.828613181003043 2.071721708828981 98.65 0.04999999999999716
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf3 1.8936889628815377 2.139779619692146 98.65 0.04999999999999716
-1 gpu conv perf_fp16 152 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf4 1.8936889628815377 2.139779619692146 98.65 0.04999999999999716
-1 gpu conv perf_fp16 152 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf5 1.8936889628815377 2.139779619692146 98.65 0.04999999999999716
-1 gpu conv perf_fp16 152 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf6 1.8247639611533713 2.0227145446958756 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf7 1.8247639611533713 2.0227145446958756 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf8 1.8406161850501603 2.037849502542524 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf9 1.8406161850501603 2.037849502542524 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf10 1.8406161850501603 2.037849502542524 98.64 0.060000000000002274
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf11 1.8663357888260776 2.115790921611576 98.64 0.060000000000002274
-1 gpu conv perf_fp16 155 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf12 1.8663357888260776 2.115790921611576 98.64 0.060000000000002274
-1 gpu conv perf_fp16 155 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf13 1.8663357888260776 2.115790921611576 98.64 0.060000000000002274
-1 gpu conv perf_fp16 155 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf14 1.8645645142051612 2.1037012333044935 98.61999999999999 0.0800000000000125
-1 gpu conv perf_fp16 167 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf15 1.8645645142051612 2.1037012333044935 98.61999999999999 0.0800000000000125
-1 gpu conv perf_fp16 167 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf16 1.8645645142051612 2.1037012333044935 98.61999999999999 0.0800000000000125
-1 gpu conv perf_fp16 167 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf17 2.2168527051833635 2.453341076720038 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf18 2.2168527051833635 2.453341076720038 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf19 2.2168527051833635 2.453341076720038 98.61999999999999 0.0800000000000125
-1 gpu conv samp_fp16 264 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf20 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf21 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf22 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 12 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf23 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf24 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf25 1.8406161850501603 2.037849502542524 98.6 0.10000000000000853
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf26 2.200653361151419 2.425091789360736 98.6 0.10000000000000853
-1 gpu conv samp_fp16 266 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf27 2.200653361151419 2.425091789360736 98.6 0.10000000000000853
-1 gpu conv samp_fp16 266 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf28 1.8406161850501603 2.037849502542524 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf29 1.8406161850501603 2.037849502542524 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf30 1.8406161850501603 2.037849502542524 98.58 0.12000000000000455
-1 gpu conv fp16 11 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf31 1.8445326456180258 2.087601822059355 98.58 0.12000000000000455
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf32 1.8445326456180258 2.087601822059355 98.58 0.12000000000000455
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf33 1.8445326456180258 2.087601822059355 98.58 0.12000000000000455
-1 gpu conv perf_fp16 156 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf34 1.8916677984300285 2.155437579874673 98.58 0.12000000000000455
-1 gpu conv perf_fp16 158 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf35 1.8916677984300285 2.155437579874673 98.58 0.12000000000000455
-1 gpu conv perf_fp16 158 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf36 1.8916677984300285 2.155437579874673 98.58 0.12000000000000455
-1 gpu conv perf_fp16 158 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf37 1.8649226857257986 2.1076025277601325 98.56 0.14000000000000057
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf38 1.8649226857257986 2.1076025277601325 98.56 0.14000000000000057
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf39 1.8649226857257986 2.1076025277601325 98.56 0.14000000000000057
-1 gpu conv perf_fp16 168 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf40 1.8463058650555446 2.067271423078985 98.56 0.14000000000000057
-1 gpu conv perf_fp16 157 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf41 1.8463058650555446 2.067271423078985 98.56 0.14000000000000057
-1 gpu conv perf_fp16 157 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf42 1.8463058650555446 2.067271423078985 98.56 0.14000000000000057
-1 gpu conv perf_fp16 157 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf43 1.9234076467497994 2.1864740913112275 98.56 0.14000000000000057
-1 gpu conv perf_fp16 153 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf44 1.9234076467497994 2.1864740913112275 98.56 0.14000000000000057
-1 gpu conv perf_fp16 153 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf45 1.9234076467497994 2.1864740913112275 98.56 0.14000000000000057
-1 gpu conv perf_fp16 153 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf46 1.8698191484268973 2.13979218727595 98.54 0.1599999999999966
-1 gpu conv perf_fp16 159 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf47 1.8698191484268973 2.13979218727595 98.54 0.1599999999999966
-1 gpu conv perf_fp16 159 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf48 1.8575043605938137 2.092057786757256 98.52 0.18000000000000682
-1 gpu conv perf_fp16 165 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf49 1.8575043605938137 2.092057786757256 98.52 0.18000000000000682
-1 gpu conv perf_fp16 165 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf50 1.8575043605938137 2.092057786757256 98.52 0.18000000000000682
-1 gpu conv perf_fp16 165 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
-+++++
-conf51 1.8534621507951072 2.1231113105788597 98.44000000000001 0.2599999999999909
-1 gpu conv perf_fp16 159 add fp16 1 pool_max fp16 1 tanh fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 pool_max fp16 1 tanh fp16 1
-3 gpu mul fp16 12 add fp16 1 tanh fp16 1
-4 gpu mul fp16 12 add fp16 1 tanh fp16 1
-5 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/mobilenet_cifar10/mobilenet_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/mobilenet_cifar10/mobilenet_cifar10.txt
deleted file mode 100644
index b4d8bd893c8d9395fce6a3484d75f543f1e72da2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/mobilenet_cifar10/mobilenet_cifar10.txt
+++ /dev/null
@@ -1,3220 +0,0 @@
-4077.307063200001
-+++++
-conf1 1 1 84.42 0.0
-1 gpu conv fp32 11
-2 gpu batchnorm fp32 11
-3 gpu relu fp32 11
-4 gpu group_conv fp32 11
-5 gpu batchnorm fp32 11
-6 gpu relu fp32 11
-7 gpu conv fp32 11
-8 gpu batchnorm fp32 11
-9 gpu relu fp32 11
-10 gpu group_conv fp32 11
-11 gpu batchnorm fp32 11
-12 gpu relu fp32 11
-13 gpu conv fp32 11
-14 gpu batchnorm fp32 11
-15 gpu relu fp32 11
-16 gpu group_conv fp32 11
-17 gpu batchnorm fp32 11
-18 gpu relu fp32 11
-19 gpu conv fp32 11
-20 gpu batchnorm fp32 11
-21 gpu relu fp32 11
-22 gpu group_conv fp32 11
-23 gpu batchnorm fp32 11
-24 gpu relu fp32 11
-25 gpu conv fp32 11
-26 gpu batchnorm fp32 11
-27 gpu relu fp32 11
-28 gpu group_conv fp32 11
-29 gpu batchnorm fp32 11
-30 gpu relu fp32 11
-31 gpu conv fp32 11
-32 gpu batchnorm fp32 11
-33 gpu relu fp32 11
-34 gpu group_conv fp32 11
-35 gpu batchnorm fp32 11
-36 gpu relu fp32 11
-37 gpu conv fp32 11
-38 gpu batchnorm fp32 11
-39 gpu relu fp32 11
-40 gpu group_conv fp32 11
-41 gpu batchnorm fp32 11
-42 gpu relu fp32 11
-43 gpu conv fp32 11
-44 gpu batchnorm fp32 11
-45 gpu relu fp32 11
-46 gpu group_conv fp32 11
-47 gpu batchnorm fp32 11
-48 gpu relu fp32 11
-49 gpu conv fp32 11
-50 gpu batchnorm fp32 11
-51 gpu relu fp32 11
-52 gpu group_conv fp32 11
-53 gpu batchnorm fp32 11
-54 gpu relu fp32 11
-55 gpu conv fp32 11
-56 gpu batchnorm fp32 11
-57 gpu relu fp32 11
-58 gpu group_conv fp32 11
-59 gpu batchnorm fp32 11
-60 gpu relu fp32 11
-61 gpu conv fp32 11
-62 gpu batchnorm fp32 11
-63 gpu relu fp32 11
-64 gpu group_conv fp32 11
-65 gpu batchnorm fp32 11
-66 gpu relu fp32 11
-67 gpu conv fp32 11
-68 gpu batchnorm fp32 11
-69 gpu relu fp32 11
-70 gpu group_conv fp32 11
-71 gpu batchnorm fp32 11
-72 gpu relu fp32 11
-73 gpu conv fp32 11
-74 gpu batchnorm fp32 11
-75 gpu relu fp32 11
-76 gpu group_conv fp32 11
-77 gpu batchnorm fp32 11
-78 gpu relu fp32 11
-79 gpu conv fp32 11
-80 gpu batchnorm fp32 11
-81 gpu relu fp32 11
-82 gpu pool_mean fp32 11
-83 gpu mul fp32 11 add fp32 1
-84 gpu softmax fp32 1
------
-+++++
-conf2 1.4930855091460031 1.447990050940341 83.72 0.7000000000000028
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
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-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv fp16 12
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf3 1.493397883226807 1.449591062426989 83.72 0.7000000000000028
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
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-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
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-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
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-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
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-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
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-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
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-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
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-73 gpu conv perf_fp16 151
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-76 gpu group_conv fp16 12
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-79 gpu conv perf_fp16 163
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf4 1.4934429016801338 1.4500582352111675 83.72 0.7000000000000028
-1 gpu conv fp16 12
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-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
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-10 gpu group_conv fp16 12
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-13 gpu conv fp16 12
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-16 gpu group_conv fp16 12
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-19 gpu conv fp16 12
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-22 gpu group_conv fp16 12
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-25 gpu conv fp16 12
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-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
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-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
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-37 gpu conv fp16 12
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-40 gpu group_conv fp16 12
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-46 gpu group_conv fp16 12
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-49 gpu conv perf_fp16 155
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-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
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-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
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-58 gpu group_conv fp16 12
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-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
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-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
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-70 gpu group_conv fp16 12
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-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
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-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
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-79 gpu conv perf_fp16 168
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf5 1.4938214813031556 1.450038222978811 83.72 0.7000000000000028
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
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-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
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-13 gpu conv fp16 12
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-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
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-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
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-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 157
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf6 1.4933879828131855 1.449975636202813 83.72 0.7000000000000028
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
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-8 gpu batchnorm fp16 12
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-10 gpu group_conv fp16 12
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-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
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-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
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-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
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-58 gpu group_conv fp16 12
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-61 gpu conv perf_fp16 151
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-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
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-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 160
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf7 1.492663093331302 1.4487067754520524 83.7 0.7199999999999989
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 167
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf8 1.495724395088184 1.4507925552157772 83.56 0.8599999999999994
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 162
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf9 1.496506307637598 1.4521705950285135 83.36 1.0600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 162
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf10 1.496532672928805 1.4521696542076958 83.36 1.0600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 156
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf11 1.4988418058849937 1.4555327556053628 83.28 1.1400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 164
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf12 1.4994289979945077 1.4562439330251535 83.28 1.1400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf13 1.4952028793065038 1.450369851058777 83.14 1.2800000000000011
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 162
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf14 1.4933978285280285 1.448265686258097 83.12 1.2999999999999972
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf15 1.491958833559989 1.4459262032919467 83.08 1.3400000000000034
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 157
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf16 1.4937317297990984 1.4498121856525021 83.02000000000001 1.3999999999999915
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf17 1.4963413808686974 1.4522391736954623 82.86 1.5600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 165
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf18 1.4942172827099065 1.4504631324933321 82.86 1.5600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 157
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf19 1.4963964073376739 1.4525461321361477 82.86 1.5600000000000023
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf20 1.4932583049858652 1.4472547227714012 82.84 1.5799999999999983
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv samp_fp16 266
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf21 1.4964326545281064 1.4526263046333605 82.82000000000001 1.5999999999999943
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf22 1.4966042483929347 1.4527859961226985 82.82000000000001 1.5999999999999943
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf23 1.4966008974318024 1.4527415844509437 82.78 1.6400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 155
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf24 1.4932738366973777 1.448820445466833 82.64 1.7800000000000011
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 164
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 157
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf25 1.4940402684133964 1.447332235394843 82.48 1.9399999999999977
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv samp_fp16 261
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf26 1.4981764588414919 1.4530714150549078 82.39999999999999 2.0200000000000102
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 161
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf27 1.5004334658773033 1.4549115105608688 82.3 2.1200000000000045
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 156
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf28 1.5006808163336343 1.4553824345285296 82.3 2.1200000000000045
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 156
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf29 1.4999870719460484 1.4571625511374704 82.28 2.1400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 165
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf30 1.500042366879961 1.4574715946270216 82.28 2.1400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf31 1.500214789632402 1.4576323532660131 82.28 2.1400000000000006
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
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-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
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-22 gpu group_conv fp16 12
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-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
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-28 gpu group_conv fp16 12
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-30 gpu relu fp16 12
-31 gpu conv perf_fp16 163
-32 gpu batchnorm fp16 12
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-34 gpu group_conv fp16 12
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-40 gpu group_conv fp16 12
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-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
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-52 gpu group_conv fp16 12
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-54 gpu relu fp16 12
-55 gpu conv perf_fp16 164
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
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-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
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-64 gpu group_conv fp16 12
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-67 gpu conv perf_fp16 152
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-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 151
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
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-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf32 1.4927009086066445 1.4484049211953174 82.26 2.1599999999999966
-1 gpu conv fp16 12
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-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
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-16 gpu group_conv fp16 12
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-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
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-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 164
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
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-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
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-54 gpu relu fp16 12
-55 gpu conv perf_fp16 161
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-58 gpu group_conv fp16 12
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-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
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-67 gpu conv perf_fp16 156
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-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
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-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
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-76 gpu group_conv fp16 12
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-78 gpu relu fp16 12
-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf33 1.5003438014588875 1.4538240352408085 82.22 2.200000000000003
-1 gpu conv fp16 12
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-6 gpu relu fp16 12
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-16 gpu group_conv fp16 12
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-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
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-22 gpu group_conv fp16 12
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-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
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-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
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-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
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-67 gpu conv fp16 12
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
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-79 gpu conv perf_fp16 152
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf34 1.5041587978616728 1.4610492456195174 82.02000000000001 2.3999999999999915
-1 gpu conv fp16 12
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-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 161
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
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-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 152
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
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-76 gpu group_conv fp16 12
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-78 gpu relu fp16 12
-79 gpu conv perf_fp16 158
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf35 1.5000040131742656 1.4555601139156464 81.88 2.5400000000000063
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv fp16 12
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv perf_fp16 152
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 12
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
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-42 gpu relu fp16 12
-43 gpu conv perf_fp16 161
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 151
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
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-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
-63 gpu relu fp16 12
-64 gpu group_conv fp16 12
-65 gpu batchnorm fp16 12
-66 gpu relu fp16 12
-67 gpu conv perf_fp16 151
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 167
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf36 1.4950571524902583 1.451478376045808 81.84 2.5799999999999983
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
-8 gpu batchnorm fp16 12
-9 gpu relu fp16 12
-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 164
-20 gpu batchnorm fp16 12
-21 gpu relu fp16 12
-22 gpu group_conv fp16 12
-23 gpu batchnorm fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv perf_fp16 161
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
-41 gpu batchnorm fp16 12
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-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 161
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
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-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
-56 gpu batchnorm fp16 12
-57 gpu relu fp16 12
-58 gpu group_conv fp16 12
-59 gpu batchnorm fp16 12
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-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
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-64 gpu group_conv fp16 12
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-67 gpu conv perf_fp16 155
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-69 gpu relu fp16 12
-70 gpu group_conv fp16 12
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
-74 gpu batchnorm fp16 12
-75 gpu relu fp16 12
-76 gpu group_conv fp16 12
-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
-+++++
-conf37 1.4975271575548847 1.4532126224638244 81.44 2.980000000000004
-1 gpu conv fp16 12
-2 gpu batchnorm fp16 12
-3 gpu relu fp16 12
-4 gpu group_conv fp16 12
-5 gpu batchnorm fp16 12
-6 gpu relu fp16 12
-7 gpu conv fp16 12
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-10 gpu group_conv fp16 12
-11 gpu batchnorm fp16 12
-12 gpu relu fp16 12
-13 gpu conv fp16 12
-14 gpu batchnorm fp16 12
-15 gpu relu fp16 12
-16 gpu group_conv fp16 12
-17 gpu batchnorm fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 164
-20 gpu batchnorm fp16 12
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-22 gpu group_conv fp16 12
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-24 gpu relu fp16 12
-25 gpu conv fp16 12
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu group_conv fp16 12
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-30 gpu relu fp16 12
-31 gpu conv fp16 12
-32 gpu batchnorm fp16 12
-33 gpu relu fp16 12
-34 gpu group_conv fp16 12
-35 gpu batchnorm fp16 12
-36 gpu relu fp16 12
-37 gpu conv fp16 11
-38 gpu batchnorm fp16 12
-39 gpu relu fp16 12
-40 gpu group_conv fp16 12
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-43 gpu conv fp16 12
-44 gpu batchnorm fp16 12
-45 gpu relu fp16 12
-46 gpu group_conv fp16 12
-47 gpu batchnorm fp16 12
-48 gpu relu fp16 12
-49 gpu conv perf_fp16 155
-50 gpu batchnorm fp16 12
-51 gpu relu fp16 12
-52 gpu group_conv fp16 12
-53 gpu batchnorm fp16 12
-54 gpu relu fp16 12
-55 gpu conv perf_fp16 155
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-58 gpu group_conv fp16 12
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-60 gpu relu fp16 12
-61 gpu conv perf_fp16 151
-62 gpu batchnorm fp16 12
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-67 gpu conv perf_fp16 155
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-70 gpu group_conv fp16 12
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-72 gpu relu fp16 12
-73 gpu conv perf_fp16 152
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-77 gpu batchnorm fp16 12
-78 gpu relu fp16 12
-79 gpu conv perf_fp16 153
-80 gpu batchnorm fp16 12
-81 gpu relu fp16 12
-82 gpu pool_mean fp16 12
-83 gpu mul fp16 12 add fp16 1
-84 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet18_cifar10/resnet18_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet18_cifar10/resnet18_cifar10.txt
deleted file mode 100644
index 654cffbf632686dca6310a93ecf56b6521e32039..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet18_cifar10/resnet18_cifar10.txt
+++ /dev/null
@@ -1,2296 +0,0 @@
-2484.981244
-+++++
-conf1 1 1 89.56 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1
-3 gpu conv fp32 11 add fp32 1
-4 gpu add fp32 11
-5 gpu relu fp32 11
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1
-8 gpu add fp32 11
-9 gpu relu fp32 11
-10 gpu conv fp32 11 add fp32 1 relu fp32 1
-11 gpu conv fp32 11 add fp32 1
-12 gpu add fp32 11
-13 gpu relu fp32 11
-14 gpu conv fp32 11 add fp32 1 relu fp32 1
-15 gpu conv fp32 11 add fp32 1
-16 gpu conv fp32 11 add fp32 1
-17 gpu add fp32 11
-18 gpu relu fp32 11
-19 gpu conv fp32 11 add fp32 1 relu fp32 1
-20 gpu conv fp32 11 add fp32 1
-21 gpu add fp32 11
-22 gpu relu fp32 11
-23 gpu conv fp32 11 add fp32 1 relu fp32 1
-24 gpu conv fp32 11 add fp32 1
-25 gpu add fp32 11
-26 gpu relu fp32 11
-27 gpu conv fp32 11 add fp32 1 relu fp32 1
-28 gpu conv fp32 11 add fp32 1
-29 gpu conv fp32 11 add fp32 1
-30 gpu add fp32 11
-31 gpu relu fp32 11
-32 gpu conv fp32 11 add fp32 1 relu fp32 1
-33 gpu conv fp32 11 add fp32 1
-34 gpu add fp32 11
-35 gpu relu fp32 11
-36 gpu conv fp32 11 add fp32 1 relu fp32 1
-37 gpu conv fp32 11 add fp32 1
-38 gpu add fp32 11
-39 gpu relu fp32 11
-40 gpu pool_mean fp32 11
-41 gpu mul fp32 11 add fp32 1
-42 gpu softmax fp32 1
------
-+++++
-conf2 1.767527790869615 1.7962938589450996 88.96 0.6000000000000085
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
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-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 155 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf3 1.7676486174436143 1.7967155014984917 88.78 0.7800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 155 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf4 1.7674352647250422 1.792910560846682 88.7 0.8599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf5 1.8655703338511067 1.8930089896922888 88.53999999999999 1.0200000000000102
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 167 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 159 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 165 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 157 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf6 1.9070428103729684 1.9172857853336078 88.38000000000001 1.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 152 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv samp_fp16 266 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 152 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv samp_fp16 261 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf7 1.769778590701739 1.7956222622694236 88.24 1.3200000000000074
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv fp16 12 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv samp_fp16 268 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf8 1.841404652091802 1.8677947628418006 88.24 1.3200000000000074
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 168 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 162 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf9 1.8679349428783487 1.8995927920729931 88.22 1.3400000000000034
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 159 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 160 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 168 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 161 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf10 1.876937310100899 1.9041581451399825 88.1 1.460000000000008
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf11 1.842140004857965 1.8673692956620238 88.06 1.5
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 166 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 167 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf12 1.9070567138857761 1.9165525910492667 88.02 1.5400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 152 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv samp_fp16 266 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 261 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 152 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf13 1.9185835698271805 1.9328202469403 87.98 1.5799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 152 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv samp_fp16 266 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 152 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 152 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf14 1.781744853993609 1.8082995958456516 87.92 1.6400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 166 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 168 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 159 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 165 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv samp_fp16 265 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv samp_fp16 268 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf15 1.9185835698271805 1.9328202469403 87.92 1.6400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 152 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv samp_fp16 266 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 152 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 152 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 12 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf16 1.875261840315855 1.8986912653657988 87.88 1.6800000000000068
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 159 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 12 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf17 1.9013559086026153 1.9230901214481015 87.86 1.7000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf18 1.9185835698271805 1.9328202469403 87.83999999999999 1.720000000000013
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 152 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv samp_fp16 266 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 152 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 152 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf19 1.8770503055325798 1.9007923328014182 87.82 1.740000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 151 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf20 1.8774136276932418 1.90365663123621 87.82 1.740000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf21 1.943143041264842 1.9591958561422729 87.82 1.740000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf22 1.870789918969847 1.8863625217899933 87.8 1.7600000000000051
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 264 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf23 1.7445941809066292 1.7754934270309912 87.78 1.7800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 155 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv perf_fp16 166 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf24 1.9065930313550916 1.928938946228637 87.78 1.7800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 167 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf25 1.9021824494907031 1.9237134505552098 87.78 1.7800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 154 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf26 1.9017271009017505 1.9211078231701697 87.78 1.7800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf27 1.8187224917656395 1.820406007609536 87.76 1.7999999999999972
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv samp_fp16 264 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf28 1.9070855899343322 1.9285210655709735 87.76 1.7999999999999972
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv samp_fp16 268 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf29 1.9013559086026153 1.9230901214481015 87.74 1.8200000000000074
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf30 1.8772990284718367 1.9022146647342513 87.72 1.8400000000000034
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf31 1.9013559086026153 1.9230901214481015 87.68 1.8799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf32 1.9020502478364545 1.923319572598976 87.66000000000001 1.8999999999999915
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf33 1.7516394053514481 1.7809034526471939 87.62 1.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 155 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv perf_fp16 166 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf34 1.7814953252955337 1.8122658147993431 87.62 1.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 162 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 167 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv perf_fp16 160 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 155 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv fp16 12 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 160 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv perf_fp16 166 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 155 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf35 1.887538247557846 1.9103369445911678 87.62 1.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 158 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 159 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf36 1.9107566783735581 1.9273803227885578 87.6 1.960000000000008
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 157 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf37 1.9013559086026153 1.9230901214481015 87.58 1.980000000000004
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 12 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf38 1.8984089819969947 1.9195632881772446 87.58 1.980000000000004
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf39 1.9020502478364545 1.923319572598976 87.52 2.0400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf40 1.9020502478364545 1.923319572598976 87.52 2.0400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf41 1.9013559086026153 1.9230901214481015 87.5 2.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf42 1.9013559086026153 1.9230901214481015 87.46000000000001 2.0999999999999943
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv fp16 11 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf43 1.9196179152539186 1.9443459719929068 87.44 2.1200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 153 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf44 1.9020502478364545 1.923319572598976 87.4 2.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf45 1.9152817031040366 1.9357432559063958 87.4 2.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf46 1.915754791147898 1.9373322475753219 87.4 2.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf47 1.9130551004051772 1.9409232417921056 87.38 2.180000000000007
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv perf_fp16 153 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf48 1.9421147660673033 1.9584555432766413 87.38 2.180000000000007
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf49 1.9052849920081363 1.9300100333661123 87.32 2.240000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 153 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf50 1.9154322863033566 1.934908329027621 87.3 2.260000000000005
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv perf_fp16 151 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
-+++++
-conf51 1.9079703554020564 1.9287218218306195 86.96000000000001 2.5999999999999943
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1
-3 gpu conv fp16 12 add fp16 1
-4 gpu add fp16 12
-5 gpu relu fp16 12
-6 gpu conv perf_fp16 153 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 161 add fp16 1
-8 gpu add fp16 12
-9 gpu relu fp16 12
-10 gpu conv perf_fp16 154 add fp16 1 relu fp16 1
-11 gpu conv perf_fp16 151 add fp16 1
-12 gpu add fp16 12
-13 gpu relu fp16 12
-14 gpu conv fp16 12 add fp16 1 relu fp16 1
-15 gpu conv fp16 12 add fp16 1
-16 gpu conv fp16 11 add fp16 1
-17 gpu add fp16 12
-18 gpu relu fp16 12
-19 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-20 gpu conv samp_fp16 262 add fp16 1
-21 gpu add fp16 12
-22 gpu relu fp16 12
-23 gpu conv perf_fp16 158 add fp16 1 relu fp16 1
-24 gpu conv perf_fp16 153 add fp16 1
-25 gpu add fp16 12
-26 gpu relu fp16 12
-27 gpu conv fp16 12 add fp16 1 relu fp16 1
-28 gpu conv fp16 12 add fp16 1
-29 gpu conv samp_fp16 261 add fp16 1
-30 gpu add fp16 12
-31 gpu relu fp16 12
-32 gpu conv fp16 12 add fp16 1 relu fp16 1
-33 gpu conv fp16 12 add fp16 1
-34 gpu add fp16 12
-35 gpu relu fp16 12
-36 gpu conv fp16 12 add fp16 1 relu fp16 1
-37 gpu conv perf_fp16 152 add fp16 1
-38 gpu add fp16 12
-39 gpu relu fp16 12
-40 gpu pool_mean fp16 12
-41 gpu mul fp16 12 add fp16 1
-42 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet50_imagenet/resnet50_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet50_imagenet/resnet50_imagenet.txt
deleted file mode 100644
index 094eed413b520f9dd661797b96735438861d1c08..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/resnet50_imagenet/resnet50_imagenet.txt
+++ /dev/null
@@ -1,1057 +0,0 @@
-7161.053769000008
-+++++
-conf1 1 1 75.7 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-2 gpu batchnorm fp32 11
-3 gpu conv fp32 11 add fp32 1
-4 gpu batchnorm fp32 11
-5 gpu relu fp32 11
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-14 gpu relu fp32 11
-15 gpu conv fp32 11 add fp32 1
-16 gpu batchnorm fp32 11
-17 gpu relu fp32 11
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-19 gpu batchnorm fp32 11
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-24 gpu relu fp32 11
-25 gpu conv fp32 11 add fp32 1
-26 gpu batchnorm fp32 11
-27 gpu relu fp32 11
-28 gpu conv fp32 11 add fp32 1
-29 gpu batchnorm fp32 11
-30 gpu relu fp32 11
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-35 gpu conv fp32 11 add fp32 1
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-37 gpu relu fp32 11
-38 gpu conv fp32 11 add fp32 1
-39 gpu batchnorm fp32 11
-40 gpu relu fp32 11
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-42 gpu batchnorm fp32 11
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-44 gpu batchnorm fp32 11
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-46 gpu relu fp32 11
-47 gpu conv fp32 11 add fp32 1
-48 gpu batchnorm fp32 11
-49 gpu relu fp32 11
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-51 gpu batchnorm fp32 11
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-58 gpu batchnorm fp32 11
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-84 gpu batchnorm fp32 11
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-86 gpu batchnorm fp32 11
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-90 gpu batchnorm fp32 11
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-100 gpu batchnorm fp32 11
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-103 gpu batchnorm fp32 11
-104 gpu relu fp32 11
-105 gpu conv fp32 11 add fp32 1
-106 gpu batchnorm fp32 11
-107 gpu add fp32 11
-108 gpu relu fp32 11
-109 gpu conv fp32 11 add fp32 1
-110 gpu batchnorm fp32 11
-111 gpu relu fp32 11
-112 gpu conv fp32 11 add fp32 1
-113 gpu batchnorm fp32 11
-114 gpu relu fp32 11
-115 gpu conv fp32 11 add fp32 1
-116 gpu batchnorm fp32 11
-117 gpu add fp32 11
-118 gpu relu fp32 11
-119 gpu conv fp32 11 add fp32 1
-120 gpu batchnorm fp32 11
-121 gpu relu fp32 11
-122 gpu conv fp32 11 add fp32 1
-123 gpu batchnorm fp32 11
-124 gpu relu fp32 11
-125 gpu conv fp32 11 add fp32 1
-126 gpu batchnorm fp32 11
-127 gpu add fp32 11
-128 gpu relu fp32 11
-129 gpu conv fp32 11 add fp32 1
-130 gpu batchnorm fp32 11
-131 gpu relu fp32 11
-132 gpu conv fp32 11 add fp32 1
-133 gpu batchnorm fp32 11
-134 gpu relu fp32 11
-135 gpu conv fp32 11 add fp32 1
-136 gpu batchnorm fp32 11
-137 gpu add fp32 11
-138 gpu relu fp32 11
-139 gpu conv fp32 11 add fp32 1
-140 gpu batchnorm fp32 11
-141 gpu relu fp32 11
-142 gpu conv fp32 11 add fp32 1
-143 gpu batchnorm fp32 11
-144 gpu relu fp32 11
-145 gpu conv fp32 11 add fp32 1
-146 gpu batchnorm fp32 11
-147 gpu conv fp32 11 add fp32 1
-148 gpu batchnorm fp32 11
-149 gpu add fp32 11
-150 gpu relu fp32 11
-151 gpu conv fp32 11 add fp32 1
-152 gpu batchnorm fp32 11
-153 gpu relu fp32 11
-154 gpu conv fp32 11 add fp32 1
-155 gpu batchnorm fp32 11
-156 gpu relu fp32 11
-157 gpu conv fp32 11 add fp32 1
-158 gpu batchnorm fp32 11
-159 gpu add fp32 11
-160 gpu relu fp32 11
-161 gpu conv fp32 11 add fp32 1
-162 gpu batchnorm fp32 11
-163 gpu relu fp32 11
-164 gpu conv fp32 11 add fp32 1
-165 gpu batchnorm fp32 11
-166 gpu relu fp32 11
-167 gpu conv fp32 11 add fp32 1
-168 gpu batchnorm fp32 11
-169 gpu add fp32 11
-170 gpu relu fp32 11
-171 gpu pool_max fp32 11
-172 gpu mul fp32 11 add fp32 1
-173 gpu softmax fp32 1
------
-+++++
-conf2 1.8254789092281507 1.4527803526239977 75.7 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv fp16 12 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf3 1.8254789092281507 1.4527803526239977 75.7 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv fp16 12 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf4 1.8254789092281507 1.4527803526239977 75.7 0.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
-73 gpu conv fp16 12 add fp16 1
-74 gpu batchnorm fp16 12
-75 gpu add fp16 12
-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv fp16 12 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv fp16 12 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv fp16 12 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf5 1.8323072136026506 1.457112696128105 74.76 0.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
-59 gpu relu fp16 12
-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
-62 gpu relu fp16 12
-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
-65 gpu add fp16 12
-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
-68 gpu batchnorm fp16 12
-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
-72 gpu relu fp16 12
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-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
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-83 gpu conv fp16 12 add fp16 1
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-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv perf_fp16 157 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv fp16 12 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv perf_fp16 152 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
-+++++
-conf6 1.8333922701839533 1.4589203187717397 74.53999999999999 1.1600000000000108
-1 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-2 gpu batchnorm fp16 12
-3 gpu conv fp16 12 add fp16 1
-4 gpu batchnorm fp16 12
-5 gpu relu fp16 12
-6 gpu conv fp16 12 add fp16 1
-7 gpu batchnorm fp16 12
-8 gpu relu fp16 12
-9 gpu conv fp16 12 add fp16 1
-10 gpu batchnorm fp16 12
-11 gpu conv fp16 12 add fp16 1
-12 gpu batchnorm fp16 12
-13 gpu add fp16 12
-14 gpu relu fp16 12
-15 gpu conv fp16 12 add fp16 1
-16 gpu batchnorm fp16 12
-17 gpu relu fp16 12
-18 gpu conv fp16 12 add fp16 1
-19 gpu batchnorm fp16 12
-20 gpu relu fp16 12
-21 gpu conv fp16 12 add fp16 1
-22 gpu batchnorm fp16 12
-23 gpu add fp16 12
-24 gpu relu fp16 12
-25 gpu conv fp16 12 add fp16 1
-26 gpu batchnorm fp16 12
-27 gpu relu fp16 12
-28 gpu conv fp16 12 add fp16 1
-29 gpu batchnorm fp16 12
-30 gpu relu fp16 12
-31 gpu conv fp16 12 add fp16 1
-32 gpu batchnorm fp16 12
-33 gpu add fp16 12
-34 gpu relu fp16 12
-35 gpu conv fp16 12 add fp16 1
-36 gpu batchnorm fp16 12
-37 gpu relu fp16 12
-38 gpu conv fp16 12 add fp16 1
-39 gpu batchnorm fp16 12
-40 gpu relu fp16 12
-41 gpu conv fp16 12 add fp16 1
-42 gpu batchnorm fp16 12
-43 gpu conv fp16 12 add fp16 1
-44 gpu batchnorm fp16 12
-45 gpu add fp16 12
-46 gpu relu fp16 12
-47 gpu conv fp16 12 add fp16 1
-48 gpu batchnorm fp16 12
-49 gpu relu fp16 12
-50 gpu conv fp16 12 add fp16 1
-51 gpu batchnorm fp16 12
-52 gpu relu fp16 12
-53 gpu conv fp16 12 add fp16 1
-54 gpu batchnorm fp16 12
-55 gpu add fp16 12
-56 gpu relu fp16 12
-57 gpu conv fp16 12 add fp16 1
-58 gpu batchnorm fp16 12
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-60 gpu conv fp16 12 add fp16 1
-61 gpu batchnorm fp16 12
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-63 gpu conv fp16 12 add fp16 1
-64 gpu batchnorm fp16 12
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-66 gpu relu fp16 12
-67 gpu conv fp16 12 add fp16 1
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-69 gpu relu fp16 12
-70 gpu conv fp16 12 add fp16 1
-71 gpu batchnorm fp16 12
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-74 gpu batchnorm fp16 12
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-76 gpu relu fp16 12
-77 gpu conv fp16 12 add fp16 1
-78 gpu batchnorm fp16 12
-79 gpu relu fp16 12
-80 gpu conv fp16 12 add fp16 1
-81 gpu batchnorm fp16 12
-82 gpu relu fp16 12
-83 gpu conv fp16 12 add fp16 1
-84 gpu batchnorm fp16 12
-85 gpu conv fp16 12 add fp16 1
-86 gpu batchnorm fp16 12
-87 gpu add fp16 12
-88 gpu relu fp16 12
-89 gpu conv fp16 12 add fp16 1
-90 gpu batchnorm fp16 12
-91 gpu relu fp16 12
-92 gpu conv fp16 12 add fp16 1
-93 gpu batchnorm fp16 12
-94 gpu relu fp16 12
-95 gpu conv fp16 12 add fp16 1
-96 gpu batchnorm fp16 12
-97 gpu add fp16 12
-98 gpu relu fp16 12
-99 gpu conv perf_fp16 157 add fp16 1
-100 gpu batchnorm fp16 12
-101 gpu relu fp16 12
-102 gpu conv samp_fp16 267 add fp16 1
-103 gpu batchnorm fp16 12
-104 gpu relu fp16 12
-105 gpu conv fp16 12 add fp16 1
-106 gpu batchnorm fp16 12
-107 gpu add fp16 12
-108 gpu relu fp16 12
-109 gpu conv fp16 12 add fp16 1
-110 gpu batchnorm fp16 12
-111 gpu relu fp16 12
-112 gpu conv fp16 12 add fp16 1
-113 gpu batchnorm fp16 12
-114 gpu relu fp16 12
-115 gpu conv fp16 12 add fp16 1
-116 gpu batchnorm fp16 12
-117 gpu add fp16 12
-118 gpu relu fp16 12
-119 gpu conv fp16 12 add fp16 1
-120 gpu batchnorm fp16 12
-121 gpu relu fp16 12
-122 gpu conv fp16 12 add fp16 1
-123 gpu batchnorm fp16 12
-124 gpu relu fp16 12
-125 gpu conv fp16 12 add fp16 1
-126 gpu batchnorm fp16 12
-127 gpu add fp16 12
-128 gpu relu fp16 12
-129 gpu conv fp16 12 add fp16 1
-130 gpu batchnorm fp16 12
-131 gpu relu fp16 12
-132 gpu conv fp16 12 add fp16 1
-133 gpu batchnorm fp16 12
-134 gpu relu fp16 12
-135 gpu conv fp16 12 add fp16 1
-136 gpu batchnorm fp16 12
-137 gpu add fp16 12
-138 gpu relu fp16 12
-139 gpu conv fp16 12 add fp16 1
-140 gpu batchnorm fp16 12
-141 gpu relu fp16 12
-142 gpu conv fp16 12 add fp16 1
-143 gpu batchnorm fp16 12
-144 gpu relu fp16 12
-145 gpu conv fp16 12 add fp16 1
-146 gpu batchnorm fp16 12
-147 gpu conv fp16 12 add fp16 1
-148 gpu batchnorm fp16 12
-149 gpu add fp16 12
-150 gpu relu fp16 12
-151 gpu conv fp16 12 add fp16 1
-152 gpu batchnorm fp16 12
-153 gpu relu fp16 12
-154 gpu conv fp16 12 add fp16 1
-155 gpu batchnorm fp16 12
-156 gpu relu fp16 12
-157 gpu conv fp16 12 add fp16 1
-158 gpu batchnorm fp16 12
-159 gpu add fp16 12
-160 gpu relu fp16 12
-161 gpu conv fp16 12 add fp16 1
-162 gpu batchnorm fp16 12
-163 gpu relu fp16 12
-164 gpu conv perf_fp16 152 add fp16 1
-165 gpu batchnorm fp16 12
-166 gpu relu fp16 12
-167 gpu conv fp16 12 add fp16 1
-168 gpu batchnorm fp16 12
-169 gpu add fp16 12
-170 gpu relu fp16 12
-171 gpu pool_max fp16 12
-172 gpu mul fp16 12 add fp16 1
-173 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar10/vgg16_cifar10.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar10/vgg16_cifar10.txt
deleted file mode 100644
index 2b325a9fe2d122e74cdd2b80e2768e68591313bf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar10/vgg16_cifar10.txt
+++ /dev/null
@@ -1,913 +0,0 @@
-3776.508929999999
-+++++
-conf1 1 1 89.96 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.1225958306417145 1.9771056444390926 89.91 0.04999999999999716
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 161 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf3 2.090180991844805 1.9532689756636086 89.82 0.14000000000000057
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 161 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf4 2.169931036393396 2.0048851858669283 89.53999999999999 0.4200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv perf_fp16 162 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 264 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf5 2.1012179398201756 1.9325098819632314 89.42 0.539999999999992
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 264 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf6 2.2313002482945326 2.069581185407626 89.38000000000001 0.5799999999999841
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 158 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf7 2.143061101834193 1.9675759235961738 89.3 0.6599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 265 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 264 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf8 2.199379444387758 2.0314348091429677 89.2 0.7599999999999909
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 264 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf9 2.3236298452294624 2.156907976575644 89.03999999999999 0.9200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf10 2.3224369486241603 2.1560351277882046 89.03999999999999 0.9200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf11 2.358467412507993 2.1904290636262784 89.02 0.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf12 2.3633503986583126 2.1980949050120437 88.88000000000001 1.079999999999984
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf13 2.4903388172036043 2.3063593441573564 88.82 1.1400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf14 2.508156996742662 2.3204109539869595 88.78 1.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf15 2.4818531813049622 2.2910866330696744 88.75999999999999 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf16 2.4591564896606 2.272664410995804 88.74 1.2199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf17 2.5370582721089496 2.3464665753522405 88.72 1.2399999999999949
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf18 2.438100014978735 2.257620696759345 88.7 1.259999999999991
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf19 2.4776935382337006 2.2949598026093168 88.7 1.259999999999991
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf20 2.4380041604279596 2.254330054479329 88.68 1.279999999999987
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf21 2.4745444350223327 2.2883888475386525 88.64 1.3199999999999932
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf22 2.4136652022060625 2.2360545757445407 88.52 1.4399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf23 2.510093966915115 2.316437144001897 88.52 1.4399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf24 2.475990790728594 2.28127562431577 88.5 1.4599999999999937
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf25 2.4761929121466926 2.290365501363375 88.5 1.4599999999999937
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf26 2.4763575559033875 2.291312348847263 88.5 1.4599999999999937
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf27 2.600249602991055 2.4123747341424644 88.06 1.8999999999999915
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 165 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf28 2.596077615026303 2.4115375655840245 88.02 1.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 166 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf29 2.580888020555937 2.3840829703999833 87.88 2.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf30 2.556352783745439 2.3641413704751537 87.8 2.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf31 2.5559756082494527 2.3677471703724575 87.78 2.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 11 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf32 2.597413373332546 2.4091972878097585 87.76 2.1999999999999886
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf33 2.4797467027434656 2.2874608793842612 87.74 2.219999999999999
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf34 2.593675604602072 2.400513932866452 87.7 2.259999999999991
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf35 2.6300759173431336 2.432687374579977 87.62 2.339999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf36 2.5907083037103864 2.4042762580264356 87.6 2.3599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf37 2.6143261650366187 2.423427684623993 87.6 2.3599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf38 2.6144436259117203 2.4231961521843344 87.6 2.3599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf39 2.662088796913144 2.4660859696742032 87.6 2.3599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf40 2.6210428708834517 2.423389791646294 87.58 2.3799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 265 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf41 2.6399924349243533 2.4443864221157914 87.58 2.3799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf42 2.616443708384916 2.4217582570150697 87.58 2.3799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf43 2.6883473596205225 2.5036952786284137 87.5 2.4599999999999937
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv perf_fp16 166 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf44 2.6117356623585875 2.420771216556161 87.48 2.4799999999999898
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf45 2.6359174040106708 2.444231592562593 87.48 2.4799999999999898
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf46 2.56504192294198 2.371871906722655 87.44 2.519999999999996
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf47 2.5652588453899727 2.3816996471861174 87.44 2.519999999999996
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf48 2.68806951500876 2.5007647690311425 87.14 2.819999999999993
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv perf_fp16 166 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv perf_fp16 156 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar100/vgg16_cifar100.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar100/vgg16_cifar100.txt
deleted file mode 100644
index 2c29bedd096aec2c7f66afbe729353e372fac403..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_cifar100/vgg16_cifar100.txt
+++ /dev/null
@@ -1,970 +0,0 @@
-3768.819777999999
-+++++
-conf1 1 1 66.5 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1
-16 gpu softmax fp32 1
------
-+++++
-conf2 2.2877724452131787 2.08025704453875 66.45 0.04999999999999716
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 153 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 162 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf3 2.5314658805383816 2.30737681453141 66.45 0.04999999999999716
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf4 2.044123178914057 1.8616966918258782 66.32000000000001 0.1799999999999926
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 168 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf5 2.231179358259141 2.0317825813373864 66.18 0.3199999999999932
-1 gpu conv fp16 11 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 161 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 265 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf6 2.2474834421641057 2.0338639876373272 65.88000000000001 0.6199999999999903
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 265 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 267 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf7 2.22281439516094 2.0205460706906377 65.88000000000001 0.6199999999999903
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 161 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-12 gpu conv perf_fp16 161 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf8 2.1625085012968484 1.94560449637282 65.88000000000001 0.6199999999999903
-1 gpu conv fp16 11 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv fp16 11 add fp16 1 relu fp16 1
-10 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 263 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf9 2.639337323402163 2.3960416499256825 65.8 0.7000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 269 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf10 2.672718090670276 2.4276905528801507 65.68 0.8199999999999932
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf11 2.699089631751789 2.446114054498494 65.68 0.8199999999999932
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf12 2.6003752638648767 2.3553067802112344 65.64 0.8599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf13 2.638763904718665 2.395072565223988 65.64 0.8599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 268 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf14 2.6003752638648767 2.3553067802112344 65.64 0.8599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf15 2.6003752638648767 2.3553067802112344 65.64 0.8599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf16 2.6732183804279006 2.4287517162140326 65.62 0.8799999999999955
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf17 2.6728394017929027 2.428768169588016 65.60000000000001 0.8999999999999915
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf18 2.4549989178389238 2.2406620346549433 65.56 0.9399999999999977
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf19 2.673556689244081 2.429092581627209 65.52 0.980000000000004
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf20 2.6525635304451756 2.406830663552284 65.5 1.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf21 2.6692288605087553 2.423462800937785 65.5 1.0
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf22 2.583650505571873 2.3471533059252194 65.48 1.019999999999996
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf23 2.6474572655420125 2.400471260394867 65.48 1.019999999999996
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 265 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf24 2.4710116424304736 2.2555966923178996 65.46 1.0400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 161 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf25 2.557911102074785 2.3292661683311526 65.46 1.0400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf26 2.6032957018479532 2.367574146141511 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 163 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf27 2.6029968728098916 2.3672068592437223 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf28 2.602540311129756 2.3691028781436954 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 167 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf29 2.602756708588441 2.3708111025211718 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 168 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf30 2.603240857443844 2.3662875785790183 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf31 2.602882717372841 2.368011704225619 65.44 1.0600000000000023
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf32 2.67999343314603 2.4305182001043826 65.4 1.0999999999999943
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf33 2.670314990364046 2.4275308713267485 65.38000000000001 1.1199999999999903
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf34 2.650982630033638 2.405821467700663 65.36 1.1400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 263 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf35 2.6507266317871756 2.405938171802741 65.36 1.1400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 265 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf36 2.6523068534836174 2.406695716686769 65.34 1.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf37 2.6533198495191073 2.4077689394073865 65.34 1.1599999999999966
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf38 2.64630900155657 2.4073892305914986 65.32 1.1800000000000068
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 152 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf39 2.6725522534379413 2.42903505877629 65.32 1.1800000000000068
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf40 2.6435249267602225 2.403536258709464 65.3 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 161 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf41 2.6442059720503557 2.4037376163252024 65.3 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf42 2.6536933126724027 2.4077527693156053 65.3 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf43 2.6442798101298948 2.4056031584129225 65.3 1.2000000000000028
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf44 2.603921271336049 2.3665955131107683 65.28 1.2199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf45 2.4967248028856828 2.2748997625822716 65.25999999999999 1.240000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 157 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf46 2.4963953691980665 2.2764932409573166 65.25999999999999 1.240000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf47 2.678944927989822 2.4251978482969956 65.24 1.2600000000000051
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 264 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf48 2.6727135417173904 2.428897140422096 65.22 1.2800000000000011
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf49 2.600256135586627 2.355428067042657 65.16 1.3400000000000034
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 151 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv fp16 11 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf50 2.264460006128871 2.058037581586567 64.9 1.5999999999999943
-1 gpu conv fp16 11 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv perf_fp16 165 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 269 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 164 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 263 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 265 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
-+++++
-conf51 2.2817447204106736 2.0758846029697513 64.84 1.6599999999999966
-1 gpu conv fp16 11 add fp16 1 relu fp16 1
-2 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv perf_fp16 165 add fp16 1 relu fp16 1
-4 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-9 gpu conv perf_fp16 155 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv perf_fp16 160 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 265 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1
-16 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_imagenet/vgg16_imagenet.txt b/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_imagenet/vgg16_imagenet.txt
deleted file mode 100644
index 108a101c810f4ebe488e6f2029be4d970d7869a2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/PPoPP_results/soc_sim_results/predictive/vgg16_imagenet/vgg16_imagenet.txt
+++ /dev/null
@@ -1,561 +0,0 @@
-19194.623482
-+++++
-conf1 1 1 72.84 0.0
-1 gpu conv fp32 11 add fp32 1 relu fp32 1
-2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-3 gpu conv fp32 11 add fp32 1 relu fp32 1
-4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-5 gpu conv fp32 11 add fp32 1 relu fp32 1
-6 gpu conv fp32 11 add fp32 1 relu fp32 1
-7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-8 gpu conv fp32 11 add fp32 1 relu fp32 1
-9 gpu conv fp32 11 add fp32 1 relu fp32 1
-10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-11 gpu conv fp32 11 add fp32 1 relu fp32 1
-12 gpu conv fp32 11 add fp32 1 relu fp32 1
-13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
-14 gpu mul fp32 11 add fp32 1 relu fp32 1
-15 gpu mul fp32 11 add fp32 1 relu fp32 1
-16 gpu mul fp32 11 add fp32 1
-17 gpu softmax fp32 1
------
-+++++
-conf2 2.0787477568568082 1.7725701909562666 72.76 0.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf3 2.2877881266029436 1.9268677640464096 72.04 0.7999999999999972
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf4 2.493698381711785 2.0336802939709626 72.02 0.8200000000000074
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf5 2.164723960411776 1.8442442134020163 71.94 0.9000000000000057
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf6 2.53794461743687 2.069640641367895 71.67999999999999 1.1600000000000108
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf7 1.7943268128686711 1.6103705347377417 71.58 1.2600000000000051
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf8 1.8143284638396158 1.6288620764171362 71.5 1.3400000000000034
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv fp16 12 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf9 2.5462742331906263 2.076061630349781 71.48 1.3599999999999994
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf10 2.526515422129153 2.063839193109964 71.39999999999999 1.440000000000012
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf11 2.1596661517243856 1.8351710968407349 71.34 1.5
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 267 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 268 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 156 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf12 2.3444383477958337 1.981259839350623 71.22 1.6200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf13 1.8402020049200172 1.652343405000522 71.2 1.6400000000000006
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 266 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-13 gpu conv fp16 11 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf14 2.6420417968257306 2.167425635999969 71.12 1.7199999999999989
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 155 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf15 2.543198098440602 2.0805826545876145 71.1 1.740000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf16 2.6224991911009328 2.1476958232678807 70.89999999999999 1.940000000000012
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf17 2.5978010917593752 2.131515210392801 70.8 2.0400000000000063
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 157 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf18 2.623210258119482 2.156636511928761 70.76 2.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf19 2.598187894495609 2.1322228990374104 70.76 2.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf20 2.640464221374653 2.1682626030871295 70.76 2.0799999999999983
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 167 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf21 2.659563405662692 2.1881035849678936 70.54 2.299999999999997
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf22 2.636584103560761 2.1652496021557557 70.39999999999999 2.440000000000012
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 165 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf23 2.6315080449303547 2.161259580137757 70.38 2.460000000000008
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 162 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf24 2.7367939789033153 2.263326406058847 70.34 2.5
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 160 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf25 2.712182817327382 2.2404693918737233 70.24000000000001 2.5999999999999943
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 168 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf26 2.660510795888948 2.187299344706456 70.22 2.6200000000000045
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-9 gpu conv fp16 12 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf27 2.457573203839654 2.0936930776435383 70.1 2.740000000000009
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv fp16 12 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-10 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv samp_fp16 262 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv samp_fp16 261 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
-+++++
-conf28 2.7452293174567757 2.2593302388139347 69.92 2.9200000000000017
-1 gpu conv fp16 12 add fp16 1 relu fp16 1
-2 gpu conv samp_fp16 262 add fp16 1 relu fp16 1 pool_max fp16 1
-3 gpu conv fp16 12 add fp16 1 relu fp16 1
-4 gpu conv perf_fp16 159 add fp16 1 relu fp16 1 pool_max fp16 1
-5 gpu conv fp16 12 add fp16 1 relu fp16 1
-6 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-7 gpu conv samp_fp16 266 add fp16 1 relu fp16 1 pool_max fp16 1
-8 gpu conv fp16 12 add fp16 1 relu fp16 1
-9 gpu conv samp_fp16 261 add fp16 1 relu fp16 1
-10 gpu conv perf_fp16 152 add fp16 1 relu fp16 1 pool_max fp16 1
-11 gpu conv fp16 12 add fp16 1 relu fp16 1
-12 gpu conv fp16 12 add fp16 1 relu fp16 1
-13 gpu conv perf_fp16 151 add fp16 1 relu fp16 1 pool_max fp16 1
-14 gpu mul fp16 12 add fp16 1 relu fp16 1
-15 gpu mul fp16 12 add fp16 1 relu fp16 1
-16 gpu mul fp16 12 add fp16 1
-17 gpu softmax fp32 1
------
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/autotuner/img_pareto_curve.py b/hpvm/projects/hpvm-tensor-rt/autotuner/autotuner/img_pareto_curve.py
deleted file mode 100644
index 5192eb7e580205ba9fcd368baa2e26358d4315d5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/autotuner/img_pareto_curve.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import os
-import shutil
-
-
-AL_THRESHOLD = 0.1
-
-
-class Config:
-    def __init__(self):
-        self.avg_accuracy = 0
-        self.avg_loss = 0
-        self.speedup = 1
-        self.fname = ""
-        self.flags = []
-
-
-def loadConfigsFromDir(result_dir):
-    def parseTopLine(x):
-
-        toks = x.split()
-
-        speedup = 1.0
-        for tok in toks:
-            if "avg_accuracy" in tok:
-                avg_accuracy = float(tok.split("=")[1])
-            if "speedup" in tok:
-                speedup = float(tok.split("=")[1])
-        return avg_accuracy, speedup
-
-    def skipFile(fname):
-
-        skip_files = {}
-        skip_files["confidence_summary.txt"] = 1
-        skip_files["promise_confs.txt"] = 1
-
-        if "accuracy" in fname:  # *_accuracy files should be skipped
-            return True
-
-        if "norms" in fname:  # *_accuracy files should be skipped
-            return True
-
-        if ".#" in fname:  # *_accuracy files should be skipped
-            return True
-
-        # if "_promise" in fname: # *_accuracy files should be skipped
-        #  return True
-
-        if not fname[-1].isdigit():
-            return True
-
-        if fname in skip_files:
-            return True
-        else:
-            return False
-
-    config_arr = []
-    file_names = os.listdir(result_dir)
-
-    for fname in file_names:
-        if not skipFile(fname):
-
-            fpath = result_dir + fname
-            config = Config()
-            f = open(fpath, "r")
-
-            it = 0
-            for x in f:
-                if x.strip == "":
-                    continue
-                if it == 0:
-                    avg_accuracy, speedup = parseTopLine(x)
-                    config.avg_accuracy = avg_accuracy
-                    config.avg_loss = -avg_accuracy
-                    config.speedup = speedup
-                    config.fname = fname
-                else:
-                    flag = int(x.strip())
-                    config.flags.append(flag)
-                it += 1
-
-            config_arr.append(config)
-
-    return config_arr
-
-
-class Configuration:
-    def __init__(self, name, speedup, energy, accuracy, accuracy_loss):
-        self.name = name
-        self.speedup = speedup
-        self.energy = energy
-        self.accuracy = accuracy
-        self.accuracy_loss = accuracy_loss
-
-    def __repr__(self):
-        return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-    @classmethod
-    def from_config(cls, config):
-        return cls(config.fname, config.speedup, 0, config.avg_accuracy, config.avg_loss)
-
-    @staticmethod
-    def energy_points(configurations):
-        return [
-            (conf.energy, conf.accuracy)
-            for conf in configurations
-        ]
-
-    @staticmethod
-    def speedup_points(configurations):
-        return [
-            (conf.speedup, conf.accuracy)
-            for conf in configurations
-        ]
-
-
-def is_pareto_efficient(configs, values, value_margins):
-    import numpy as np
-    from pprint import pprint
-
-    np_values = np.array(values)
-    np_margins = np.array(value_margins)
-    is_efficient = np.ones(np_values.shape[0], dtype=bool)
-    for i, c in enumerate(np_values):
-        if is_efficient[i]:
-            # Keep any point with a higher value
-            is_efficient[is_efficient] = np.any(
-                np_values[is_efficient] + np_margins >= c, axis=1
-            )
-            is_efficient[i] = True  # And keep self
-    return (np.array(configs)[is_efficient]).tolist()
-
-
-def findParetoConfigs(base_dir, psnr_band_size):
-    result_dir = base_dir + "/pareto/"
-    try:
-        os.mkdir(result_dir)
-    except:
-        print "could not create dir"
-
-    input_dir = base_dir + "/high_confidence/"
-    config_arr = loadConfigsFromDir(input_dir)
-    configurations = [Configuration.from_config(
-        config) for config in config_arr]
-    # energy_points = Configuration.energy_points(configurations)
-    speedup_points = Configuration.speedup_points(configurations)
-
-    # No Pareto Selection if list is < 50 configurations
-    if len(configurations) < 50:
-        speedup_pareto = configurations # Include all in Pareto Frontier
-    else:
-        # energy_pareto = is_pareto_efficient(configurations, energy_points, ...)
-        speedup_pareto = is_pareto_efficient(
-            configurations, speedup_points, [-1e-2, psnr_band_size]
-        )
-    print("len(configurations) = ", len(configurations))
-    print("len(speedup_pareto) = ", len(speedup_pareto))
-
-    for conf in speedup_pareto:
-        #dst_path = conf.name.replace("full_results", "pareto")
-        src_path = input_dir + conf.name
-        dst_path = result_dir + conf.name
-        shutil.copy(src_path, dst_path)
-
-
-if __name__ == "__main__":
-    from sys import argv
-    psnr_band_size = float(argv[2]) if len(argv) > 2 else 3.0
-    print("*psnr_band_size = ", psnr_band_size)
-    findParetoConfigs(argv[1], psnr_band_size)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_layers.txt
deleted file mode 100644
index 5741a41ba302af533e5f6e31be0611226dfbe7db..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_layers.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-conv  add  tanh  pool  
-conv  add  tanh  pool  
-conv  add  tanh  
-conv  add  tanh  
-conv  add  tanh  pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_tensors.txt
deleted file mode 100644
index 2a925c986fc2b82718bfb0497f01ce48a99db223..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/alexnet_tensors.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-#Conv1,4
-Conv
-Add
-Relu
-Pool
-#Conv2,4
-Conv
-Add
-Relu
-Pool
-#Conv3,3
-Conv
-Add
-Relu
-#Conv4,3
-Conv
-Add
-Relu
-#Conv5,4
-Conv
-Add
-Relu
-Pool
-#FC1,2
-Mul
-Add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/dev_knobs.txt
deleted file mode 100644
index bba4dc88d5b3940413a099c13c903a01d0000c56..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/dev_knobs.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs.txt
deleted file mode 100644
index 050fc6118045090b4a5cc442105181f56d693a77..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs1.txt
deleted file mode 100644
index d2fc2c9493453f55cb83094373b19a24b59135d4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/knobs1.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/op_cost.txt
deleted file mode 100644
index 04336fca2708d5e5d78849e1c12014f5ddbd1ad7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet/op_cost.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-11894784.000000
-39321600.000000
-21233664.000000
-28311552.000000
-18874368.000000
-20480.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_layers.txt
deleted file mode 100644
index 00059a38ce4d7a71d3c5f0b4888924e4fcce9e98..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_layers.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-conv  add  tanh  
-conv  add  tanh  pool  
-conv  add  tanh  
-conv  add  tanh  pool  
-conv  add  tanh  
-conv  add  tanh  pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_tensors.txt
deleted file mode 100644
index 747c7221bae6e15ebb2d86c3ae6e577362602700..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/alexnet2_tensors.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-#Conv1,3
-Conv
-Add
-Relu
-#Conv2,4
-Conv
-Add
-Relu
-Pool
-#Conv3,3
-Conv
-Add
-Relu
-#Conv4,4
-Conv
-Add
-Relu
-Pool
-#Conv5,3
-Conv
-Add
-Relu
-#Conv6,4
-Conv
-Add
-Relu
-Pool
-#FC1,2
-Mul
-Add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/dev_knobs.txt
deleted file mode 100644
index 0324aecdca3c4b13fb30f1afdabcf69d22df9027..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/dev_knobs.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs.txt
deleted file mode 100644
index c873eeddcdeaa44fe0365bdb5e3292997d0074b6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs1.txt
deleted file mode 100644
index 063ba473d6a7fa57d7572c86dde9beac0932163d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/knobs1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/op_cost.txt
deleted file mode 100644
index 5a5722f202dde469dca94c71dd9c5fc1cd7aa32b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2/op_cost.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-88473.601562
-943718.375000
-471859.187500
-943718.375000
-471859.187500
-943718.375000
-2048.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/layers.txt
deleted file mode 100644
index 01f40077d4f8342479d1965551af2d7e30a4c3f2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/layers.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-conv  add  tanh
-conv  add  tanh  pool
-conv  add  tanh
-conv  add  tanh  pool
-conv  add  tanh
-conv  add  tanh  pool
-dense  add
-reduce
-conv
-conv
-conv
-reduce
-reduce
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/op_cost.txt
deleted file mode 100644
index 80ff2706a43e33b81af6d47e96f702efdfcb21b3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet2_canny_hpvm/op_cost.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-468.076
-947.434
-255.422
-348.769
-256.658
-1.05427
-1.05427
-107.5062
-666.888
-432.622
-252.458
-11.51922
-2.01168
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/layer_composition.txt
deleted file mode 100644
index b2bf962cd60722978b3205adca9c5822e59fc603..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/layer_composition.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-conv  add  activation  pool  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-dense  add  activation  
-dense  add  activation  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/op_cost.txt
deleted file mode 100644
index ec3b8b5f375673e659594dca7ad8fd8ef6ace435..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/op_cost.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-1457111.000000
-4478976.000000
-2242805.750000
-2990407.750000
-1993605.125000
-754974.750000
-335544.312500
-81920.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/quant_ranges2.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/quant_ranges2.txt
deleted file mode 100644
index 36c9c390b54168b9c872939f81dc2c6187e04761..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/alexnet_imagenet/quant_ranges2.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-0.0 255.0 -0.5503702693581581 0.5811487324237921 -2.802485 1.648145 0.0 1572.3096923828125
-0.0 1572.3096923828125 -0.2867645202279091 0.26272463005783797 -0.47985682 0.501206 0.0 3183.7813264160477
-0.0 3183.7813264160477 -0.16606662392616273 0.15785247704386754 -0.42038992 0.5545839 0.0 1765.4451872558668
-0.0 1765.4451872558668 -0.10464580833911895 0.11035470351576919 -1.4275751 0.9042998 0.0 1345.5418548586083
-0.0 1345.5418548586083 -0.09240880391001702 0.10250756608694818 -0.45662758 2.4040315 0.0 1227.3563232421875
-0.0 1227.3563232421875 -0.030517672039568428 0.02963459612801672 -0.07124679 0.09377053 0.0 1034.5966391601676
-0.0 1034.5966391601676 -0.038392101023346184 0.039147199764847845 -0.050027702 0.1841282 0.0 839.0697069702154
-0.0 839.0697069702154 -0.05494491942599416 0.08549865524470925 -0.16314922 0.15416704 -608.3993963623047 1082.8444653320819
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/knobs.txt
deleted file mode 100644
index 3bcc8d25cc464e2557fdedbb2b5f93b05999f5f9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/knobs.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,41,43,45
-11,41,43,45
-11
-11,41,43,45
-11,41,43,45
-11
-11
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,41,43,45
-11,41,43,45
-11
-11,41,43,45
-11,41,43,45
-11
-11,12
-11
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/op_cost.txt
deleted file mode 100644
index 9575b4b7c6783c0bd49eb8aa945045a6a4614af1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/op_cost.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-4425.6
-112.809
-10.9522
-675.831
-113.067
-11.4471
-674.881
-686.27
-4334.16
-112.544
-11.1993
-674.836
-112.973
-11.7161
-675.575
-685.598
-686.733
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/tuner_conf_template.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/tuner_conf_template.txt
deleted file mode 100644
index 4cd90c098b42e33b2af46593ac760775c5c92e85..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/blend/tuner_conf_template.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-+++++
-conf1 1.5 90 1.0 2.0
-1 gpu conv fp32 1
-2 gpu reduce fp32 1 
-3 gpu reduce fp32 1
-4 gpu map2 fp32 1
-5 gpu reduce fp32 1
-6 gpu reduce fp32 1
-7 gpu map2 fp32 1
-8 gpu map2 fp32 1
-9 gpu conv fp32 1
-10 gpu reduce fp32 1 
-11 gpu reduce fp32 1
-12 gpu map2 fp32 1
-13 gpu reduce fp32 1
-14 gpu reduce fp32 1
-15 gpu map2 fp32 1
-16 gpu map2 fp32 1
-17 gpu map2 fp32 1
------
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/knobs.txt
deleted file mode 100644
index bd4820f4123eafa0fbcda1c4896e91e2f7dfda7f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/knobs.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-11,41,43,45
-11
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
-11,41,43,45
-11,41,43,45
-11
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/op_cost.txt
deleted file mode 100644
index 64e2c8bec1bed96c8d6a25d3358075adb0de48f0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/op_cost.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-87.1338
-10.9506
-703.070
-520.145
-335.735
-19.1877
-9.27351
-2.46408
-17.7423
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/tuner_conf_template.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/tuner_conf_template.txt
deleted file mode 100644
index 17b30a46e0e4ae079f909e38d79c86876e802e25..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/canny/tuner_conf_template.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-+++++
-conf1 1.5 90 1.0 2.0
-1 gpu reduce fp16 1
-2 gpu map1 fp16 1 
-3 gpu conv fp16 1
-4 gpu conv fp16 1
-5 gpu conv fp16 1
-6 gpu map2 fp16 1
-7 gpu reduce fp16 1
-8 gpu reduce fp16 1
-9 gpu map2 fp16 1
------
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/knobs.txt
deleted file mode 100644
index 15a06d67cbfc94f0add1221c4e07cc3c4a0459c5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/knobs.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,42,44,46
-12,42,44,46
-12
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/op_cost.txt
deleted file mode 100644
index 910bcf3579b7d66759a0198db07e4339ccc1aa08..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/op_cost.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-1672.868
-198.9254
-9876.189
-28158.14
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/tuner_conf_template.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/tuner_conf_template.txt
deleted file mode 100644
index 49cee56932256482dd15746fa369406eb7cba23b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/fft/tuner_conf_template.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-+++++
-conf1 1.5 90 1.0 2.0
-1 gpu conv fp32 1
-2 gpu reduce fp32 1
-3 gpu reduce fp32 1
-4 gpu map2 fp32 1
------
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs.txt
deleted file mode 100644
index ee2cd80cb6e33da5e97ffe2e842644d7a705cdff..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs.txt
+++ /dev/null
@@ -1,69 +0,0 @@
-fp32,11	-1	1.0	tensorConvolution	tensorConvApprox	dev	conv_fc_red
-fp16,12	-1	1.5	tensorConvolution	tensorConvApproxHalf2	install	conv_fc_red
-perf,121	1,2,0	2.0	tensorConvolution	tensorConvApprox	dev	conv
-perf,122	1,2,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
-perf,123	2,1,0	2.0	tensorConvolution	tensorConvApprox	dev	conv
-perf,124	2,1,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
-perf,125	1,3,0	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,126	1,3,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,127	1,3,2	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,128	3,1,0	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,129	3,1,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,130	3,1,2	1.5	tensorConvolution	tensorConvApprox	dev	conv
-perf,131	1,4,0	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,132	1,4,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,133	1,4,2	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,134	1,4,3	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,135	4,1,0	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,136	4,1,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,137	4,1,2	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf,138	4,1,3	1.33	tensorConvolution	tensorConvApprox	dev	conv
-perf_fp16,151	1,2,0	3.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,152	1,2,1	3.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,153	2,1,0	3.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,154	2,1,1	3.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,155	1,3,0	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,156	1,3,1	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,157	1,3,2	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,158	3,1,0	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,159	3,1,1	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,160	3,1,2	2.25	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,161	1,4,0	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,162	1,4,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,163	1,4,2	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,164	1,4,3	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,165	4,1,0	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,166	4,1,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,167	4,1,2	2.0	tensorConvolution	tensorConvApprox	install	conv
-perf_fp16,168	4,1,3	2.0	tensorConvolution	tensorConvApprox	install	conv
-samp,231	2,0,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
-samp,232	2,1,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
-samp,233	3,0,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
-samp,234	3,1,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
-samp,235	3,2,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
-samp,236	4,0,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-samp,237	4,1,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-samp,238	4,2,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-samp,239	4,3,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
-samp_fp16,261	2,0,1	3.0	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,262	2,1,1	3.0	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,263	3,0,1	2.25	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,264	3,1,1	2.25	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,265	3,2,1	2.25	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,266	4,0,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,267	4,1,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,268	4,2,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-samp_fp16,269	4,3,1	2.0	tensorConvolution	tensorConvApprox	install	conv
-red_samp,41	1	1.5	tensorReduction		tensorReduction		dev	red
-red_samp,42	1	2.25	tensorReduction		tensorReduction		dev	red
-red_samp,43	1	1.4	tensorReduction		tensorReduction		dev	red
-red_samp,44	1	2	tensorReduction		tensorReduction		dev	red
-red_samp,45	1	1.25	tensorReduction		tensorReduction		dev	red
-red_samp,46	1	1.8	tensorReduction		tensorReduction		dev	red
-swing_level,1	1	12	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,2	1	10	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,3	1	9	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,4	1	8	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,5	1	6	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,6	1	5	tensorConvolution	tensorConvApprox	install	conv_fc
-swing_level,7	1	4	tensorConvolution	tensorConvApprox	install	conv_fc
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_dnn.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_dnn.txt
deleted file mode 100644
index 2180997527410cfdbf577a116fd39a592e2af05b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_dnn.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-fp32,11	-1	1.0	tensorConvolution	tensorConvolution
-fp16,12	-1	1.5	tensorConvolution	tensorHalfConvolution
-perf,21	1,2,0	2.25	tensorConvolution	tensorConvPerfCuda
-perf,22	1,2,1	2.25	tensorConvolution	tensorConvPerfCuda
-perf,23	1,3,0	1.88	tensorConvolution	tensorConvPerfCuda
-perf,24	1,3,1	1.88	tensorConvolution	tensorConvPerfCuda
-perf,25	1,3,2	1.88	tensorConvolution	tensorConvPerfCuda
-perf,26	2,1,0	2.25	tensorConvolution	tensorConvPerfCuda
-perf,27	2,1,1	2.25	tensorConvolution	tensorConvPerfCuda
-perf,28	3,1,0	1.88	tensorConvolution	tensorConvPerfCuda
-perf,29	3,1,1	1.88	tensorConvolution	tensorConvPerfCuda
-perf,30	3,1,2	1.88	tensorConvolution	tensorConvPerfCuda
-samp,31	2,0	2.25	tensorConvolution	tensorConvSampSim
-samp,32	2,1	2.25	tensorConvolution	tensorConvSampSim
-samp,33	4,0	1.8	tensorConvolution	tensorConvSampSim
-samp,34	4,1	1.8	tensorConvolution	tensorConvSampSim
-samp,35	4,2	1.8	tensorConvolution	tensorConvSampSim
-samp,36	4,3	1.8	tensorConvolution	tensorConvSampSim
-swing_level,1	1	12
-swing_level,2	1	10
-swing_level,3	1	9
-swing_level,4	1	8
-swing_level,5	1	6
-swing_level,6	1	5
-swing_level,7	1	4
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_old.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_old.txt
deleted file mode 100644
index c632abbd478101158063879706d6baf93852c8ef..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/global_knobs_old.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-fp32,11	-1	1.0	tensorConvolution	tensorConvolution
-fp16,12	-1	1.5	tensorConvolution	tensorHalfConvolution
-perf,21	1,2,0	2.0	tensorConvolution	tensorConvPerfCuda
-perf,22	1,2,1	2.0	tensorConvolution	tensorConvPerfCuda
-perf,23	1,3,0	1.5	tensorConvolution	tensorConvPerfCuda
-perf,24	1,3,1	1.5	tensorConvolution	tensorConvPerfCuda
-perf,25	1,3,2	1.5	tensorConvolution	tensorConvPerfCuda
-perf,26	2,1,0	2.0	tensorConvolution	tensorConvPerfCuda
-perf,27	2,1,1	2.0	tensorConvolution	tensorConvPerfCuda
-perf,28	3,1,0	1.5	tensorConvolution	tensorConvPerfCuda
-perf,29	3,1,1	1.5	tensorConvolution	tensorConvPerfCuda
-perf,30	3,1,2	1.5	tensorConvolution	tensorConvPerfCuda
-samp,31	2,0	2.0	tensorConvolution	tensorConvSampSim
-samp,32	2,1	2.0	tensorConvolution	tensorConvSampSim
-samp,33	4,0	1.5	tensorConvolution	tensorConvSampSim
-samp,34	4,1	1.5	tensorConvolution	tensorConvSampSim
-samp,35	4,2	1.5	tensorConvolution	tensorConvSampSim
-samp,36	4,3	1.5	tensorConvolution	tensorConvSampSim
-reduction_samp,41	1	1.5
-reduction_samp,42	1	2.25
-reduction_samp,43	1	1.4
-reduction_samp,44	1	2
-reduction_samp,45	1	1.25
-reduction_samp,46	1	1.8
-swing_level,1	1	12
-swing_level,2	1	10
-swing_level,3	1	9
-swing_level,4	1	8
-swing_level,5	1	6
-swing_level,6	1	5
-swing_level,7	1	4
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/dev_knobs.txt
deleted file mode 100644
index 94b9e6ebd34d115c62f075f2f12553284fdd981d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/dev_knobs.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs.txt
deleted file mode 100644
index 8973c89f7a89f9c62c12f8371d16eebad7264b31..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs1.txt
deleted file mode 100644
index be1ce58c95981535ec94a7f8badffe967cfed586..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/knobs1.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_layers.txt
deleted file mode 100644
index 5c28aa6dca176e1b0ef00fcad2fecf8024c76563..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_layers.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-conv  add  pool  tanh  
-conv  add  pool  tanh  
-dense  add  tanh
-dense  add  tanh
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_tensors.txt
deleted file mode 100644
index f26403376fd23964ab11743d4c667860126f1581..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/lenet_tensors.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-#Conv1,4
-Conv
-Add
-Relu
-Pool
-#Conv2,4
-Conv
-Add
-Relu
-Pool
-#FC1,3
-Mul
-Add
-Relu
-#FC1,3
-Mul
-Add
-Relu
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/op_cost.txt
deleted file mode 100644
index 74b1b668e2f27f3ddb77dcac7fff9890c70a6f02..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/lenet/op_cost.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-62720.000000
-1003520.000000
-321126.406250
-1024.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/dev_knobs.txt
deleted file mode 100644
index 7e8de16a0800979f707099559dc14cfd003140b3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/dev_knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs.txt
deleted file mode 100644
index 900ad3944d5203d4552a75140358388c99bea181..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs1.txt
deleted file mode 100644
index 6719acb97a58bd7f3d9fbe428f755e13df98b3d0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/knobs1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_layer_comp.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_layer_comp.txt
deleted file mode 100644
index adcfbfed538bedeb1d947039943dcb2dfca5e548..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_layer_comp.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-pool_mean  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_ops.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_ops.txt
deleted file mode 100644
index 6481664b869927a6b40f14d46e2e56c07068456a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/mobilenet_ops.txt
+++ /dev/null
@@ -1,167 +0,0 @@
-#Conv1,1
-Conv1
-#NML1,1
-BatchNorm1
-#NML2,1
-Relu1
-#NML3,1
-Conv2
-#NML4,1
-BatchNorm2
-#NML5,1
-Relu2
-#Conv3,1
-Conv3
-#NML6,1
-BatchNorm3
-#NML7,1
-Relu3
-#NML8,1
-Conv4
-#NML9,1
-BatchNorm4
-#NML10,1
-Relu4
-#Conv5,1
-Conv5
-#NML11,1
-BatchNorm5
-#NML12,1
-Relu5
-#NML13,1
-Conv6
-#NML14,1
-BatchNorm6
-#NML15,1
-Relu6
-#Conv7,1
-Conv7
-#NML16,1
-BatchNorm7
-#NML17,1
-Relu7
-#NML18,1
-Conv8
-#NML19,1
-BatchNorm8
-#NML20,1
-Relu8
-#Conv9,1
-Conv9
-#NML21,1
-BatchNorm9
-#NML22,1
-Relu9
-#NML23,1
-Conv10
-#NML24,1
-BatchNorm10
-#NML25,1
-Relu10
-#Conv11,1
-Conv11
-#NML26,1
-BatchNorm11
-#NML27,1
-Relu11
-#NML28,1
-Conv12
-#NML29,1
-BatchNorm12
-#NML30,1
-Relu12
-#Conv13,1
-Conv13
-#NML31,1
-BatchNorm13
-#NML32,1
-Relu13
-#NML33,1
-Conv14
-#NML34,1
-BatchNorm14
-#NML35,1
-Relu14
-#Conv15,1
-Conv15
-#NML36,1
-BatchNorm15
-#NML37,1
-Relu15
-#NML38,1
-Conv16
-#NML39,1
-BatchNorm16
-#NML40,1
-Relu16
-#Conv17,1
-Conv17
-#NML41,1
-BatchNorm17
-#NML42,1
-Relu17
-#NML43,1
-Conv18
-#NML44,1
-BatchNorm18
-#NML45,1
-Relu18
-#Conv19,1
-Conv19
-#NML46,1
-BatchNorm19
-#NML47,1
-Relu19
-#NML48,1
-Conv20
-#NML49,1
-BatchNorm20
-#NML50,1
-Relu20
-#Conv21,1
-Conv21
-#NML51,1
-BatchNorm21
-#NML52,1
-Relu21
-#NML53,1
-Conv22
-#NML54,1
-BatchNorm22
-#NML55,1
-Relu22
-#Conv23,1
-Conv23
-#NML56,1
-BatchNorm23
-#NML57,1
-Relu23
-#NML58,1
-Conv24
-#NML59,1
-BatchNorm24
-#NML60,1
-Relu24
-#Conv25,1
-Conv25
-#NML61,1
-BatchNorm25
-#NML62,1
-Relu25
-#NML63,1
-Conv26
-#NML64,1
-BatchNorm26
-#NML65,1
-Relu26
-#Conv27,1
-Conv27
-#NML66,1
-BatchNorm27
-#NML67,1
-Relu27
-#NML68,1
-Pool1
-#FC1,2
-Mul1
-Add1
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/op_cost.txt
deleted file mode 100644
index 673e704b7e37e19c090e98799189a4411bad9f7c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet/op_cost.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-88473.601562
-29491.199219
-209715.203125
-14745.599609
-209715.203125
-29491.199219
-419430.406250
-7372.799805
-209715.203125
-14745.599609
-419430.406250
-3686.399902
-209715.203125
-7372.799805
-419430.406250
-7372.799805
-419430.406250
-7372.799805
-419430.406250
-7372.799805
-419430.406250
-7372.799805
-419430.406250
-1843.199951
-209715.203125
-3686.399902
-419430.406250
-1024.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/dev_knobs.txt
deleted file mode 100644
index 9b93811d3b6aaca218ce83af9e03bcacf9fe62a0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/dev_knobs.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs.txt
deleted file mode 100644
index c7273f3fc6e487ada58eaed7bc036f707c3ce541..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs1.txt
deleted file mode 100644
index 719d96e48168a477d6edfee1a02b80b554612ec7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/knobs1.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_layer_comp.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_layer_comp.txt
deleted file mode 100644
index 8ba22c7e6f0268cf4f802576e04bdbe55b9efd15..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_layer_comp.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-pool_mean  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_ops.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_ops.txt
deleted file mode 100644
index ee6dcf89d959234cc5fdd3267d705dcd76db8250..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/mobilenet_shallow_ops.txt
+++ /dev/null
@@ -1,47 +0,0 @@
-#Conv1,1
-Conv1
-#NML1,1
-BatchNorm1
-#NML2,1
-Relu1
-#NML3,1
-Conv2
-#NML4,1
-BatchNorm2
-#NML5,1
-Relu2
-#Conv3,1
-Conv3
-#NML6,1
-BatchNorm3
-#NML7,1
-Relu3
-#NML8,1
-Conv4
-#NML9,1
-BatchNorm4
-#NML10,1
-Relu4
-#Conv5,1
-Conv5
-#NML11,1
-BatchNorm5
-#NML12,1
-Relu5
-#NML13,1
-Conv6
-#NML14,1
-BatchNorm6
-#NML15,1
-Relu6
-#Conv7,1
-Conv7
-#NML16,1
-BatchNorm7
-#NML17,1
-Relu7
-#NML18,1
-Pool1
-#FC1,2
-Mul1
-Add1
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/op_cost.txt
deleted file mode 100644
index 7266441905a08c1ef1796dec8ee6c05660998378..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_shallow/op_cost.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-265420.812500
-629145.625000
-629145.625000
-1258291.250000
-629145.625000
-1258291.250000
-629145.625000
-6144.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/layers.txt
deleted file mode 100644
index a93fac1daed00254fca84258bc92e7788390fd93..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/layers.txt
+++ /dev/null
@@ -1,81 +0,0 @@
-conv
-batchnorm
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-dense  add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/op_cost.txt
deleted file mode 100644
index 44d50dbe00baba66bd76bb7a0d2a9f37b8580fd4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/mobilenet_torch/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-44236.80078
-104857.6019
-104857.6019
-209715.2037
-104857.6019
-209715.2037
-104857.6019
-209715.2037
-209715.2037
-209715.2037
-209715.2037
-209715.2037
-104857.6019
-209715.2037
-256.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/dev_knobs.txt
deleted file mode 100644
index 2a07f89372edf02499c9e4462290d2495da5bfee..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/dev_knobs.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs.txt
deleted file mode 100644
index eadcb5ebff73feb75b9f7533f7703252ab895afc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs1.txt
deleted file mode 100644
index b7ff033cec2b85390ce6c7667fbbb04837a7eaf9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs1.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs2.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs2.txt
deleted file mode 100644
index ec3e26a51f1bbfb29436aa532b493c22557e31d7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/knobs2.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/op_cost.txt
deleted file mode 100644
index fdba070cfc5eac559c8384306993fb52a1eb2e04..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/op_cost.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-44236.800781
-235929.593750
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-235929.593750
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-235929.593750
-235929.593750
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_layers.txt
deleted file mode 100644
index 43f00249253e6f4375153ff2309c470f4923a1d0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv  add  activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-conv  add  activation  
-conv  add  
-add  
-activation  
-pool_mean  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_tensors.txt
deleted file mode 100644
index ee0e8456daf66eb07bf3200d3f4ab076534f6634..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet/resnet_tensors.txt
+++ /dev/null
@@ -1,114 +0,0 @@
-#Conv1,3
-Conv
-Add
-Relu
-#Conv2,3
-Conv
-Add
-Relu
-#Conv3,2
-Conv
-Add
-#NML1,1
-Add
-#NML2,1
-Relu
-#Conv4,3
-Conv
-Add
-Relu
-#Conv5,2
-Conv
-Add
-#NML3,1
-Add
-#NML4,1
-Relu
-#Conv6,3
-Conv
-Add
-Relu
-#Conv7,2
-Conv
-Add
-#NML5,1
-Add
-#NML6,1
-Relu
-#Conv8,3
-Conv
-Add
-Relu
-#Conv9,2
-Conv
-Add
-#Conv10,2
-Conv
-Add
-#NML7,1
-Add
-#NML8,1
-Relu
-#Conv11,3
-Conv
-Add
-Relu
-#Conv12,2
-Conv
-Add
-#NML9,1
-Add
-#NML10,1
-Relu
-#Conv13,3
-Conv
-Add
-Relu
-#Conv14,2
-Conv
-Add
-#NML11,1
-Add
-#NML12,1
-Relu
-#Conv15,3
-Conv
-Add
-Relu
-#Conv16,2
-Conv
-Add
-#Conv17,2
-Conv
-Add
-#NML13,1
-Add
-#NML14,1
-Relu
-#Conv18,3
-Conv
-Add
-Relu
-#Conv19,2
-Conv
-Add
-#NML15,1
-Add
-#NML16,1
-Relu
-#Conv20,3
-Conv
-Add
-Relu
-#Conv21,2
-Conv
-Add
-#NML17,1
-Add
-#NML18,1
-Relu
-#NML19,1
-Pool
-#FC1,2
-Mul
-Add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/op_cost.txt
deleted file mode 100644
index 6fb1aef66aaa4a02c5eb6f9282753a43c629f203..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/op_cost.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-88473.60156
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/resnet_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/resnet_layers.txt
deleted file mode 100644
index 2e51c67842656762091f2465b2824235a9959723..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet18_torch/resnet_layers.txt
+++ /dev/null
@@ -1,59 +0,0 @@
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\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/dev_knobs.txt
deleted file mode 100644
index 44fabd399e9d114f8bbbf4b64822e85a334fc162..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/dev_knobs.txt
+++ /dev/null
@@ -1,54 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/op_cost.txt
deleted file mode 100644
index 51a116031c59a0e62d90861e31dda222a901156b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/op_cost.txt
+++ /dev/null
@@ -1,54 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/quant_ranges2.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/quant_ranges2.txt
deleted file mode 100644
index efd6050d42c41cc53bca6b8a1e37ecd476dc2c10..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/quant_ranges2.txt
+++ /dev/null
@@ -1,54 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_layers.txt
deleted file mode 100644
index f0b6ebedc9beccfc639b70433e30b172e2d44fea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_layers.txt
+++ /dev/null
@@ -1,172 +0,0 @@
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-batchnorm  
-activation  
-conv  add  
-batchnorm  
-activation  
-conv  add  
-batchnorm  
-add  
-activation  
-pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_tensors.txt
deleted file mode 100644
index 70ec0ff11101dd554842d0a721ca4aa772a3bab2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/resnet50_imagenet/resnet50_tensors.txt
+++ /dev/null
@@ -1,172 +0,0 @@
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-#tensorBatchNorm1
-Conv2,100,64,55,55,64,64,1,1
-#tensorBatchNorm2
-#tensorRelu1
-Conv3,100,64,55,55,64,64,3,3
-#tensorBatchNorm3
-#tensorRelu2
-Conv4,100,64,55,55,256,64,1,1
-#tensorBatchNorm4
-Conv5,100,64,55,55,256,64,1,1
-#tensorBatchNorm5
-#tensorAdd1
-#tensorRelu3
-Conv6,100,256,55,55,64,256,1,1
-#tensorBatchNorm6
-#tensorRelu4
-Conv7,100,64,55,55,64,64,3,3
-#tensorBatchNorm7
-#tensorRelu5
-Conv8,100,64,55,55,256,64,1,1
-#tensorBatchNorm8
-#tensorAdd2
-#tensorRelu6
-Conv9,100,256,55,55,64,256,1,1
-#tensorBatchNorm9
-#tensorRelu7
-Conv10,100,64,55,55,64,64,3,3
-#tensorBatchNorm10
-#tensorRelu8
-Conv11,100,64,55,55,256,64,1,1
-#tensorBatchNorm11
-#tensorAdd3
-#tensorRelu9
-Conv12,100,256,55,55,128,256,1,1
-#tensorBatchNorm12
-#tensorRelu10
-Conv13,100,128,28,28,128,128,3,3
-#tensorBatchNorm13
-#tensorRelu11
-Conv14,100,128,28,28,512,128,1,1
-#tensorBatchNorm14
-Conv15,100,256,55,55,512,256,1,1
-#tensorBatchNorm15
-#tensorAdd4
-#tensorRelu12
-Conv16,100,512,28,28,128,512,1,1
-#tensorBatchNorm16
-#tensorRelu13
-Conv17,100,128,28,28,128,128,3,3
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-#tensorRelu14
-Conv18,100,128,28,28,512,128,1,1
-#tensorBatchNorm18
-#tensorAdd5
-#tensorRelu15
-Conv19,100,512,28,28,128,512,1,1
-#tensorBatchNorm19
-#tensorRelu16
-Conv20,100,128,28,28,128,128,3,3
-#tensorBatchNorm20
-#tensorRelu17
-Conv21,100,128,28,28,512,128,1,1
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-#tensorAdd6
-#tensorRelu18
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-#tensorBatchNorm27
-Conv28,100,512,28,28,1024,512,1,1
-#tensorBatchNorm28
-#tensorAdd8
-#tensorRelu24
-Conv29,100,1024,14,14,256,1024,1,1
-#tensorBatchNorm29
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-Conv30,100,256,14,14,256,256,3,3
-#tensorBatchNorm30
-#tensorRelu26
-Conv31,100,256,14,14,1024,256,1,1
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-Conv34,100,256,14,14,1024,256,1,1
-#tensorBatchNorm34
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-Conv35,100,1024,14,14,256,1024,1,1
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-Conv38,100,1024,14,14,256,1024,1,1
-#tensorBatchNorm38
-#tensorRelu34
-Conv39,100,256,14,14,256,256,3,3
-#tensorBatchNorm39
-#tensorRelu35
-Conv40,100,256,14,14,1024,256,1,1
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-#tensorRelu36
-Conv41,100,1024,14,14,256,1024,1,1
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-#tensorRelu37
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-#tensorRelu38
-Conv43,100,256,14,14,1024,256,1,1
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-Conv47,100,1024,14,14,2048,1024,1,1
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-#tensorRelu42
-Conv48,100,2048,7,7,512,2048,1,1
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-#tensorRelu43
-Conv49,100,512,7,7,512,512,3,3
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-#tensorBatchNorm53
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-#tensorRelu48
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-FC1,100,2048,2048,1000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/dev_knobs.txt
deleted file mode 100644
index 8970b19bfb5f7a6fb093f1b36215c23742e6f599..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/dev_knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs.txt
deleted file mode 100644
index d238fa1036729f79cc66bdaa14667dcf16c60a9a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs1.txt
deleted file mode 100644
index fb54e7f077eaf27d7182e273fae31a867d8cbb9f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/knobs1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/op_cost.txt
deleted file mode 100644
index 5f58ebcc043915d28cf874a1f67e5b2637db1dfc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_layers.txt
deleted file mode 100644
index 79818d6f010035c6e19f12881749f4d5b3d3c253..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-dense  add  activation  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_tensors.txt
deleted file mode 100644
index a524e1e74e189c175b9f0e371ba04f4a6f452a1c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10/vgg16_tensors.txt
+++ /dev/null
@@ -1,64 +0,0 @@
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-Add
-Relu
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-Conv
-Add
-Relu
-Pool
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-Conv
-Add
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-Conv
-Add
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-Add
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/dev_knobs.txt
deleted file mode 100644
index 8970b19bfb5f7a6fb093f1b36215c23742e6f599..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/dev_knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs.txt
deleted file mode 100644
index d238fa1036729f79cc66bdaa14667dcf16c60a9a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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-12
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs1.txt
deleted file mode 100644
index fb54e7f077eaf27d7182e273fae31a867d8cbb9f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/knobs1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/op_cost.txt
deleted file mode 100644
index 8c6daad2e2902e3ac821d99ebbe12e21b6428cc7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_layers.txt
deleted file mode 100644
index 79818d6f010035c6e19f12881749f4d5b3d3c253..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
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-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-dense  add  activation  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_tensors.txt
deleted file mode 100644
index a524e1e74e189c175b9f0e371ba04f4a6f452a1c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar100/vgg16_tensors.txt
+++ /dev/null
@@ -1,64 +0,0 @@
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-Conv
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-Add
-Relu
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-Relu
-Pool
-#Conv11,3
-Conv
-Add
-Relu
-#Conv12,3
-Conv
-Add
-Relu
-#Conv13,4
-Conv
-Add
-Relu
-Pool
-#FC1,3
-Mul
-Add
-Relu
-#FC2,2
-Mul
-Add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/layers.txt
deleted file mode 100644
index ef3d0ebcf7c50b8a67a7c42cc71d4b69fe21fde2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/layers.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-pool
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-pool
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-pool
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-pool
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-conv  add
-batchnorm
-activation
-pool
-pool_mean
-dense  add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/op_cost.txt
deleted file mode 100644
index 10dc83f865f3cc4ec02e86d4ae9f689eaa143610..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_cifar10_torch/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-88473.60156
-1887436.833
-943718.4167
-1887436.833
-943718.4167
-1887436.833
-1887436.833
-943718.4167
-1887436.833
-1887436.833
-471859.2083
-471859.2083
-471859.2083
-13107.200195
-256.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/dev_knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/dev_knobs.txt
deleted file mode 100644
index 793b41f5b8f316daf96604e25be70b21fc115046..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/dev_knobs.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-11
-11
-11
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs.txt
deleted file mode 100644
index d238fa1036729f79cc66bdaa14667dcf16c60a9a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
-12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs1.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs1.txt
deleted file mode 100644
index fb54e7f077eaf27d7182e273fae31a867d8cbb9f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/knobs1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12,21,22,23,24,25,26,27,28,31,32,33,34
-11,12
-11,12
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/op_cost.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/op_cost.txt
deleted file mode 100644
index 77754f5f8b03634faba7e933eea8d9c05f6e58ee..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/op_cost.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-88473.601562
-1887436.750000
-943718.375000
-1887436.750000
-943718.375000
-1887436.750000
-1887436.750000
-943718.375000
-1887436.750000
-1887436.750000
-471859.187500
-471859.187500
-471859.187500
-13107.200195
-13107.200195
-256.000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_layers.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_layers.txt
deleted file mode 100644
index bfa2a2700a164cf135541c560cdf1499f584b7a1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_layers.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-dense  add  activation
-dense  add  activation
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_tensors.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_tensors.txt
deleted file mode 100644
index b9afe8dd34f5b26d71f215eb5e09ea08ed4c76b6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/vgg16_tensors.txt
+++ /dev/null
@@ -1,68 +0,0 @@
-#Conv1,3
-Conv
-Add
-Relu
-#Conv2,4
-Conv
-Add
-Relu
-Pool
-#Conv3,3
-Conv
-Add
-Relu
-#Conv4,4
-Conv
-Add
-Relu
-Pool
-#Conv5,3
-Conv
-Add
-Relu
-#Conv6,3
-Conv
-Add
-Relu
-#Conv7,4
-Conv
-Add
-Relu
-Pool
-#Conv8,3
-Conv
-Add
-Relu
-#Conv9,3
-Conv
-Add
-Relu
-#Conv10,4
-Conv
-Add
-Relu
-Pool
-#Conv11,3
-Conv
-Add
-Relu
-#Conv12,3
-Conv
-Add
-Relu
-#Conv13,4
-Conv
-Add
-Relu
-Pool
-#FC1,3
-Mul
-Add
-Relu
-#FC2,3
-Mul
-Add
-Relu
-#FC3,2
-Mul
-Add
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/.gitignore
deleted file mode 100644
index 9eb809777b0bcfdb2a7d91f9e671282ca03610a7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/.gitignore
+++ /dev/null
@@ -1,51 +0,0 @@
-*.py[cod]
-
-# C extensions
-*.so
-
-# Packages
-*.egg
-*.egg-info
-dist
-build
-eggs
-parts
-bin
-var
-sdist
-develop-eggs
-.installed.cfg
-lib
-lib64
-
-# Installer logs
-pip-log.txt
-
-# Unit test / coverage reports
-.coverage
-.tox
-nosetests.xml
-
-# Translations
-*.mo
-
-# Mr Developer
-.mr.developer.cfg
-.project
-.pydevproject
-
-#vim
-*.swp
-
-#virtualenv
-venv
-.ropeproject
-opentuner.log
-.*.swo
-opentuner.db
-.idea
-
-# SMB ROM (for SMB demo)
-smb.nes
-
-MANIFEST
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/AUTHORS.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/AUTHORS.txt
deleted file mode 100644
index 620e549e236ad322446694d11dc68a2f39a3ee31..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/AUTHORS.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-Jason Ansel
-Sam Fingeret
-Shoaib Kamil
-Deepak Narayanan
-Jonathan Ragan-Kelley
-Kalyan Veeramachaneni
-Kevin Wu
-Minshu Zhan
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/CHANGES.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/CHANGES.txt
deleted file mode 100644
index a0af44222226f64cceb85bb633072a25abb40777..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/CHANGES.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-v0.5.0, 2015-02-10 -- Refactoring and bugfixes.
-v0.4.0, 2014-10-26 -- Add api and bugfixes.
-v0.3.0, 2014-08-11 -- Initial release.
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/LICENSE.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/LICENSE.txt
deleted file mode 100644
index 2b602e192b4a2302cae3288e3bd34746ff8475df..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/LICENSE.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2014 Jason Ansel
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/MANIFEST.in b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/MANIFEST.in
deleted file mode 100644
index 376b77ae8f44a06787d5910191c713c986791a72..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/MANIFEST.in
+++ /dev/null
@@ -1 +0,0 @@
-include *.txt *.md
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/README.md b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/README.md
deleted file mode 100644
index 729f35553a0fe22177a38f0545d03a4497bef03c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-OpenTuner
-=========
-
-Program autotuning has been demonstrated in many domains to achieve better
-or more portable performance.  However, autotuners themselves are often not
-very portable between projects because using a domain informed search space
-representation is critical to achieving good results and because no single
-search technique performs best for all problems.
-
-OpenTuner is a new framework for building domain-specific multi-objective
-program autotuners. OpenTuner supports fully customizable configuration
-representations, an extensible technique representation to allow for
-domain-specific techniques, and an easy to use interface for communicating
-with the tuned program. A key capability inside OpenTuner is the use of
-ensembles of disparate search techniques simultaneously, techniques which
-perform well will receive larger testing budgets and techniques which perform
-poorly will be disabled.
-
-System dependencies
--------------------
-
-A list of system dependencies can be found in [debian-packages-deps][]
-which are primarily python 2.6+ (not 3.x) and sqlite3 (or your
-[supported][sqlalchemy-dialects] database backend of choice).
-
-On Ubuntu/Debian there can be installed with:
-
-    sudo apt-get install `cat debian-packages-deps | tr '\n' ' '`
-
-[debian-packages-deps]: https://raw.github.com/jansel/opentuner/master/debian-packages-deps
-[sqlalchemy-dialects]: http://docs.sqlalchemy.org/en/rel_0_8/dialects/index.html
-
-
-Installation
--------------------
-OpenTuner (and dependencies) can be installed with
-
-    sudo pip install opentuner
-
-or
-
-    pip install --user opentuner
-
-This will not install any of the example programs.
-
-
-Development installation
--------------------
-For development (running OpenTuner out of a git checkout), a list of python
-dependencies can be found in [requirements.txt][] these can be installed
-system-wide with `pip`.
-
-    sudo apt-get install python-pip
-    sudo pip install -r requirements.txt
-
-Or you can use virtual env to create a isolated python environment by running:
-
-    python ./venv-bootstrap.py
-
-which will create a ./venv/bin/python (./venv/Scripts/python.exe on windows)
-with all the required packages installed.
-
-[requirements.txt]: https://raw.github.com/jansel/opentuner/master/requirements.txt
-
-
-Checking Installation
----------------------
-
-Quickly checking that a successful installation has been made, may be performed
-by running an example program such as:
-
-    ./examples/rosenbrock/rosenbrock.py
-
-
-Tutorials
----------
-
-- [Optimizing Block Matrix Multiplication][gettingstarted]
-- [Creating OpenTuner Techniques][technique-tutorial].
-
-[gettingstarted]: http://opentuner.org/tutorial/gettingstarted/
-[technique-tutorial]:  http://opentuner.org/tutorial/techniques/
-
-
-Papers
----------
-
-- [OpenTuner: An Extensible Framework for Program Autotuning][paper1]. <br>
-  Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley,
-  Jeffrey Bosboom, Una-May O'Reilly, Saman Amarasinghe. <br>
-  International Conference on Parallel Architectures and Compilation
-  Techniques. <br>
-  Edmonton, Canada. August, 2014. [Slides][slides1]. [Bibtex][bibtex1].
-
-[paper1]: http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf
-[bibtex1]: http://groups.csail.mit.edu/commit/bibtex.cgi?key=ansel:pact:2014
-[slides1]: http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf
-
-
-Contributing Code
------------------
-
-The preferred way to contribute code to OpenTuner is to fork the project
-on github and [submit a pull request][pull-req].
-
-[pull-req]: https://www.openshift.com/wiki/github-workflow-for-submitting-pull-requests
-
-
-Support
--------
-OpenTuner is supported in part by the United States Department of Energy
-[X-Stack][xstack] program as part of [D-TEC][dtec].
-
-[xstack]: http://science.energy.gov/ascr/research/computer-science/ascr-x-stack-portfolio/
-[dtec]: http://www.dtec-xstack.org/
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner.py
deleted file mode 100644
index 5977fe7ee5b4780139d2c5a865c8231361cf0f2c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner.py
+++ /dev/null
@@ -1,198 +0,0 @@
-#!/usr/bin/env python
-#
-
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-  
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-    """
-    Compile and run a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    run_result_call_program = self.call_program(run_cmd)
-    #print run_result_call_program
-
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-    
-    return Result
-         
-
-  def save_final_config(self, configuration):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir  
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner_piped.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner_piped.py
deleted file mode 100644
index 6d46c5762ead377292337c47d045ee5e58322954..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/accuracy_tuner_piped.py
+++ /dev/null
@@ -1,269 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-
-
-
-def kill(proc_pid):
-  process = psutil.Process(proc_pid)
-  for proc in process.children(recursive=True):
-    proc.kill()
-  process.kill()
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    FNULL = open(os.devnull, 'wb')
-    #run_result_call_program = self.call_program(run_cmd)
-    self.start_process = subprocess.Popen([binary_name, "opentuner_run"]) #,  stdout=FNULL);
-
-    try:
-      os.mkfifo("/tmp/myfifo")
-    except OSError, e:
-      print("FIFO exists")
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-
-    """
-    Run  a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    #run_result_call_program = self.call_program(run_cmd)      
-  
-    # Using Named Pipes to signal execution to the DNN outer thread
-    fifo = open("/tmp/myfifo", "w")
-    fifo.write("start_run")
-    fifo.close()
-
-    print "Waiting for process to signal back - when done processing one run"
-
-    fifo2 = open("/tmp/myfifo", "r")
-    fifo2.read()
-    fifo2.close()
-
-    print "Process Signalled back"
-
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-        
-    print "done with one run"
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results"
-    sleep(5)
-    
-    # Only dumping files with 95% confidence
-    dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
-    select_top_results(orig_result_dir + "/high_confidence")
-
-    
-    #self.start_process.kill()
-    kill(self.start_process.pid)
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/adddeps.py
deleted file mode 100644
index 61fd4757d6a6045346e5cdcd3dfbfcdc00e236fa..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/adddeps.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner.py
deleted file mode 100644
index b8145e179893bc0db2631cf1f7ee0f11bcc9be0e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps  
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
-  def __init__(self):
-    self.binary_path = ""
-    self.output_dir = ""
-    self.num_layers = 0
-    self.knobs_list = []
-    self.knobs_speedup = {}
-    self.accuracy_threshold = 0
-    self.test_id = 0
-    self.layer_costs = []
-    self.tuning_flags = []
-    self.autotuner_runs = 0
-    
-
-
-tunerData = TunerData()
-
-
-def readCostFile(file_path):
-
-  layer_costs = []
-  f = open(file_path)
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  print ("len(layer_costs) = ", layer_costs)
-  f.close()
-
-  return layer_costs
-
-  
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print (accuracy)
-  return accuracy
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for i in range(tunerData.num_layers):  # flag in tunerData.tuning_flags:
-    flag = tunerData.tuning_flags[i]
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-  
-
-def readLayerKnobs(file_path):
-
-  f = open(file_path, "r")
-  knobs_list = []
-  for x in f:
-    knobs = []
-    vals = x.split(",")
-    for val in vals:
-      knobs.append(int(val))
-      
-    knobs_list.append(knobs)
-
-  print ("knobs_list = ", knobs_list)
-  
-  return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
-  knobs_speedup = {}
-  f = open(file_path, "r")
-  for x in f:
-    toks = x.split("\t")
-    ID = int(toks[0].split(",")[1])
-
-    speedup = float(toks[2])
-    knobs_speedup[ID] = speedup
-  
-  print ("knobs_speedup = ", knobs_speedup)
-  
-  return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
-  orig_cost = 0.0
-  total_cost = 0.0
-  for it in range(tunerData.num_layers):
-    flag = tunerData.tuning_flags[it]
-    flag_value = cfg[flag]
-    op_cost = tunerData.layer_costs[it]
-    speedup = tunerData.knobs_speedup[flag_value]
-
-    total_cost += (op_cost * 1.0 / speedup * 1.0)
-    orig_cost += op_cost
-    
-    it += 1
-
-  speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-  
-  return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
-  f_str = open(f_path, "r").read()
-
-  f_out = open(f_path, "w+")
-
-  f_out.write("total_runs=" + str(total_runs) + "\tconfidence=100.0" + "\tavg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
-  f_out.write(f_str)
-
-  f_out.close()
-      
-
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
-    input_manager = FixedInputManager(size=tunerData.num_layers)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    for i in range(tunerData.num_layers):
-      tunerData.tuning_flags.append("flag" + str(i))
-
-         
-    #for flag in tunerData.tuning_flags:
-    for ind in range(tunerData.num_layers):
-        flag = tunerData.tuning_flags[ind]
-        manipulator.add_parameter(
-        EnumParameter(flag, tunerData.knobs_list[ind]))
-
-        print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
-        print (tunerData.knobs_list[ind])
-        ind += 1  
-      
-    return manipulator
-
-  
-  
-  def run(self, desired_result, input, limit):
-    
-    """
-    Run  a given configuration then
-    return performance
-    """
-    global test_id
-    
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("promise_flags", cfg)
-    
-    run_cmd = tunerData.binary_path
-    print "\nbinary_path = ", run_cmd
- 
-
-    total_runs = 1 # NOTE: Single run sufficient in Algorithmic Approx Tuner
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
-    p.wait()
-
-       
-    accuracy = getAccuracy("final_accuracy")
-    
-    # getConfigCost returns the cost associated with the selected configuration
-    total_comps, speedup = getConfigCost(cfg)
-   
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    #Result.accuracy = accuracy
-    min_accuracy = getMinAccuracy("run_accuracies.txt")
-    print ("min_accuracy = ", min_accuracy)
-    Result.accuracy = min_accuracy
-    
-    if min_accuracy > tunerData.accuracy_threshold:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
-      shutil.copy('promise_flags', f_path)
-
-      appendTopLine(f_path, accuracy, total_runs, total_comps, speedup)
-
-      f_acc = open(tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id) + "_accuracy", "w")
-      f_acc.write(str(accuracy))
-      f_acc.close()
-                   
-      
-    tunerData.test_id += 1
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Done with Autotuning Run \n"
-    sleep(2)
-
-    print "Final configuration", configuration.data
-
-    return
-
-  
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--cost-file', help='layer description')
-  argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
-  argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-  
-  
-  args = argparser.parse_args()
-
-  tunerData.binary_path = str(args.binary)
-  tunerData.num_layers = int(args.num_layers)
-  tunerData.accuracy_threshold = float(args.accuracy)
-
-
-  # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
-  cost_file_path = args.cost_file
-  tunerData.layer_costs = readCostFile(cost_file_path)
-
-  
-  tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
-  tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-  
-  result_dir = args.result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-        
-  tunerData.output_dir = result_dir + "/high_confidence/"
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(tunerData.output_dir):
-    print("Creating output directory = ", tunerData.output_dir)
-    os.mkdir(tunerData.output_dir)
-
-
-    
-  ClangFlagsTuner.main(argparser.parse_args())
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner2.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner2.py
deleted file mode 100644
index 4ca0062f93441954d3ee0acc0eabf10352e3a76c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/algo_tuner2.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps  
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files4
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
-  def __init__(self):
-    self.binary_path = ""
-    self.output_dir = ""
-    self.num_layers = 0
-    self.knobs_list = []
-    self.knobs_speedup = {}
-    self.accuracy_threshold = 0
-    self.test_id = 0
-    self.layer_costs = []
-    self.tuning_flags = []
-    self.autotuner_runs = 0
-    
-
-
-tunerData = TunerData()
-
-
-orig_result_dir = ""
-
-
-def readCostFile(file_path):
-
-  layer_costs = []
-  f = open(file_path)
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  print ("len(layer_costs) = ", layer_costs)
-  f.close()
-
-  return layer_costs
-
-  
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print (accuracy)
-  return accuracy
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for i in range(tunerData.num_layers):  # flag in tunerData.tuning_flags:
-    flag = tunerData.tuning_flags[i]
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-  
-
-def readLayerKnobs(file_path):
-
-  f = open(file_path, "r")
-  knobs_list = []
-  for x in f:
-    knobs = []
-    vals = x.split(",")
-    for val in vals:
-      knobs.append(int(val))
-      
-    knobs_list.append(knobs)
-
-  print ("knobs_list = ", knobs_list)
-  
-  return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
-  knobs_speedup = {}
-  f = open(file_path, "r")
-  for x in f:
-    toks = x.split("\t")
-    ID = int(toks[0].split(",")[1])
-
-    speedup = float(toks[2])
-    knobs_speedup[ID] = speedup
-  
-  print ("knobs_speedup = ", knobs_speedup)
-  
-  return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
-  orig_cost = 0.0
-  total_cost = 0.0
-  for it in range(tunerData.num_layers):
-    flag = tunerData.tuning_flags[it]
-    flag_value = cfg[flag]
-    op_cost = tunerData.layer_costs[it]
-    speedup = tunerData.knobs_speedup[flag_value]
-
-    total_cost += (op_cost * 1.0 / speedup * 1.0)
-    orig_cost += op_cost
-    
-    it += 1
-
-  speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-  
-  return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
-  f_str = open(f_path, "r").read()
-
-  f_out = open(f_path, "w+")
-
-  f_out.write("total_runs=" + str(total_runs) + "\tconfidence=100.0" + "\tavg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
-  f_out.write(f_str)
-
-  f_out.close()
-      
-
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
-    input_manager = FixedInputManager(size=tunerData.num_layers)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    for i in range(tunerData.num_layers):
-      tunerData.tuning_flags.append("flag" + str(i))
-
-         
-    #for flag in tunerData.tuning_flags:
-    for ind in range(tunerData.num_layers):
-        flag = tunerData.tuning_flags[ind]
-        manipulator.add_parameter(
-        EnumParameter(flag, tunerData.knobs_list[ind]))
-
-        print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
-        print (tunerData.knobs_list[ind])
-        ind += 1  
-      
-    return manipulator
-
-  
-  
-  def run(self, desired_result, input, limit):
-    
-    """
-    Run  a given configuration then
-    return performance
-    """
-    global test_id
-    
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("promise_flags", cfg)
-    
-    run_cmd = tunerData.binary_path
-    print "\nbinary_path = ", run_cmd
-
-
-    input_size = 5000
-    offset = 5000
-
-    total_runs = 2 # NOTE: Single run sufficient in Algorithmic Approx Tuner
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen([run_cmd, str(total_runs), str(tunerData.accuracy_threshold), str(1), str(input_size), str(offset) ], stdout = FNULL)
-    p.wait()
-
-    #total_runs = 2 # NOTE: Atleast two runs for promise tuner
-    #FNULL = open(os.devnull, 'wb')
-    #p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
-    #p.wait()
-
-       
-    accuracy = getAccuracy("final_accuracy")
-    
-    # getConfigCost returns the cost associated with the selected configuration
-    total_comps, speedup = getConfigCost(cfg)
-   
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    #Result.accuracy = accuracy
-    min_accuracy = getMinAccuracy("run_accuracies.txt")
-    print ("min_accuracy = ", min_accuracy)
-    Result.accuracy = min_accuracy
-    
-    if min_accuracy > tunerData.accuracy_threshold:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
-      shutil.copy('promise_flags', f_path)
-
-      appendTopLine(f_path, accuracy, total_runs, total_comps, speedup)
-
-      
-    tunerData.test_id += 1
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Done with Autotuning Run \n"
-    sleep(2)
-
-
-    #findParetoConfigs(orig_result_dir, layer_costs, accuracy_threshold)
-
-    input_dir = orig_result_dir + "/full_results/"
-    output_dir = orig_result_dir + "/high_confidence/"
-    
-    # Only dumping files with 95% confidence
-    dump_promise_confidence_files4(tunerData.binary_path, input_dir, output_dir, tunerData.layer_file, tunerData.num_layers, tunerData.accuracy_threshold, tunerData.layer_costs, 95, tunerData.knobs_speedup)
-
-    
-    print "Final configuration", configuration.data
-
-    return
-
-  
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-
-  argparser.add_argument('--layer-file', help='layer file')
-
-  argparser.add_argument('--cost-file', help='layer description')
-  argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
-  argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-  
-  
-  args = argparser.parse_args()
-
-  tunerData.binary_path = str(args.binary)
-  tunerData.num_layers = int(args.num_layers)
-  tunerData.accuracy_threshold = float(args.accuracy)
-
-  tunerData.layer_file = args.layer_file
-
-  # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
-  cost_file_path = args.cost_file
-  tunerData.layer_costs = readCostFile(cost_file_path)
-
-  
-  tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
-  tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-  
-  result_dir = args.result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-  orig_result_dir = result_dir  
-  tunerData.output_dir = result_dir + "/full_results/"
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(tunerData.output_dir):
-    print("Creating output directory = ", tunerData.output_dir)
-    os.mkdir(tunerData.output_dir)
-
-
-    
-  ClangFlagsTuner.main(argparser.parse_args())
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/approxhpvm_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/approxhpvm_tuner.py
deleted file mode 100644
index 9ae2266bf481a9dd772fd139b375463b35bcd1b9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/approxhpvm_tuner.py
+++ /dev/null
@@ -1,262 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def copyTunerRuntime():
-  tensor_rt_path = os.environ["LLVM_SRC_ROOT"]
-  if tensor_rt_path == "":
-    print "LLVM_SRC_ROOT NOT SET"
-    sys.exit(0)
-
-  print "tensor_rt_path = ", tensor_rt_path  
-
-  
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-
-
-
-def kill(proc_pid):
-  process = psutil.Process(proc_pid)
-  for proc in process.children(recursive=True):
-    proc.kill()
-  process.kill()
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-  
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-
-    """
-    Run  a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print "binary_name = ", run_cmd
-    #run_result_call_program = self.call_program(run_cmd)
-    #print "returned \n\n"
-
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen(run_cmd, stdout = FNULL)
-    p.wait()
-    
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-       
-    print "done with one run"
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results"
-    sleep(5)
-    
-    # Only dumping files with 95% confidence
-    dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
-    select_top_results(orig_result_dir + "/high_confidence")
-
-    
-    #self.start_process.kill()
-    kill(self.start_process.pid)
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/devtuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/devtuner.py
deleted file mode 100644
index 4d5da6afb6d95e1372c8dbea00fec07494c46426..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/devtuner.py
+++ /dev/null
@@ -1,331 +0,0 @@
-#!/usr/bin/env python
-#
-# Development-time Tuner with Algorithmic Approximations:
-# Approximations: Perforation, Sampling with varying knobs for rate, skip offset
-
-
-import adddeps  
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-import thread
-
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-import utils
-
-
-
-class TunerData:
-    def __init__(self):
-      self.binary_path = ""
-      self.output_dir = ""
-      self.num_layers = 0
-      self.knobs_list = []
-      self.knobs_speedup = {}
-      self.accuracy_threshold = 0
-      self.accuracy_slack = 0
-      self.test_id = 0
-      self.layer_costs = []
-      self.tuning_flags = []
-      self.autotuner_runs = 0
-      self.best_speedup = 1
-      self.log_file = ""
-
-      self.use_seed = True
-
-
-
-class DevTuner(MeasurementInterface):
-
-  
-  def initTunerData(self, args):
-
-    self.tunerData.binary_path = str(args.binary)
-    self.tunerData.num_layers = int(args.num_layers)
-    self.tunerData.accuracy_threshold = float(args.accuracy)
-    self.tunerData.accuracy_slack = float(args.accuracy_slack)
-
-    # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
-    cost_file_path = args.cost_file
-    self.tunerData.layer_costs = utils.readCostFile(cost_file_path)
-  
-    self.tunerData.knobs_list = utils.readLayerKnobs(args.layer_knobs)
-    self.tunerData.knobs_speedup = utils.readGlobalKnobConfig(args.knobs_config)
-    self.tunerData.test_id = args.start_id
-  
-    result_dir = args.result_dir
-    if result_dir == "":
-      print("Provide --result-dir ")
-        
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-
-    self.tunerData.output_dir = result_dir + "/high_confidence/" 
-    if not os.path.exists(self.tunerData.output_dir):
-      print("Creating output directory = ", self.tunerData.output_dir)
-      os.mkdir(self.tunerData.output_dir)
-
-
-  def createFIFO(self):
-    try:
-      os.mkfifo("/tmp/opentuner_fifo")
-    except OSError, e:
-      print("FIFO exists")
-
-
-  def invokeBinary(self, runs):
-
-    print ("\n\n\n\n SPAWNING BINARY *****\n\n***")
-    run_cmd = self.tunerData.binary_path
-    print "\nbinary_path = ", run_cmd    
-    FNULL = open(os.devnull, 'wb')
-    self.process = subprocess.Popen([run_cmd, str(runs)], stdout = FNULL)
-
-
-    
-  def signalPipe(self):
-
-      fifo = open("/tmp/opentuner_fifo", "w")
-      fifo.write("start_run")
-      fifo.close()
-      #print "Waiting for process to signal back - when done processing one run"
-
-
-
-  def pollOnProcess(self, self2):
-
-    print (" self.piped_execution = ", self.piped_execution, "*** \n")
-    sleep(5)
-    
-    while (not self.escape_poll_thread):
-      poll = self.process.poll()
-      #print ("POLLING")
-        
-      if poll is not None:  # If process aborted, invoke another instance
-        sleep(6)
-        
-        poll = self.process.poll()
-        if not utils.check_pid(self.process.pid) and poll is not None: # Second check for process existence
-          self.corrupted_run = True
-          utils.process_kill(self.process.pid) # Kill existing process if exists
-          self.invokeBinary(100000)
-          self.signalPipe()
-        
-      
-  
-  def waitOnPipe(self):
-      
-      fifo2 = open("/tmp/opentuner_fifo", "r")
-      fifo2.read()
-      fifo2.close()
-
-    
-
-  def stopProcess(self):
-
-      fifo = open("/tmp/opentuner_fifo", "w")
-      fifo.write("stop_run")
-      fifo.close()
-      print "***** Sending Stop Signal ***** "
-      
-      
-  def __init__(self, args):
-
-    #print ("\n\n\n\n\n\******* ARGS[0] = ", args)
-    
-    self.tunerData = TunerData()
-    self.initTunerData(args)
-
-    # Adding knob to use piped execution instead
-    self.piped_execution = True
-    self.corrupted_run = False
-    self.escape_poll_thread = False
-    
-    objective = ThresholdAccuracyMinimizeTime(self.tunerData.accuracy_threshold)
-    input_manager = FixedInputManager(size=self.tunerData.num_layers)
-    self.configs_list = []
-    # initializing tuner related data
-       
-    super(DevTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    log_path = self.tunerData.output_dir + "/log_file.txt"
-    self.log_file = open(log_path, "a+")
-    
-    
-    if self.piped_execution:
-      self.createFIFO()
-      self.invokeBinary(100000)
-      print ("Invoking thread to launch a Polling THREAD ")
-      sleep(10)
-      thread.start_new_thread(self.pollOnProcess, (self, ))  
-      
-      
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    for i in range(self.tunerData.num_layers):
-      self.tunerData.tuning_flags.append("flag" + str(i))
-
-      
-    for ind in range(self.tunerData.num_layers):
-        flag = self.tunerData.tuning_flags[ind]
-        manipulator.add_parameter(
-        EnumParameter(flag, self.tunerData.knobs_list[ind]))
-
-        print ("ind = ", ind, " len = ", len(self.tunerData.knobs_list))
-        print (self.tunerData.knobs_list[ind])
-        ind += 1  
-      
-    return manipulator
-
-
-
-  def seed_configurations(self):
-        """Provide baseline config as seed if model uses seed."""
-        baseline_config = {layer: 11 for layer in self.tunerData.tuning_flags}
-        return [baseline_config] if self.tunerData.use_seed else []
-
-  
-  
-  def run(self, desired_result, input, limit):
-    
-    """
-    Run  a given configuration then
-    return performance
-    """
-    global test_id
-    
-    cfg = desired_result.configuration.data
-
-
-    print ("cfg = ", cfg)
-    
-    # NOTE: creates flags file used by hpvm-tensor-rt
-    utils.genLayerFlagsFile("promise_flags", cfg, self.tunerData)
-    
-
-    total_runs = 1 # NOTE: Single run sufficient in Algorithmic Approx Tuner
-    if not self.piped_execution:    
-      run_cmd = self.tunerData.binary_path
-      print "\nbinary_path = ", run_cmd
-      FNULL = open(os.devnull, 'wb')
-      p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
-      p.wait()
-
-
-    waitSignal = 0
-    if self.piped_execution:
-      self.signalPipe()
-      waitSignal = self.waitOnPipe()
-      
-    accuracy = utils.readAccuracy("final_accuracy")
-
-    if self.corrupted_run == True:
-      accuracy = self.tunerData.accuracy_slack - 5  # Invalid Run
-      print ("\n\n\n **** Corrupted Run **** Accuracy = ", accuracy, "  --- \n\n\n")
-      self.corrupted_run = False
-    
-    # getConfigCost returns the cost associated with the selected configuration
-    total_comps, speedup = utils.computeConfigCost(cfg, self.tunerData)
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-    
-    
-    if accuracy > self.tunerData.accuracy_slack:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      f_path = self.tunerData.output_dir + '/' + self.tunerData.binary_path + '_' + str(self.tunerData.test_id)
-      shutil.copy('promise_flags', f_path)
-
-      utils.addInfoToOutFile(f_path, accuracy, total_runs, total_comps, speedup)
-
-      print ("------ Config Chosen with Accuracy = ", accuracy, " And Predicted Speedup = ", speedup, "\n")
-
-      if speedup > self.tunerData.best_speedup:
-          self.tunerData.best_speedup = speedup
-          
-
-
-    if self.tunerData.test_id % 100 == 0:
-        self.log_file.write("** iteration = " + str(self.tunerData.test_id) + \
-                            "  speedup = " + str(self.tunerData.best_speedup) + "   \n")
-
-    
-      
-      
-    self.tunerData.test_id += 1
-    
-    return Result
-
-
-
-  def save_final_config(self, configuration):
-
-    # Indication to terminate polling thread
-    self.escape_poll_thread = True  
-
-    if self.piped_execution:
-      #self.stopProcess()
-      utils.process_kill(self.process.pid)
-      print ("Killed hanging process")
-    
-    print "Final configuration", configuration.data
-
-    # Close log file
-    self.log_file.close()
-    
-    print "Done with Autotuning Run \n"
-    sleep(2)
-
-    return
-
-  
-
-
-if __name__ == '__main__':
-
-  
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--accuracy-slack', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--cost-file', help='layer description')
-  argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
-  argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-  # NOTE: needed to have unique file-names across runs
-  argparser.add_argument('--start-id', type=int, help='start id for naming output files')
-  
-  
-  args = argparser.parse_args()
-  #devTuner = DevTuner(args)
-  print ("\n\n\n\n\ -- NOTE --- \n\n")
-  DevTuner.main(argparser.parse_args())
-  
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/gettingstarted.md b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/gettingstarted.md
deleted file mode 100644
index 8a442c5f44d6c501f686125d4468ca642f745920..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/gettingstarted.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-layout: default
-title: OpenTuner - Using OpenTuner
-permalink: /tutorial/gettingstarted/index.html
----
-
-Tutorial: Optimizing Block Matrix Multiplication
-================================================
-
-This tutorial assumes that you have checked out a copy of opentuner. For
-guidelines on how to get opentuner set up, refer [here][setup].
-
-[setup]: http://opentuner.org/tutorial/setup/
-
-Identifying a Program to Autotune
----------------------------------
-
-In order to do autotuning, you first need something to autotune. This will
-normally be your own program that you want to make either fast or better in
-some way.  For this tutorial we will use a blocked version of matrix multiply
-as an example. We will use opentuner to find the optimal value of the block
-size parameter.
-
-We will autotune the sample code below(based off of modification of code
-found [here][matrix-multiply-code]), making sure to take the block size as
-a compile time constant to the program.
-
-[matrix-multiply-code]: http://csapp.cs.cmu.edu/public/waside/waside-blocking.pdf
-
-Save the sample code below to examples/tutorials/mmm_block.cpp
-
-    #include <stdio.h>
-    #include <cstdlib>
-
-    #define N 100
-    
-    int main(int argc, const char** argv)
-    {
-    
-      int n = BLOCK_SIZE * (N/BLOCK_SIZE);
-      int a[N][N];
-      int b[N][N];
-      int c[N][N];
-      int sum=0;
-      for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-      {
-          for(int j1=0;j1<n;j1+=BLOCK_SIZE)
-          {
-              for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-              {
-                  for(int i=0;i<n;i++)
-                  {
-                      for(int j=j1;j<j1+BLOCK_SIZE;j++)
-                      {
-                          sum = c[i][j];
-                          for(int k=k1;k<k1+BLOCK_SIZE;k++)
-                          {
-                              sum += a[i][k] * b[k][j];
-                          }
-                          c[i][j] = sum;
-                      }
-                  }
-              }
-          }
-             }
-      return 0;
-    }
-
-Creating a New Autotuner with Opentuner
-------------------------------------
-Now we need to create a program that uses OpenTuner to optimize the program we just saved.
-
-Save the following code to examples/tutorials/mmm_tuner.py
-
-    #!/usr/bin/env python
-    #
-    # Optimize blocksize of apps/mmm_block.cpp
-    #
-    # This is an extremely simplified version meant only for tutorials
-    #
-    import adddeps  # fix sys.path
-
-    import opentuner
-    from opentuner import ConfigurationManipulator
-    from opentuner import IntegerParameter
-    from opentuner import MeasurementInterface
-    from opentuner import Result
-
-
-    class GccFlagsTuner(MeasurementInterface):
-
-      def manipulator(self):
-        """
-        Define the search space by creating a
-        ConfigurationManipulator
-        """
-        manipulator = ConfigurationManipulator()
-        manipulator.add_parameter(
-          IntegerParameter('blockSize', 1, 10))
-        return manipulator
-
-      def run(self, desired_result, input, limit):
-        """
-        Compile and run a given configuration then
-        return performance
-        """
-        cfg = desired_result.configuration.data
-
-        gcc_cmd = 'g++ mmm_block.cpp '
-        gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
-        gcc_cmd += ' -o ./tmp.bin'
-
-        compile_result = self.call_program(gcc_cmd)
-        assert compile_result['returncode'] == 0
-
-        run_cmd = './tmp.bin'
-
-        run_result = self.call_program(run_cmd)
-        assert run_result['returncode'] == 0
-
-        return Result(time=run_result['time'])
-
-      def save_final_config(self, configuration):
-        """called at the end of tuning"""
-        print "Optimal block size written to mmm_final_config.json:", configuration.data
-        self.manipulator().save_to_file(configuration.data,
-                                        'mmm_final_config.json')
-
-
-    if __name__ == '__main__':
-      argparser = opentuner.default_argparser()
-      GccFlagsTuner.main(argparser.parse_args())
-
-
-This file consists of several components, each of which will be discussed in further detail below.
-
-Tuning Programs have a general structure as follows:
-
-    from opentuner import MeasurementInterface
-    from opentuner import Result
-
-Create an instance of class GccFlagsTuner, which tunes specified parameters using opentuner.
-    class GccFlagsTuner(MeasurementInterface):
-
-The manipulator method defines the variable search space by specifying parameters that should be tuned by this instance of GccFlagsTuner
-
-    def manipulator(self):
-      """
-      Define the search space by creating a
-      ConfigurationManipulator
-      """
-      manipulator = ConfigurationManipulator()
-      manipulator.add_parameter(
-        IntegerParameter('blockSize', 1, 10))
-      return manipulator
-
-The run method actually runs opentuner under the given configuration and returns the calculated performance under this configuration. In this example, the blockSize parameter to be tuned is input as a compile-time constant that takes on a value within the specified range each time it is run. However, opentuner also supports other methods of specifying these parameters that may be preferred in different use cases.
-
-    def run(self, desired_result, input, limit):
-      """
-      Compile and run a given configuration then
-      return performance
-      """
-      cfg = desired_result.configuration.data
-
-      gcc_cmd = 'g++ mmm_block.cpp '
-      gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
-      gcc_cmd += ' -o ./tmp.bin'
-
-      compile_result = self.call_program(gcc_cmd)
-      assert compile_result['returncode'] == 0
-
-      run_cmd = './tmp.bin'
-
-      run_result = self.call_program(run_cmd)
-      assert run_result['returncode'] == 0
-
-      return Result(time=run_result['time'])
-
-We can actually display the result of running opentuner(the optimal block size for our multiplication problem) by creating a method, save_final_config() in our class. This saves a json dictionary of the optimal blockSize parameter found to the file mmm_final_config.json
-
-    def save_final_config(self, configuration):
-      """called at the end of tuning"""
-      print "Optimal block size written to mmm_final_config.json:", configuration.data
-      self.manipulator().save_to_file(configuration.data,
-                                      'mmm_final_config.json')
-
-    if __name__ == '__main__':
-      argparser = opentuner.default_argparser()
-      GccFlagsTuner.main(argparser.parse_args())
-
-Generating and Viewing Results
-------------------------------
-
-Run the following command to autotune our program(The --no-dups flag hides warnings about duplicate results and the --stop-after parameter specifies that we are running opentuner for a maximum of 30 seconds):
-
-    python mmm_tuner.py --no-dups --stop-after=30
-
-The results of each run configuration will be displayed as follows(output lines are truncated for readability here):
-
-    [    10s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    19s]    INFO opentuner.search.metatechniques: AUCBanditMetaTechniqueA: [('DifferentialEvolutionAlt', 477), ('UniformGreedyMutation', 18), ('NormalGreedyMutation', 5), ('RandomNelderMead', 1)]
-    [    20s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    30s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    30s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    Optimal block size written to mmm_final_config.json: {'BLOCK_SIZE': 4}
-
-
-Look up the optimal BlockSize value by inspecting the following created file:
-
-    mmm_final_config.json
-
-In this example, the output file content was as follows:
-
-    {'BLOCK_SIZE': 4}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence.py
deleted file mode 100644
index dd7a050ac8428f99872abd25d1aa2f3d794f7e2b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence.py
+++ /dev/null
@@ -1,290 +0,0 @@
-
-import argparse
-import os
-import sys
-from time import sleep
-
-
-def getAccuracy(file_name):
-
-  if not os.path.exists(file_name):
-    print("final_accuracy file not found ")
-    sys.exit(0)
-    
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-
-
-total_runs = 40
-skip_lines = 0
-
-
-def test_func():
-  print "test_func"
-  sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
-  #total_runs = 100.0
-  successful_runs = 0.0
-  total_acc = 0
-
-  for i in range(int(total_runs)):
-
-    fifo = open("/tmp/myfifo", "w")
-    fifo.write("start_run")
-    fifo.close()
-
-    print "Waiting for process to signal back - when done processing one run"
-
-    fifo2 = open("/tmp/myfifo", "r")
-    fifo2.read()
-    fifo2.close()
-
-    print "Process Signalled back"
-
-    accuracy = getAccuracy("final_accuracy")
-    total_acc += accuracy
-
-    if accuracy > accuracy_threshold:
-      successful_runs += 1
-
-  confidence = (successful_runs / (total_runs*1.0) ) * 100.0    
-  print("confidence = ", confidence)    
-  avg_acc = total_acc / total_runs
-  print("average accuracy = ", avg_acc)
-
-  return confidence, avg_acc
-  
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("opentuner_flags", "w+")
-
-    index = 0
-    results_str = ""
-    for x in f:
-      if index >= skip_lines:
-        error_knob = int(float(x.split()[1]))
-        print error_knob
-        tuner_file.write(str(error_knob) + "\n")
-
-      results_str += x
-      index += 1
-      
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
-    if run_confidence > 90:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(results_str)
-      f2.close()
-
-    conf_result = (run_confidence, avg_accuracy, file_name)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-
-def compute_promise_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("opentuner_flags", "w+")
-
-    config_str = f.read()
-    tuner_file.write(config_str)  
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
-    if run_confidence > 90:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(config_str)
-      f2.close()
-
-    flags_str = config_str.replace('\n', ',')
-    conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  result_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)
-
-    
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  print confidence_list
-
-  # descending sort on confidence
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  print  "Dumped Confidence Summary"
-  
-
-def processLayerFile(layer_file_path):
-
-  layer_sizes = []
-  layer_file = open(layer_file_path, "r")
-
-  for layer_size in layer_file:
-    try:
-      size = int(layer_size)
-      layer_sizes.append(size)
-    except:
-      return layer_sizes
-
-  return layer_sizes
-
-
-
-def getLayerConfigStr(config_str, layer_sizes, num_flags):
-
-  new_config_str = ""
-  config_vals = config_str.split(',')
-  it_count = 0
-  for val in config_vals:
-    if val == "":
-      continue
-    
-    config_val = int(val)
-    # For FP32 and FP32 values, each tensor op needs to be annotated
-    if config_val == 8 or config_val == 9:
-      for i in range(layer_sizes[it_count] - 1):
-        new_config_str += val + " "
-      new_config_str += val
-      if it_count < num_flags - 1:
-        new_config_str += ","
-    else:
-      new_config_str += val
-      if it_count < num_flags - 1:
-        new_config_str += ","
-
-    it_count += 1  
-
-  return new_config_str
-
-
-def dump_promise_confidence_files(binary, result_dir, layer_file_path, num_flags, accuracy, confidence):
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  input_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)
-    
-
-  layer_sizes = processLayerFile(layer_file_path);
-  print layer_sizes
-  sleep(3)
-    
-  confidence_list = compute_promise_confidence(binary, accuracy, confidence, input_dir, output_dir)
-  print confidence_list
-
-  # Ascending sort on accuracy
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-   
-  promise_file = open(output_dir + "/promise_confs.txt", "w+")
-  confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
-  max_configs = 50
-  it_count = 0
-  for x in sorted_list:
-    if x[1] > accuracy and x[0] > confidence:
-      config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
-      promise_file.write(config_str + "\n")
-      it_count += 1
-      if it_count > max_configs:
-        break
-       
-    confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")    
-    
-  promise_file.close()
-  confidence_file.close()
-  
-  print  "Dumped Confidence Summary"
-
-  
-
-
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-  argparser.add_argument('--output-dir', help='Directory for storing output directory')
-  argparser.add_argument('--binary', help='Binary name to run')
-  argparser.add_argument('--accuracy', type=float,  help='Accuracy constraint')
-  argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-  
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-  output_dir = args.output_dir
-  binary = args.binary
-  accuracy = args.accuracy
-  confidence = args.confidence
-
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  #print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence2.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence2.py
deleted file mode 100644
index f5998ff3c871fe2db625873dc75fcf8fe4452838..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/measure_confidence2.py
+++ /dev/null
@@ -1,664 +0,0 @@
-
-import argparse
-import os
-import sys
-import subprocess
-from time import sleep
-
-
-def getAccuracy(file_name):
-
-  if not os.path.exists(file_name):
-    print("final_accuracy file not found ")
-    sys.exit(0)
-    
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-
-
-total_runs = 40.0
-fails_allowed = 3
-skip_lines = 0
-
-
-def test_func():
-  print "test_func"
-  sys.exit(0)
-
-  
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
-  successful_runs = 0.0
-  unsuccessful_runs = 0.0
-  total_acc = 0
-
-  for i in range(int(total_runs)):
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen(binary_name, stdout = FNULL)
-    p.wait()
-    
-    accuracy = getAccuracy("final_accuracy")
-    total_acc += accuracy
-
-    if accuracy > accuracy_threshold:
-      successful_runs += 1
-    else:
-      unsuccessful_runs += 1
-
-    if unsuccessful_runs > 6:
-      break
-      
-
-  confidence = (successful_runs / total_runs) * 100.0    
-  print("confidence = ", confidence)    
-  avg_acc = total_acc / total_runs
-  print("average accuracy = ", avg_acc)
-
-  return confidence, avg_acc
-
-
-
-def getConfidence(accuracy_outfile, acc_threshold):
-
-  f = open(accuracy_outfile, "r")
-
-  total_acc = 0.0
-  failed = 0
-  it = 0
-  
-  for x in f:
-    acc = float(x.strip())
-    if acc < acc_threshold:
-      failed += 1
-
-    total_acc += acc     
-    it += 1
-
-  conf = (it * 1.0 - failed) / it * 100
-  avg_acc = total_acc / it
-  
-  return conf, avg_acc
-
-
-
-def getMinAccuracy(accuracy_outfile):
-
-  f = open(accuracy_outfile, "r")
-
-  total_acc = 0.0
-  failed = 0
-  it = 0
-
-  acc_list = []
-  for x in f:
-    acc = float(x.strip())
-    acc_list.append(acc)
-    
-  return min(acc_list)
-
-  
-# NOTE: invokes the binary with the number of runs
-def do_multiple_runs2(binary_name, accuracy_threshold, confidence_threshold):
-
-  successful_runs = 0.0
-  unsuccessful_runs = 0.0
-  total_acc = 0
-
-  FNULL = open(os.devnull, 'wb')
-  p = subprocess.Popen([binary_name, str(int(total_runs)), str(accuracy_threshold), str(fails_allowed)], stdout = FNULL)
-  p.wait()
-
-  confidence, avg_acc = getConfidence("run_accuracies.txt", accuracy_threshold) 
-
-  print("confidence = ", confidence)    
-  print("average accuracy = ", avg_acc)
-
-  return confidence, avg_acc
-  
-
-
-
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("opentuner_flags", "w+")
-
-    index = 0
-    results_str = ""
-    for x in f:
-      if index >= skip_lines:
-        error_knob = int(float(x.split()[1]))
-        print error_knob
-        tuner_file.write(str(error_knob) + "\n")
-
-      results_str += x
-      index += 1
-      
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
-    if run_confidence >= 95:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(results_str)
-      f2.close()
-
-    conf_result = (run_confidence, avg_accuracy, file_name)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  result_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)
-    
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  print  "Dumped Confidence Summary"
-
-
-  
-
-def processLayerFile(layer_file_path):
-
-  layer_sizes = []
-  layer_file = open(layer_file_path, "r")
-
-  for layer_desc in layer_file:
-    try:
-      toks = layer_desc.split(",")
-      if len(toks) < 2: # Not layer size description
-        continue
-      
-      size = int(toks[1])
-      if "NML" in layer_desc:
-        size = -1
-      layer_sizes.append(size)
-    except:
-      return layer_sizes
-
-  return layer_sizes
-
-
-
-def getLayerConfigStr(config_str, layer_sizes, num_flags):
-
-  new_config_str = ""
-  config_vals = config_str.split(',')
-  it_count = 0
-  layer_count = 0
-  
-  #for layer_size in  val in config_vals:
-  for layer_depth_size in layer_sizes:
-
-    if layer_depth_size == -1:
-      new_config_str += "8"
-      layer_count += 1
-      if layer_count < len(layer_sizes):
-        new_config_str += ","
-      continue
-    
-    val = config_vals[it_count]      
-    if val == "":
-      continue
-    
-    config_val = int(val)
-    # For FP32 and FP32 values, each tensor op needs to be annotated
-    if config_val == 8 or config_val == 9:
-      for i in range(layer_depth_size - 1):
-        new_config_str += val + " "
-      new_config_str += val
-      if layer_count < len(layer_sizes) - 1:
-        new_config_str += ","
-    else:
-      new_config_str += val
-      if layer_count < len(layer_sizes) - 1:
-        new_config_str += ","
-
-    it_count += 1
-    layer_count += 1
-    
-
-  return new_config_str
-
-
-
-def compute_promise_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("promise_flags", "w+")
-
-    config_str = f.read()
-    tuner_file.write(config_str)  
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
-    if run_confidence >= 95:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(config_str)
-      f2.close()
-
-    flags_str = config_str.replace('\n', ',')
-    conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-def getConfigCost(layer_costs, config_str):
-
-  tuning_flags = config_str.split("\n")
-  
-  it = 0
-  orig_cost = 0.0
-  total_cost = 0.0
-  for flag in tuning_flags:
-
-    flag_value = -1
-    try:
-      flag_value = int(flag)
-    except:
-      continue
-
-    orig_cost += layer_costs[it]
-
-    #print ("orig_cost = ", orig_cost, " flag_value = ", flag_value) 
-    
-    if flag_value == 11:
-      total_cost += layer_costs[it]
-    elif flag_value == 10:
-      total_cost += (layer_costs[it] / 1.3)
-    elif flag_value == 8 or flag_value == 9:
-      total_cost += (layer_costs[it] / 1.6)
-    elif flag_value < 8:
-      divisor = 5 + (7 - flag_value)
-      total_cost += (layer_costs[it] / divisor)
- 
-    it += 1
-
-  speedup = orig_cost * 1.0 / total_cost * 1.0
-  
-  return total_cost, speedup 
-
-
-
-
-
-def getConfigCost2(layer_costs, knobs_speedup, config_flags):
-
-  orig_cost = 0.0
-  total_cost = 0.0
-  for it in range(len(config_flags)):
-    flag_value = config_flags[it]
-    op_cost = layer_costs[it]
-    speedup = knobs_speedup[flag_value]
-
-    total_cost += (op_cost * 1.0 / speedup * 1.0)
-    orig_cost += op_cost
-    
-    it += 1
-
-  speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-  
-  return total_cost, speedup
-
-
-
-
-
-def compute_promise_confidence2(binary_name, accuracy, confidence, layer_costs,
-                                result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("promise_flags", "w+")
-
-    config_str = f.read()
-    tuner_file.write(config_str)  
-    tuner_file.close()
-    
-    #run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-    run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
-    if run_confidence >= 95:    
-      f2 = open(output_dir + "/" + file_name, "w+")
-
-      config_cost, speedup = getConfigCost(layer_costs, config_str)
-      
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\t config_cost=" + str(config_cost) + "\t speedup=" + str(speedup) +   "\n")
-      f2.write(config_str)
-      f2.close()
-
-    flags_str = config_str.replace('\n', ',')
-    conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-
-
-def compute_promise_confidence3(binary_name, accuracy, confidence, layer_costs,
-                                result_dir, output_dir, knobs_speedup):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("promise_flags", "w+")
-
-    config_flags = []
-    config_str = ""
-    it = 0
-    for x in f:
-
-      if it > 0:
-        config_str += x
-        config_flags.append(int(x.strip()))
-        tuner_file.write(x)    
-      it += 1
-
-    tuner_file.close()
-
-    
-    #run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-    run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
-    if run_confidence >= 95:    
-      f2 = open(output_dir + "/" + file_name, "w+")
-
-      config_cost, speedup = getConfigCost2(layer_costs, knobs_speedup, config_flags)
-      
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\t config_cost=" + str(config_cost) + "\t speedup=" + str(speedup) +   "\n")
-      f2.write(config_str)
-      f2.close()
-
-    flags_str = config_str.replace('\n', ',')
-    conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-
-def dump_promise_confidence_files(binary, result_dir, layer_file_path,
-                                  num_flags, accuracy, confidence):
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  input_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)    
-
-  layer_sizes = processLayerFile(layer_file_path);
-  print layer_sizes
-  sleep(2)
-    
-  confidence_list = compute_promise_confidence(binary, accuracy, confidence, input_dir, output_dir)
-  print confidence_list
-
-  # Ascending sort on accuracy
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-   
-  promise_file = open(output_dir + "/promise_confs.txt", "w+")
-  confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
-  max_configs = 50
-  it_count = 0
-  for x in sorted_list:
-    if x[1] > accuracy and x[0] > confidence:
-      config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
-      promise_file.write(config_str + "\n")
-      it_count += 1
-      if it_count > max_configs:
-        break
-       
-    confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")    
-    
-  promise_file.close()
-  confidence_file.close()
-  
-  print "Dumped Confidence Summary"
-
-  
-  
-
-
-def dump_promise_confidence_files2(binary, result_dir, layer_file_path,
-                                   num_flags, accuracy, layer_costs, confidence):
-
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  input_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)    
-
-  layer_sizes = processLayerFile(layer_file_path);
-  print layer_sizes
-  sleep(2)
-    
-  confidence_list = compute_promise_confidence2(binary, accuracy, confidence, layer_costs, input_dir, output_dir)
-  print confidence_list
-
-  # Ascending sort on accuracy
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-   
-  promise_file = open(output_dir + "/promise_confs.txt", "w+")
-  confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
-  max_configs = 50
-  it_count = 0
-  for x in sorted_list:
-    if x[1] > accuracy and x[0] > confidence:
-      config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
-      promise_file.write(config_str + "\n")
-      it_count += 1
-      if it_count > max_configs:
-        break
-       
-    confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")    
-    
-  promise_file.close()
-  confidence_file.close()
-  
-  print "Dumped Confidence Summary"
-
-
-
-
-def dump_promise_confidence_files3(binary, input_dir, output_dir, layer_file_path,
-                                   num_flags, accuracy, layer_costs, confidence):
-
-
-  #result_dir = args.result_dir
-  #output_dir = result_dir + "/high_confidence"
-  #input_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)    
-
-  layer_sizes = processLayerFile(layer_file_path);
-  print layer_sizes
-  sleep(2)
-    
-  confidence_list = compute_promise_confidence2(binary, accuracy, confidence, layer_costs, input_dir, output_dir)
-  print confidence_list
-
-  # Ascending sort on accuracy
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-   
-  promise_file = open(output_dir + "/promise_confs.txt", "w+")
-  confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
-  max_configs = 50
-  it_count = 0
-  for x in sorted_list:
-    if x[1] > accuracy and x[0] > confidence:
-      config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
-      promise_file.write(config_str + "\n")
-      it_count += 1
-      if it_count > max_configs:
-        break
-       
-    confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")    
-    
-  promise_file.close()
-  confidence_file.close()
-  
-  print "Dumped Confidence Summary"
-
-
-
-
-
-def dump_promise_confidence_files4(binary, input_dir, output_dir, layer_file_path,
-                                   num_flags, accuracy, layer_costs, confidence, knobs_speedup):
-
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)    
-
-  layer_sizes = processLayerFile(layer_file_path);
-  print layer_sizes
-  sleep(2)
-    
-  confidence_list = compute_promise_confidence3(binary, accuracy, confidence, layer_costs, input_dir, output_dir, knobs_speedup)
-  print confidence_list
-
-  # Ascending sort on accuracy
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-   
-  promise_file = open(output_dir + "/promise_confs.txt", "w+")
-  confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
-  max_configs = 50
-  it_count = 0
-  for x in sorted_list:
-    if x[1] > accuracy and x[0] > confidence:
-      config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
-      promise_file.write(config_str + "\n")
-      it_count += 1
-      if it_count > max_configs:
-        break
-       
-    confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")    
-    
-  promise_file.close()
-  confidence_file.close()
-  
-  print "Dumped Confidence Summary"
-
-
-
-  
-
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-  argparser.add_argument('--output-dir', help='Directory for storing output directory')
-  argparser.add_argument('--binary', help='Binary name to run')
-  argparser.add_argument('--accuracy', type=float,  help='Accuracy constraint')
-  argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-  
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-  output_dir = args.output_dir
-  binary = args.binary
-  accuracy = args.accuracy
-  confidence = args.confidence
-
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  #print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/pareto_curve.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/pareto_curve.py
deleted file mode 100644
index db8233994b855317095c94331fba869d9ad79d16..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/pareto_curve.py
+++ /dev/null
@@ -1,264 +0,0 @@
-
-
-import os
-import shutil
-from measure_confidence2 import getConfigCost
-
-
-AL_THRESHOLD = 0.1
-  
-
-class Config:
-  def __init__(self):
-    self.avg_accuracy = 0
-    self.avg_loss = 0
-    self.speedup = 1
-    self.fname = ""
-    self.flags = []
-
-
-
-
-def skipFile(fname):
-
-  skip_files = {}
-  skip_files["confidence_summary.txt"] = 1
-  skip_files["promise_confs.txt"] = 1
-
-  if "accuracy" in fname:
-    return True
-
-  if fname in skip_files:
-    return True
-  else:
-    return False
-    
-
-  
-    
-def loadConfigData(result_dir, layer_costs, baseline_accuracy):
-
-  config_arr = []
-  
-  #result_dir += "/promise_tuner/high_confidence/"
-  file_names = os.listdir(result_dir)
-
-  
-  for fname in file_names:
-    if not skipFile(fname):
-
-      fpath = result_dir + fname  
-      config = Config()
-      f = open(fpath, "r")
-
-      config_str = f.read()
-      cost, speedup = getConfigCost(layer_costs, config_str)
-
-      config.speedup = speedup
-      config.fname = fname
-
-      fpath2 = fpath + "_accuracy"
-      f2 = open(fpath2, "r")
-      acc_str = f2.read().strip()
-      accuracy = float(acc_str)
-      
-      config.avg_accuracy = accuracy
-      config.avg_loss = baseline_accuracy - accuracy
-   
-      config_arr.append(config)
-        
-
-  return config_arr      
-
-    
-
-
-class Configuration:
-    def __init__(self, name, speedup, energy, accuracy, accuracy_loss):
-        self.name = name
-        self.speedup = speedup
-        self.energy = energy
-        self.accuracy = accuracy
-        self.accuracy_loss = accuracy_loss
-    def __repr__(self):
-        return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-configuration_objects = [
-    Configuration('conf1', 1.05, 15, 85, 1.2),
-    Configuration('conf2', 2.51, 12, 83, 1.4),
-    Configuration('conf3', 2.05, 10, 84, 0.8),
-]
-
-def compute_pareto_points(configurations):
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy > en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        # outer while loop variable increment
-        start_idx = end_idx
-    return [speedupconfigurations, energyconfigurations]
-
-
-def compute_pareto_points_with_margin(configurations, speedup_band_width, energy_band_width):
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    idx_to_sp_conf_dict = {}
-    idx_to_en_conf_dict = {}
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy < en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        idx_to_sp_conf_dict[start_idx] = len(speedupconfigurations)-1
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        idx_to_en_conf_dict[start_idx] = len(energyconfigurations)-1
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    # We want to add configurations in a band of a certain width around the curves
-    # not possible to do during contruction, because the quality of the curve would
-    # deteriorate quickly
-
-    AdjustedSpeedupCurve = []
-    AdjustedEnergyCurve = []
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup + speedup_band_width >= speedupconfigurations[idx_to_sp_conf_dict[start_idx]].speedup:
-                AdjustedSpeedupCurve.append(sorted_configurations[i])
-            if sorted_configurations[i].energy + energy_band_width >= energyconfigurations[idx_to_en_conf_dict[start_idx]].energy:
-                AdjustedEnergyCurve.append(sorted_configurations[i])
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    return [AdjustedSpeedupCurve, AdjustedEnergyCurve]
-
-
-
-def findParetoConfigs(base_dir, layer_costs, accuracy):
-
-  result_dir = base_dir + "/pareto/"
-  try:
-      os.mkdir(result_dir)
-  except:
-      print "could not create dir"
-
-  input_dir = base_dir + "/full_results/"    
-  #result_dir = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/batch15"
-  config_arr = loadConfigData(input_dir, layer_costs, accuracy)
-
-  config_list = []
-
-  it = 0
-  for config in config_arr:
-    config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss)
-    config_list.append(config)
-
-  
-  SPEEDUP_BAND_SIZE = 1.0
-  ENERGY_BAND_SIZE = 10
-
-  # No Pareto Selection if list is < 50 configurations
-  if len(config_list) < 50:
-    SPEEDUP_BAND_SIZE = 100 # Include all in Pareto Frontier
-    
-
-  print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-  
-  ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-  
-  print ("len(config_list) = ", len(config_list))
-  print ("len(ASC) = ", len(ASC))
-
-  #print (ASC)
-  #print (config_list)
-
-  for conf in ASC:
-    #dst_path = conf.name.replace("full_results", "pareto")
-    src_path = base_dir + "/full_results/" + conf.name
-    dst_path = base_dir + "/pareto/" + conf.name
-    shutil.copy(src_path, dst_path)
-    
-  
-
-if __name__ == "__main__":
-
-  get_pareto_configs("")
-  
-  #SC, EC = compute_pareto_points(configuration_objects)
-  #ASC, AEC = compute_pareto_points_with_margin(configuration_objects, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-  #print(SC)
-  #print(EC)
-
-  #print(ASC)
-  #print(AEC)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner2.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner2.py
deleted file mode 100644
index ca96ff16c2d176b3bb91e213005202634916fc41..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner2.py
+++ /dev/null
@@ -1,220 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-gpu_layers = 0
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    # NOTE: Skipping first 'gpu_layers' to run on GPU
-    for flag in tuning_flags[:gpu_layers]:
-      manipulator.add_parameter(
-        EnumParameter(flag, [8, 9]))
-      
-    for flag in tuning_flags[gpu_layers:]:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-
-    """
-    Run  a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("promise_flags", cfg)
-    
-    run_cmd = binary_name
-    print "binary_name = ", run_cmd
-    #run_result_call_program = self.call_program(run_cmd)
-    #print "returned \n\n"
-
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen(run_cmd, stdout = FNULL)
-    p.wait()
-
-       
-    accuracy = getAccuracy("final_accuracy")
-    total_comps = abs(accuracy_threshold - accuracy)
-    
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      #if accuracy not in evaluated_configs:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      evaluated_configs[accuracy] = 1
-      shutil.copy('promise_flags', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-        
-    print "done with one run"
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results \n"
-    sleep(20)
-    
-    # Only dumping files with 95% confidence
-    dump_promise_confidence_files(binary_name, orig_result_dir, layer_file, num_flags, accuracy_threshold, 95)
-    #select_top_results(orig_result_dir + "/high_confidence")
-
-  
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
-  argparser.add_argument('--start-range', type=int, help='start range in tuning') 
-  argparser.add_argument('--error-range', type=int, help='range of error values used in tuning')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--layer-file', help='layer description')
-  argparser.add_argument('--gpu-layers', type=int, help='first N layers to run on GPU')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  start_range = int(args.start_range)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-    
-  gpu_layers = args.gpu_layers     
-
-    
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  #for j in range(error_range):
-  #  flag_ranges.append(j)
-
-  for j in range(start_range, error_range):
-    flag_ranges.append(j)
-    
-  
-  print("flag_ranges = ", flag_ranges)
-
-  # File with layer description
-  layer_file = args.layer_file
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner3.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner3.py
deleted file mode 100644
index 04ce0d6158819d5cb014411456e1a985fb17b354..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner3.py
+++ /dev/null
@@ -1,314 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-gpu_layers = 0
-
-test_id = 0
-
-layer_costs = []
-
-
-def readCostFile(file_path):
-
-  f = open(file_path)
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  print ("len(layer_costs) = ", layer_costs)
-  f.close()
-  
-  
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-  
-def getConfigCost(cfg):
-
-  it = 0
-  total_cost = 0.0
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    if flag_value == 11:
-      total_cost += layer_costs[it]
-    elif flag_value == 10:
-      total_cost += (layer_costs[it] / 1.3)
-    elif flag_value == 8 or flag_value == 9:
-      total_cost += (layer_costs[it] / 1.6)
-    elif flag_value < 8:
-      divisor = 5 + (7 - flag_value)
-      total_cost += (layer_costs[it] / divisor)
-      
-    it += 1
-    
-  return total_cost
-  
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    flags_arr = []
-    for i in range (8, error_range):
-      flags_arr.append(i)
-      
-    # NOTE: Skipping first 'gpu_layers' to run on GPU
-    for flag in tuning_flags[:gpu_layers]:
-      manipulator.add_parameter(
-        EnumParameter(flag, flags_arr))
-
-    ind = gpu_layers  
-    for flag in tuning_flags[gpu_layers:]:
-      if ind in skip_layers:
-        manipulator.add_parameter(
-        EnumParameter(flag, flags_arr))
-        print ("8 ..... 11")
-      else:
-        manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-        print ("1 .... 11")
-      ind += 1  
-
-      
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-    
-    """
-    Run  a given configuration then
-    return performance
-    """
-    global test_id
-    
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("promise_flags", cfg)
-    
-    run_cmd = binary_name
-    print "\nbinary_name = ", run_cmd
-    #run_result_call_program = self.call_program(run_cmd)
-
-
-    total_runs = 2
-    FNULL = open(os.devnull, 'wb')
-    #p = subprocess.Popen(run_cmd, stdout = FNULL)
-    p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
-    p.wait()
-
-       
-    accuracy = getAccuracy("final_accuracy")
-
-    # Get Confidence for multiple runs
-    conf, avg_acc = getConfidence("run_accuracies.txt", accuracy_threshold)  
-    
-    # getConfigCost returns the cost associated with the selected configuration
-    total_comps = getConfigCost(cfg)
-   
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    #Result.accuracy = accuracy
-    min_accuracy = getMinAccuracy("run_accuracies.txt")
-    print ("min_accuracy = ", min_accuracy)
-    Result.accuracy = min_accuracy
-    
-    # Only pass conf if conf == 100
-    if min_accuracy > accuracy_threshold and conf == 100:
-      print ("conf = ", conf, " avg_acc = ", avg_acc)
-      #if accuracy not in evaluated_configs:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      evaluated_configs[accuracy] = 1
-      shutil.copy('promise_flags', output_dir + '/' + binary_name + '_' + str(test_id))
-
-      f_acc = open(output_dir + '/' + binary_name + '_' + str(test_id) + "_accuracy", "w")
-      f_acc.write(str(accuracy))
-      f_acc.close()
-                   
-      
-    test_id += 1
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results \n"
-    sleep(2)
-
-
-    findParetoConfigs(orig_result_dir, layer_costs, accuracy_threshold)
-
-    input_dir = orig_result_dir + "/pareto/"
-    output_dir = orig_result_dir + "/high_confidence/"
-    
-    # Only dumping files with 95% confidence
-    dump_promise_confidence_files3(binary_name, input_dir, output_dir, layer_file, num_flags, accuracy_threshold, layer_costs, 95)
-    #select_top_results(orig_result_dir + "/high_confidence")
-  
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-  
-
-error_range = 11
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
-  argparser.add_argument('--start-range', type=int, help='start range in tuning') 
-  argparser.add_argument('--error-range', type=int, help='range of error values used in tuning')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--layer-file', help='layer description')
-  argparser.add_argument('--cost-file', help='layer description')
-  argparser.add_argument('--gpu-layers', type=int, help='first N layers to run on GPU')
-  argparser.add_argument('--skip-layers', help='layer IDs to run on GPU')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  start_range = int(args.start_range)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-    
-  gpu_layers = args.gpu_layers
-  skip_layers_str = args.skip_layers
-
-  skip_layers = []
-  layer_ids = skip_layers_str.split("_")
-  for layer_id in layer_ids:
-    skip_layers.append(int(layer_id))
-
-  print ("skip_layers = ", skip_layers)
-
-  # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
-  cost_file_path = args.cost_file
-  readCostFile(cost_file_path)
-  
-    
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  #for j in range(error_range):
-  #  flag_ranges.append(j)
-
-  for j in range(start_range, error_range):
-    flag_ranges.append(j)
-    
-  
-  print("flag_ranges = ", flag_ranges)
-
-  # File with layer description
-  layer_file = args.layer_file
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner_piped.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner_piped.py
deleted file mode 100644
index cf84c503b09b6b74474cd4730d93aabd34b5ee2a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/promise_tuner_piped.py
+++ /dev/null
@@ -1,231 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_promise_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-
-
-
-def kill(proc_pid):
-  process = psutil.Process(proc_pid)
-  for proc in process.children(recursive=True):
-    proc.kill()
-  process.kill()
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    FNULL = open(os.devnull, 'wb')
-    #run_result_call_program = self.call_program(run_cmd)
-    self.start_process = subprocess.Popen([binary_name, "opentuner_run"] ,  stdout=FNULL);
-
-    try:
-      os.mkfifo("/tmp/myfifo")
-    except OSError, e:
-      print("FIFO exists")
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-
-    """
-    Run  a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    #run_result_call_program = self.call_program(run_cmd)      
-  
-    # Using Named Pipes to signal execution to the DNN outer thread
-    fifo = open("/tmp/myfifo", "w")
-    fifo.write("start_run")
-    fifo.close()
-
-    print "Waiting for process to signal back - when done processing one run"
-
-    fifo2 = open("/tmp/myfifo", "r")
-    fifo2.read()
-    fifo2.close()
-
-    print "Process Signalled back"
-
-    accuracy = getAccuracy("final_accuracy")
-    total_comps = abs(accuracy_threshold - accuracy)
-
-    
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      #if accuracy not in evaluated_configs:
-      config_tuple = (total_comps, accuracy, cfg)
-      self.configs_list.append(config_tuple)
-      evaluated_configs[accuracy] = 1
-      shutil.copy('opentuner_flags', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-        
-    print "done with one run"
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results \n"
-    sleep(20)
-    
-    # Only dumping files with 95% confidence
-    dump_promise_confidence_files(binary_name, orig_result_dir, layer_file, num_flags, accuracy_threshold, 95)
-    #select_top_results(orig_result_dir + "/high_confidence")
-
-    
-    #self.start_process.kill()
-    kill(self.start_process.pid)
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
-  argparser.add_argument('--error-range', type=int, help='range of error values used in tuning') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--layer-file', help='layer description')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-
-  # File with layer description
-  layer_file = args.layer_file
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/psnr_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/psnr_tuner.py
deleted file mode 100644
index eb126de3aaf15ed3dfb1c30cdf02e28d62d1d939..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/psnr_tuner.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps  
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
-  def __init__(self):
-    self.binary_path = ""
-    self.output_dir = ""
-    self.num_layers = 0
-    self.knobs_list = []
-    self.knobs_speedup = {}
-    self.accuracy_threshold = 0
-    self.test_id = 0
-    self.layer_costs = []
-    self.tuning_flags = []
-    self.autotuner_runs = 0
-    
-    
-
-
-tunerData = TunerData()
-
-
-
-
-def readCostFile(file_path):
-
-  layer_costs = []
-  f = open(file_path)
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  print ("len(layer_costs) = ", layer_costs)
-  f.close()
-
-  return layer_costs
-
-  
-
-def getPSNR(file_name):
-  with open(file_name) as f:
-    try:
-      raw_str = f.read()
-      violation, avg_psnr = [float(s) for s in raw_str.split(",")]
-    except:
-      return None, None
-  print (100 - violation, avg_psnr)
-  return 100 - violation, avg_psnr
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for i in range(tunerData.num_layers):  # flag in tunerData.tuning_flags:
-    flag = tunerData.tuning_flags[i]
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-  
-
-def readLayerKnobs(file_path):
-
-  f = open(file_path, "r")
-  knobs_list = []
-  for x in f:
-    knobs = []
-    vals = x.split(",")
-    for val in vals:
-      knobs.append(int(val))
-      
-    knobs_list.append(knobs)
-
-  print ("knobs_list = ", knobs_list)
-  
-  return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
-  knobs_speedup = {}
-  f = open(file_path, "r")
-  for x in f:
-    toks = x.split("\t")
-    ID = int(toks[0].split(",")[1])
-
-    speedup = float(toks[2])
-    knobs_speedup[ID] = speedup
-  
-  print ("knobs_speedup = ", knobs_speedup)
-  
-  return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
-  orig_cost = 0.0
-  total_cost = 0.0
-  for it in range(tunerData.num_layers):
-    flag = tunerData.tuning_flags[it]
-    flag_value = cfg[flag]
-    op_cost = tunerData.layer_costs[it]
-    speedup = tunerData.knobs_speedup[flag_value]
-
-    total_cost += (op_cost * 1.0 / speedup * 1.0)
-    orig_cost += op_cost
-    
-    it += 1
-
-  speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-  
-  return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
-  f_str = open(f_path, "r").read()
-
-  f_out = open(f_path, "w+")
-  f_out.write("avg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
-  f_out.write(f_str)
-
-  f_out.close()
-      
-
-  
-def dumpAccuracyFile(accuracy):
-  
-  f_acc = open(tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id) + "_accuracy", "w")
-  f_acc.write(str(accuracy))
-  f_acc.close()
- 
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
-    input_manager = FixedInputManager(size=tunerData.num_layers)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-
-    for i in range(tunerData.num_layers):
-      tunerData.tuning_flags.append("flag" + str(i))
-
-         
-    #for flag in tunerData.tuning_flags:
-    for ind in range(tunerData.num_layers):
-        flag = tunerData.tuning_flags[ind]
-
-        manipulator.add_parameter(EnumParameter(flag, tunerData.knobs_list[ind]))
-        print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
-        print (tunerData.knobs_list[ind])
-          
-        ind += 1  
-      
-    return manipulator
-
-  
-  
-  def run(self, desired_result, input, limit):
-    
-    """
-    Run  a given configuration then
-    return performance
-    """
-    global test_id
-    
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("promise_flags", cfg)
-    
-    run_cmd = tunerData.binary_path
-    print "\nbinary_path = ", run_cmd
-
-    input_size = 5000
-    offset = 5000
-
-    total_runs = 1 # NOTE: Single run sufficient in Algorithmic Approx Tuner
-    FNULL = open(os.devnull, 'wb')
-    p = subprocess.Popen([run_cmd], stdout = FNULL)
-    p.wait()
-    if p.returncode != 0:
-      # Something went wrong
-      sys.stderr.write("Child program returned non-zero; you may want to stop and check.")
-
-    success_rate, avg_psnr = getPSNR("final_accuracy")
-
-    # getConfigCost returns the cost associated with the selected configuration
-    total_comps, speedup = getConfigCost(cfg)
-
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = success_rate
-
-    if success_rate > tunerData.accuracy_threshold:
-      config_tuple = (total_comps, success_rate, cfg)
-      self.configs_list.append(config_tuple)
-      f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
-      shutil.copy('promise_flags', f_path)
-
-      appendTopLine(f_path, avg_psnr, total_runs, total_comps, speedup)
-
-      # dumpAccuracyFile(accuracy)
-                   
-      
-    tunerData.test_id += 1
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Done with Autotuning Run \n"
-    sleep(2)
-
-    print "Final configuration", configuration.data
-
-    return
-
-  
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='path to target binary')
-  argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='result directory')
-  argparser.add_argument('--cost-file', help='layer description')
-  argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
-  argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-  
-  
-  args = argparser.parse_args()
-
-  tunerData.binary_path = str(args.binary)
-  tunerData.num_layers = int(args.num_layers)
-  tunerData.accuracy_threshold = float(args.accuracy)
-
-  # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
-  cost_file_path = args.cost_file
-  tunerData.layer_costs = readCostFile(cost_file_path)
-  
-  tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
-  tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-
-  
-  result_dir = args.result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-        
-  tunerData.output_dir = result_dir + "/high_confidence/"
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(tunerData.output_dir):
-    print("Creating output directory = ", tunerData.output_dir)
-    os.mkdir(tunerData.output_dir)
-
-
-    
-  ClangFlagsTuner.main(argparser.parse_args())
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/select_top_results.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/select_top_results.py
deleted file mode 100644
index 7ee878e5f8f84f3f56ea982c1f933b2c1a5b914b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/select_top_results.py
+++ /dev/null
@@ -1,101 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 9
-linear_index = 10
-quad_index = 11
-
-top_k = 10
-skip_lines = 1
-
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
-  ref_dir = result_dir + "/" + sub_dir
-  if not os.path.exists(ref_dir):
-    os.mkdir(ref_dir)
-  
-  for i in range(min(k, len(sorted_list)) ):
-    file_name = sorted_list[i][1]
-    file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
-    f = open(file_name, "w+")
-    f.write(str(sorted_list[i][2]) + "\t")
-    f.write(str(sorted_list[i][3]) + "\t")
-    f.write(str(sorted_list[i][4]) + "\n")
-    f.write(sorted_list[i][0])
-    f.close()
-
-    
-    
-
-def select_top_results(result_dir):
-
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  results_arr = []
-  
-  for file_name in file_names:
-
-    if file_name == "confidence_summary.txt":
-      continue
-    
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-
-    log_result = 0.0
-    linear_result = 0.0
-    quad_result = 0.0
-    file_str = ""
-    
-    index = 0
-    f = open(result_dir + "/" + file_name)
-    for x in f:
-      if index >= skip_lines:
-        words = x.split()
-        log_result += float(words[log_index])
-        linear_result += float(words[linear_index])
-        quad_result += float(words[quad_index])
-        file_str += x 
-
-      index += 1
-
-
-    file_result = (file_str, file_name, log_result, linear_result, quad_result)          
-    results_arr.append(file_result)    
-
-    
-  sorted_list = sorted(results_arr, key = lambda tup: tup[2])
-  dump_results(sorted_list, top_k, result_dir, "log")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[3])
-  dump_results(sorted_list, top_k, result_dir, "linear")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[4])
-  dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-#def select_top_configuration(result_dir):
-  
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-
-  select_top_results(result_dir)
-  
-
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/utils.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/utils.py
deleted file mode 100644
index 47429d95991c77c799b809e569a08f8e184da79f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/utils.py
+++ /dev/null
@@ -1,157 +0,0 @@
-
-
-import psutil
-from time import sleep
-import os
-
-
-
-def readCostFile(cost_file_path):
-
-    layer_costs = []
-    f = open(cost_file_path)
-    for x in f:
-      cost = float(x.strip())
-      layer_costs.append(cost)
-
-    print ("-Layer count = ", layer_costs)
-    f.close()
-
-    return layer_costs
-
-  
-
-def readAccuracy(accuray_res_file):
-    
-    file = open(accuray_res_file, "r")
-    accuracy_str = file.read()
-    file.close()
-    accuracy = 0
-    
-    try:
-      accuracy = float(accuracy_str)
-    except:
-      accuracy = 0
-    
-    print ("*Configuration Accuracy = ", accuracy, "\n\n")
-    return accuracy
-
-
-def genLayerFlagsFile(flags_file_path, cfg, tunerData):
-
-    f = open(flags_file_path, "w+")
-    cmd_config = ""
-    for i in range(tunerData.num_layers):  # flag in tunerData.tuning_flags:
-      flag = tunerData.tuning_flags[i]
-      flag_value = cfg[flag]
-      cmd_config += str(flag_value) + "\n"
-      
-    f.write(cmd_config)
-    f.close()
-
-  
-
-def readLayerKnobs(layer_knobs_path):
-    
-    f = open(layer_knobs_path, "r")
-    knobs_list = []
-    for x in f:
-      knobs = []
-      vals = x.split(",")
-      for val in vals:
-        knobs.append(int(val))
-        
-      knobs_list.append(knobs)
-
-    print ("\n **** Global Approximation Knobs List = \n", knobs_list)
-    
-    return knobs_list
-
-
-
-def readGlobalKnobConfig(global_knobs_path):
-
-    knobs_speedup = {}
-    f = open(global_knobs_path, "r")
-    for x in f:
-      toks = x.split("\t")
-      ID = int(toks[0].split(",")[1])
-
-      speedup = float(toks[2])
-      knobs_speedup[ID] = speedup
-      
-    print ("knobs_speedup = ", knobs_speedup)
-    
-    return knobs_speedup
-
-
-
-
-def computeConfigCost(cfg, tunerData):
-
-    orig_cost = 0.0
-    total_cost = 0.0
-    for it in range(tunerData.num_layers):
-      flag = tunerData.tuning_flags[it]
-      flag_value = cfg[flag]
-      op_cost = tunerData.layer_costs[it]
-      speedup = tunerData.knobs_speedup[flag_value]
-
-      total_cost += (op_cost * 1.0 / speedup * 1.0)
-      orig_cost += op_cost
-   
-
-
-    speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-    
-    return total_cost, speedup
-
-
-
-def addInfoToOutFile(f_path, accuracy, total_runs, total_comps, speedup):
-
-    f_str = open(f_path, "r").read()
-
-    f_out = open(f_path, "w+")
-
-    f_out.write("total_runs=" + str(total_runs) + "\tconfidence=100.0" + "\tavg_accuracy=" + \
-                str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
-    f_out.write(f_str)
-
-    f_out.close()
-    
-
-
-def check_pid(pid):
-
-    """ Check For the existence of a unix pid. """
-    try:
-        os.kill(pid, 0)
-    except OSError:
-        return False
-    else:
-        return True
-                    
-                
-
-def process_kill(proc_pid):
-
-  if not check_pid(proc_pid):
-    return # Return if process does not exist    
-    
-  process = psutil.Process(proc_pid)
-
-  try:
-    for proc in process.children(recursive=True):
-      proc.kill()
-    process.kill()
-
-    print ("\n\n\n\n\n\ %%%%% Killed Process \n\n\n\n")
-    
-  except:
-    print ("\n\n\n\n PROCESS NOT KILLED ------- \n\n\n\n\n\n\n")
-
-  #sleep(20)  
-
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/debian-packages-deps b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/debian-packages-deps
deleted file mode 100644
index ea49289a875cfe80df1de02307e03f7791c00adf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/debian-packages-deps
+++ /dev/null
@@ -1,9 +0,0 @@
-build-essential
-git
-gnuplot
-libfreetype6-dev
-libpng-dev
-libsqlite3-dev
-python-dev
-python-pip
-sqlite3
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/Makefile b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/Makefile
deleted file mode 100644
index 1c028b3a91e5750dc927f6a865923ca2e9ac141a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/Makefile
+++ /dev/null
@@ -1,177 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-
-clean:
-	rm -rf $(BUILDDIR)/*
-
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/OpenTuner.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/OpenTuner.qhc"
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/OpenTuner"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/OpenTuner"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/rtd-requirements.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/rtd-requirements.txt
deleted file mode 100644
index e30d149ed5e3356ff54d915b556dfaed0dfb6148..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/rtd-requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-argparse>=1.2.1
-django==1.6.1
-fn>=0.2.12
-SQLAlchemy>=0.8.2
-virtualenv==1.9.1
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/conf.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/conf.py
deleted file mode 100644
index a27fabf403e0e7f6081d906819167e94eb236b61..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/conf.py
+++ /dev/null
@@ -1,261 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# OpenTuner documentation build configuration file, created by
-# sphinx-quickstart on Sat Jan  3 04:13:12 2015.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-import sys
-import os
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-sys.path.insert(0, os.path.abspath('../..'))
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.pngmath',
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = u'OpenTuner'
-copyright = u'2015, Jason Ansel'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = '0.0'
-# The full version, including alpha/beta/rc tags.
-release = '0.0'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = []
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-#keep_warnings = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-html_theme = 'default'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-#html_theme_path = []
-
-# The name for this set of Sphinx documents.  If None, it defaults to
-# "<project> v<release> documentation".
-#html_title = None
-
-# A shorter title for the navigation bar.  Default is the same as html_title.
-#html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-#html_extra_path = []
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#html_additional_pages = {}
-
-# If false, no module index is generated.
-#html_domain_indices = True
-
-# If false, no index is generated.
-#html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it.  The value of this option must be the
-# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'OpenTunerdoc'
-
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-# The paper size ('letterpaper' or 'a4paper').
-#'papersize': 'letterpaper',
-
-# The font size ('10pt', '11pt' or '12pt').
-#'pointsize': '10pt',
-
-# Additional stuff for the LaTeX preamble.
-#'preamble': '',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-  ('index', 'OpenTuner.tex', u'OpenTuner Documentation',
-   u'Jason Ansel', 'manual'),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-#latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-#latex_use_parts = False
-
-# If true, show page references after internal links.
-#latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-#latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-#latex_appendices = []
-
-# If false, no module index is generated.
-#latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    ('index', 'opentuner', u'OpenTuner Documentation',
-     [u'Jason Ansel'], 1)
-]
-
-# If true, show URL addresses after external links.
-#man_show_urls = False
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-  ('index', 'OpenTuner', u'OpenTuner Documentation',
-   u'Jason Ansel', 'OpenTuner', 'One line description of project.',
-   'Miscellaneous'),
-]
-
-# Documents to append as an appendix to all manuals.
-#texinfo_appendices = []
-
-# If false, no module index is generated.
-#texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-#texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-#texinfo_no_detailmenu = False
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/index.rst b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/index.rst
deleted file mode 100644
index 48f7468982f559d60ef98736539567fa0a320ec3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/index.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-.. OpenTuner documentation master file, created by
-   sphinx-quickstart on Sat Jan  3 04:13:12 2015.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-Welcome to OpenTuner's documentation!
-=====================================
-This is still under construction
-
-
-Contents:
-
-.. toctree::
-   :maxdepth: 2
-
-   params
-   techniques
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/params.rst b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/params.rst
deleted file mode 100644
index b8d08cd300d466c5be43dabfa0e4db1abd12182b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/params.rst
+++ /dev/null
@@ -1,339 +0,0 @@
-.. currentmodule:: opentuner.search.manipulator
-
-****************
-Parameters
-****************
-
-This will be an overview of parameters in OpenTuner.
-
-Each Parameter instance is created with a name. Most methods in parameters operate on configurations, dict-like objects spawned by the ConfigurationManipulator. Configurations contain values corresponding to a collection of instances of named parameters.
-
-A Parameter’s methods may mutate the value in a configuration corresponding to the name of the particular parameter instance. These methods are called operators.
-
-==============================
-Parameter Types and Operators
-==============================
-
-Each parameter has a set of operators. These operators take in a set of parent configurations and mutate the corresponding parameter value in the first configuration according to the parent values. Operators form the set of available transformations for search techniques to generate new configurations to test.
-
-Operator methods can be identified by the prefix 'op#_', where # is the number of required input configurations. The prefix 'opn\_' specifies an arbitrary number of input configurations, as a list. The first argument into an operator is always the configuration that will be mutated. This is followed by the required parent configurations, then any required arguments, and finally optional arguments.
-
-Any operators defined for a Parameter are inherited by its subclasses.
-
------------------
-Parameter
------------------
-This is an abstract base interface for parameters.
-
-.. autoclass:: Parameter
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op3_swarm
-
-	.. automethod:: op4_set_linear
-
-	.. automethod:: opn_stochastic_mix
-
-
--------------------------
-Primitive Parameter
--------------------------
-.. autoclass:: PrimitiveParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Parameter.op1_randomize`,
-	:meth:`Parameter.op3_swarm`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_normal_mutation
-
-	**This paragraph can have examples for the above operator**
-
-	.. automethod:: op4_set_linear
-
-
-------------------------
-Numeric Parameter
-------------------------
-.. autoclass:: NumericParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`Parameter.op3_swarm`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op1_scale
-
-	.. automethod:: op3_difference
-
-	.. automethod:: opn_sum
-
-
-------------------------
-Integer Parameter
-------------------------
-.. autoclass:: IntegerParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-	.. automethod:: op3_swarm
-
-
-------------------------
-Float Parameter
-------------------------
-.. autoclass:: FloatParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-	.. automethod:: op3_swarm
-
-
-------------------------
-ScaledNumericParameter
-------------------------
-.. autoclass:: ScaledNumericParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`Parameter.op3_swarm`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-
-------------------------
-LogIntegerParameter
-------------------------
-.. autoclass:: LogIntegerParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`FloatParameter.op3_swarm`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-
-------------------------
-LogFloatParameter
-------------------------
-.. autoclass:: LogFloatParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`FloatParameter.op3_swarm`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-
-------------------------
-PowerOfTwoParameter
-------------------------
-.. autoclass:: LogFloatParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`PrimitiveParameter.op1_normal_mutation`,
-	:meth:`NumericParameter.op1_randomize`,
-	:meth:`NumericParameter.op1_scale`,
-	:meth:`NumericParameter.op3_difference`,
-	:meth:`IntegerParameter.op3_swarm`,
-	:meth:`PrimitiveParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`,
-	:meth:`NumericParameter.opn_sum`
-
-
-------------------------
-Complex Parameter
-------------------------
-.. autoclass:: ComplexParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Parameter.op3_swarm`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op4_set_linear
-
-
-------------------------
-Boolean Parameter
-------------------------
-.. autoclass:: BooleanParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Parameter.op3_swarm`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_flip
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op3_swarm
-
---------------------------
-Switch Parameter
---------------------------
-.. autoclass:: SwitchParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Parameter.op3_swarm`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
---------------------------
-Enum Parameter
---------------------------
-.. autoclass:: EnumParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Parameter.op3_swarm`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-
---------------------------
-Permutation Parameter
---------------------------
-.. autoclass:: PermutationParameter
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op1_small_random_change
-
-	.. automethod:: op2_random_swap
-
-	.. automethod:: op2_random_invert
-
-	.. automethod:: op3_cross
-
-	.. automethod:: op3_cross_PX
-
-	.. automethod:: op3_cross_PMX
-
-	.. automethod:: op3_cross_CX
-
-	.. automethod:: op3_cross_OX1
-
-	.. automethod:: op3_cross_OX3
-
-	.. automethod:: op3_swarm
-
---------------------------
-Array
---------------------------
-.. autoclass:: Array
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`ComplexParameter.op1_randomize`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op3_cross
-
-	.. automethod:: op3_swarm
-
-
---------------------------
-BooleanArray
---------------------------
-.. autoclass:: BooleanArray
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Array.op3_cross`,
-	:meth:`Array.op3_swarm`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op3_swarm_parallel
-
-
---------------------------
-FloatArray
---------------------------
-.. autoclass:: FloatArray
-	:show-inheritance:
-
-	*Inherited Operators:*
-
-	:meth:`Array.op3_cross`,
-	:meth:`Array.op3_swarm`,
-	:meth:`ComplexParameter.op4_set_linear`,
-	:meth:`Parameter.opn_stochastic_mix`
-
-	.. automethod:: op1_randomize
-
-	.. automethod:: op3_swarm_parallel
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/techniques.rst b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/techniques.rst
deleted file mode 100644
index 3bbebddedbd9c4b999fa8a4f58244ee482ffe673..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/docs/source/techniques.rst
+++ /dev/null
@@ -1,51 +0,0 @@
-.. currentmodule:: opentuner.search.composableevolutionarytechniques
-
-********************
-Current Techniques
-********************
-
-OpenTuner has a library of existing search techniques.
-
-=================================
-Composable Search Techniques
-=================================
-
-A ComposableEvolutionaryTechnique allows for composition between the search technique and any operators. Creating a ComposableEvolutionaryTechnique requires implementing 3 methods:
-
- * :meth:`minimum_number_of_parents <ComposableEvolutionaryTechnique.minimum_number_of_parents>`
- * :meth:`get_parents <ComposableEvolutionaryTechnique.get_parents>`
- * :meth:`update_population <ComposableEvolutionaryTechnique.update_population>`
-
-Additionally, the following methods may be overridden for further customization
-
- * :meth:`make_population_member <ComposableEvolutionaryTechnique.make_population_member>`
- * :meth:`select_parameters <ComposableEvolutionaryTechnique.select_parameters>`
- * :meth:`get_default_operator <ComposableEvolutionaryTechnique.get_default_operator>`
-
-The following methods are useful when choosing parents or updating the population:
-
- * :meth:`lt <ComposableEvolutionaryTechnique.lt>`
- * :meth:`lte <ComposableEvolutionaryTechnique.lte>`
- * :meth:`get_global_best_configuration <ComposableEvolutionaryTechnique.get_global_best_configuration>`
-
-A ComposableEvolutionaryTechnique will yields configurations generated by successive iterations of applying operators on the configurations returned by :meth:`get_parents <ComposableEvolutionaryTechnique.get_parents>` and updating the population with the new configuration through :meth:`update_population <ComposableEvolutionaryTechnique.update_population>`
-
-.. autoclass:: ComposableEvolutionaryTechnique
-
-	.. automethod:: minimum_number_of_parents
-
-	.. automethod:: get_parents
-
-	.. automethod:: update_population
-
-	.. automethod:: make_population_member
-
-	.. automethod:: select_parameters
-
-	.. automethod:: get_default_operator
-
-	.. automethod:: lt
-
-	.. automethod:: lte
-
-	.. automethod:: get_global_best_configuration
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/.gitignore
deleted file mode 100644
index f525a6259ba8a55dbb66c2eb9b3489e9784ae523..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-*-journal
-stats
-opentuner.log
-opentuner.db
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/.gitignore
deleted file mode 100644
index fab2c2b13c5afd35380ae5cf8f4317d2acc58a06..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-tmp.bin
-cc_flags.json
-gccflags_final_config.cmd
-gccflags_final_config.json
-cc_params.json
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/.gitignore
deleted file mode 100644
index f06d3e01a2bedbfadb4c05ad181eb8745ac2f608..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-fft.c
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/matrixmultiply.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/matrixmultiply.cpp
deleted file mode 100644
index 9989ffbf4a2ff1f6dffcfbdcab1b7e3f3116c7a1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/matrixmultiply.cpp
+++ /dev/null
@@ -1,80 +0,0 @@
-// based on: http://blogs.msdn.com/b/xiangfan/archive/2009/04/28/optimize-your-code-matrix-multiplication.aspx
-//  by Xiang Fan
-
-#include <algorithm>
-#include <iostream>
-
-#define N 512
-
-
-template<class T>
-T** make_test_matrix() {
-    T** data = new T*[N];
-    for (int i = 0; i < N; i++) {
-        data[i] = new T[N];
-    }
-    for(int i = 0; i < N; i++) {
-        for(int j = 0; j < N; j++) {
-            data[i][j] = (int) i * j;
-        }
-    }
-    return data;
-}
-
-
-
-template<typename T>
-void Transpose(int size, T** __restrict__ m)
-{
-    for (int i = 0; i < size; i++) {
-        for (int j = i + 1; j < size; j++) {
-            std::swap(m[i][j], m[j][i]);
-        }
-    }
-}
-template<typename T>
-void SeqMatrixMult3(int size, T** __restrict__ m1, T** __restrict__ m2,
-                    T** __restrict__ result) {
-    Transpose(size, m2);
-    for (int i = 0; i < size; i++) {
-        for (int j = 0; j < size; j++) {
-            T c = 0;
-            for (int k = 0; k < size; k++) {
-                c += m1[i][k] * m2[j][k];
-            }
-            result[i][j] = c;
-        }
-    }
-    Transpose(size, m2);
-}
-
-
-template<typename T>
-void test() {
-  T** a = make_test_matrix<T>();
-  T** b = make_test_matrix<T>();
-  T** c = make_test_matrix<T>();
-  SeqMatrixMult3(N, a, b, c);
-
-
-  T avg = 0;
-  for(int i = 0; i < N; i++) {
-      for(int j = 0; j < N; j++) {
-          avg += c[i][j] / (T)(N*N);
-      }
-  }
-  // print out average so caller can check answer
-  std::cout << avg << std::endl;
-}
-
-
-int main(int argc, const char** argv) {
-  test<float>();
-  return 0;
-}
-
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/raytracer.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/raytracer.cpp
deleted file mode 100644
index 3cb1192c6a0d9cbd3502186dc391efa71d5cde18..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/raytracer.cpp
+++ /dev/null
@@ -1,277 +0,0 @@
-/*
-	A very basic raytracer example.
-	Copyright (C) 2012  www.scratchapixel.com
-
-	This program is free software: you can redistribute it and/or modify
-	it under the terms of the GNU General Public License as published by
-	the Free Software Foundation, either version 3 of the License, or
-	(at your option) any later version.
-
-	This program is distributed in the hope that it will be useful,
-	but WITHOUT ANY WARRANTY; without even the implied warranty of
-	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-	GNU General Public License for more details.
-
-	You should have received a copy of the GNU General Public License
-	along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-	- changes 02/04/13: fixed flag in ofstream causing a bug under Windows,
-	added default values for M_PI and INFINITY
-	- changes 24/05/13: small change to way we compute the refraction direction
-	vector (eta=ior if we are inside and 1/ior if we are outside the sphere)
-
-	Compile with the following command: c++ -o raytracer -O3 -Wall raytracer.cpp
-
-*/
-
-#include <cstdlib>
-#include <cstdio>
-#include <cmath>
-#include <fstream>
-#include <vector>
-#include <iostream>
-#include <cassert>
-
-#if defined(__linux__) || defined(__APPLE__)
-	// "Compiled for Linux
-#else
-	// Windows doesn't define these values by default, Linux does
-	#define M_PI 3.141592653589793
-	#define INFINITY 1e8
-#endif
-
-template<typename T>
-class Vec3
-{
-public:
-	T x, y, z;
-	Vec3() : x(T(0)), y(T(0)), z(T(0)) {}
-	Vec3(T xx) : x(xx), y(xx), z(xx) {}
-	Vec3(T xx, T yy, T zz) : x(xx), y(yy), z(zz) {}
-	Vec3& normalize()
-	{
-		T nor2 = length2();
-		if (nor2 > 0) {
-			T invNor = 1 / sqrt(nor2);
-			x *= invNor, y *= invNor, z *= invNor;
-		}
-		return *this;
-	}
-	Vec3<T> operator * (const T &f) const { return Vec3<T>(x * f, y * f, z * f); }
-	Vec3<T> operator * (const Vec3<T> &v) const { return Vec3<T>(x * v.x, y * v.y, z * v.z); }
-	T dot(const Vec3<T> &v) const { return x * v.x + y * v.y + z * v.z; }
-	Vec3<T> operator - (const Vec3<T> &v) const { return Vec3<T>(x - v.x, y - v.y, z - v.z); }
-	Vec3<T> operator + (const Vec3<T> &v) const { return Vec3<T>(x + v.x, y + v.y, z + v.z); }
-	Vec3<T>& operator += (const Vec3<T> &v) { x += v.x, y += v.y, z += v.z; return *this; }
-	Vec3<T>& operator *= (const Vec3<T> &v) { x *= v.x, y *= v.y, z *= v.z; return *this; }
-	Vec3<T> operator - () const { return Vec3<T>(-x, -y, -z); }
-	T length2() const { return x * x + y * y + z * z; }
-	T length() const { return sqrt(length2()); }
-	friend std::ostream & operator << (std::ostream &os, const Vec3<T> &v)
-	{
-		os << "[" << v.x << " " << v.y << " " << v.z << "]";
-		return os;
-	}
-};
-
-template<typename T>
-class Sphere
-{
-public:
-	Vec3<T> center;                         /// position of the sphere
-	T radius, radius2;                      /// sphere radius and radius^2
-	Vec3<T> surfaceColor, emissionColor;    /// surface color and emission (light)
-	T transparency, reflection;             /// surface transparency and reflectivity
-	Sphere(const Vec3<T> &c, const T &r, const Vec3<T> &sc, 
-		const T &refl = 0, const T &transp = 0, const Vec3<T> &ec = 0) : 
-		center(c), radius(r), radius2(r * r), surfaceColor(sc), emissionColor(ec),
-		transparency(transp), reflection(refl)
-	{}
-	// compute a ray-sphere intersection using the geometric solution
-	bool intersect(const Vec3<T> &rayorig, const Vec3<T> &raydir, T *t0 = NULL, T *t1 = NULL) const
-	{
-		Vec3<T> l = center - rayorig;
-		T tca = l.dot(raydir);
-		if (tca < 0) return false;
-		T d2 = l.dot(l) - tca * tca;
-		if (d2 > radius2) return false;
-		T thc = sqrt(radius2 - d2);
-		if (t0 != NULL && t1 != NULL) {
-			*t0 = tca - thc;
-			*t1 = tca + thc;
-		}
-
-		return true;
-	}
-};
-
-#define MAX_RAY_DEPTH 5
-
-template<typename T>
-T mix(const T &a, const T &b, const T &mix)
-{
-	return b * mix + a * (T(1) - mix);
-}
-
-// This is the main trace function. It takes a ray as argument (defined by its origin
-// and direction). We test if this ray intersects any of the geometry in the scene.
-// If the ray intersects an object, we compute the intersection point, the normal
-// at the intersection point, and shade this point using this information.
-// Shading depends on the surface property (is it transparent, reflective, diffuse).
-// The function returns a color for the ray. If the ray intersects an object that
-// is the color of the object at the intersection point, otherwise it returns
-// the background color.
-template<typename T>
-Vec3<T> trace(const Vec3<T> &rayorig, const Vec3<T> &raydir, 
-	const std::vector<Sphere<T> *> &spheres, const int &depth)
-{
-	//if (raydir.length() != 1) std::cerr << "Error " << raydir << std::endl;
-	T tnear = INFINITY;
-	const Sphere<T> *sphere = NULL;
-	// find intersection of this ray with the sphere in the scene
-	for (unsigned i = 0; i < spheres.size(); ++i) {
-		T t0 = INFINITY, t1 = INFINITY;
-		if (spheres[i]->intersect(rayorig, raydir, &t0, &t1)) {
-			if (t0 < 0) t0 = t1;
-			if (t0 < tnear) {
-				tnear = t0;
-				sphere = spheres[i];
-			}
-		}
-	}
-	// if there's no intersection return black or background color
-	if (!sphere) return Vec3<T>(2);
-	Vec3<T> surfaceColor = 0; // color of the ray/surfaceof the object intersected by the ray
-	Vec3<T> phit = rayorig + raydir * tnear; // point of intersection
-	Vec3<T> nhit = phit - sphere->center; // normal at the intersection point
-	nhit.normalize(); // normalize normal direction
-	// If the normal and the view direction are not opposite to each other 
-	// reverse the normal direction. That also means we are inside the sphere so set
-	// the inside bool to true. Finally reverse the sign of IdotN which we want
-	// positive.
-	T bias = 1e-4; // add some bias to the point from which we will be tracing
-	bool inside = false;
-	if (raydir.dot(nhit) > 0) nhit = -nhit, inside = true;
-	if ((sphere->transparency > 0 || sphere->reflection > 0) && depth < MAX_RAY_DEPTH) {
-		T facingratio = -raydir.dot(nhit);
-		// change the mix value to tweak the effect
-		T fresneleffect = mix<T>(pow(1 - facingratio, 3), 1, 0.1); 
-		// compute reflection direction (not need to normalize because all vectors
-		// are already normalized)
-		Vec3<T> refldir = raydir - nhit * 2 * raydir.dot(nhit);
-		refldir.normalize();
-		Vec3<T> reflection = trace(phit + nhit * bias, refldir, spheres, depth + 1);
-		Vec3<T> refraction = 0;
-		// if the sphere is also transparent compute refraction ray (transmission)
-		if (sphere->transparency) {
-			T ior = 1.1, eta = (inside) ? ior : 1 / ior; // are we inside or outside the surface?
-			T cosi = -nhit.dot(raydir);
-			T k = 1 - eta * eta * (1 - cosi * cosi);
-			Vec3<T> refrdir = raydir * eta + nhit * (eta *  cosi - sqrt(k));
-			refrdir.normalize();
-			refraction = trace(phit - nhit * bias, refrdir, spheres, depth + 1);
-		}
-		// the result is a mix of reflection and refraction (if the sphere is transparent)
-		surfaceColor = (reflection * fresneleffect + 
-			refraction * (1 - fresneleffect) * sphere->transparency) * sphere->surfaceColor;
-	}
-	else {
-		// it's a diffuse object, no need to raytrace any further
-		for (unsigned i = 0; i < spheres.size(); ++i) {
-			if (spheres[i]->emissionColor.x > 0) {
-				// this is a light
-				Vec3<T> transmission = 1;
-				Vec3<T> lightDirection = spheres[i]->center - phit;
-				lightDirection.normalize();
-				for (unsigned j = 0; j < spheres.size(); ++j) {
-					if (i != j) {
-						T t0, t1;
-						if (spheres[j]->intersect(phit + nhit * bias, lightDirection, &t0, &t1)) {
-							transmission = 0;
-							break;
-						}
-					}
-				}
-				surfaceColor += sphere->surfaceColor * transmission * 
-					std::max(T(0), nhit.dot(lightDirection)) * spheres[i]->emissionColor;
-			}
-		}
-	}
-
-	return surfaceColor + sphere->emissionColor;
-}
-
-// Main rendering function. We compute a camera ray for each pixel of the image
-// trace it and return a color. If the ray hits a sphere, we return the color of the
-// sphere at the intersection point, else we return the background color.
-template<typename T>
-unsigned int render(const std::vector<Sphere<T> *> &spheres)
-{
-	unsigned width = 640, height = 480;
-	Vec3<T> *image = new Vec3<T>[width * height], *pixel = image;
-	T invWidth = 1 / T(width), invHeight = 1 / T(height);
-	T fov = 30, aspectratio = width / T(height);
-	T angle = tan(M_PI * 0.5 * fov / T(180));
-	// Trace rays
-	for (unsigned y = 0; y < height; ++y) {
-		for (unsigned x = 0; x < width; ++x, ++pixel) {
-			T xx = (2 * ((x + 0.5) * invWidth) - 1) * angle * aspectratio;
-			T yy = (1 - 2 * ((y + 0.5) * invHeight)) * angle;
-			Vec3<T> raydir(xx, yy, -1);
-			raydir.normalize();
-			*pixel = trace(Vec3<T>(0), raydir, spheres, 0);
-		}
-	}
-#if 0
-	// Save result to a PPM image (keep these flags if you compile under Windows)
-	std::ofstream ofs("./untitled.ppm", std::ios::out | std::ios::binary);
-	ofs << "P6\n" << width << " " << height << "\n255\n";
-	for (unsigned i = 0; i < width * height; ++i) {
-		ofs << (unsigned char)(std::min(T(1), image[i].x) * 255) << 
-		(unsigned char)(std::min(T(1), image[i].y) * 255) <<
-		(unsigned char)(std::min(T(1), image[i].z) * 255); 
-	}
-	ofs.close();
-#endif
-
-  unsigned int bad_hash = 0;
-	for (unsigned i = 0; i < width * height; ++i) {
-    bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].x) * 255);
-    bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].y) * 255);
-    bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].z) * 255);
-	}
-	delete [] image;
-
-  return bad_hash;
-}
-
-volatile unsigned int dont_optimize_me;
-
-int main(int argc, char **argv) {
-	srand48(13);
-	std::vector<Sphere<float> *> spheres;
-	// position, radius, surface color, reflectivity, transparency, emission color
-	spheres.push_back(new Sphere<float>(Vec3<float>(0, -10004, -20), 10000, Vec3<float>(0.2), 0, 0.0));
-	spheres.push_back(new Sphere<float>(Vec3<float>(0, 0, -20), 4, Vec3<float>(1.00, 0.32, 0.36), 1, 0.5));
-	spheres.push_back(new Sphere<float>(Vec3<float>(5, -1, -15), 2, Vec3<float>(0.90, 0.76, 0.46), 1, 0.0));
-	spheres.push_back(new Sphere<float>(Vec3<float>(5, 0, -25), 3, Vec3<float>(0.65, 0.77, 0.97), 1, 0.0));
-	spheres.push_back(new Sphere<float>(Vec3<float>(-5.5, 0, -15), 3, Vec3<float>(0.90, 0.90, 0.90), 1, 0.0));
-	// light
-	spheres.push_back(new Sphere<float>(Vec3<float>(0, 20, -30), 3, Vec3<float>(0), 0, 0, Vec3<float>(3)));
-
-  dont_optimize_me = render<float>(spheres);
-  __asm__ __volatile__ ("" ::: "memory"); // memory barrier
-  if(dont_optimize_me == 0x4bd7c0e0) {
-    //printf("CORRECT\n");
-  } else {
-    printf("ERROR: WRONG ANSWER\n");
-  }
-
-	while (!spheres.empty()) {
-		Sphere<float> *sph = spheres.back();
-		spheres.pop_back();
-		delete sph;
-	}
-
-	return 0;
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/tsp_ga.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/tsp_ga.cpp
deleted file mode 100644
index 0e8f232cb099d37facffa0440b41dfd57efd4b2e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/apps/tsp_ga.cpp
+++ /dev/null
@@ -1,548 +0,0 @@
-//
-// based on: https://bitbucket.org/knordkvist/tsp-ga/overview
-// by Kristoffer Nordkvist 
-//
-#include <algorithm>
-#include <assert.h>
-#include <iostream>
-#include <limits>
-#include <math.h>
-#include <sstream>
-#include <stdio.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <stdlib.h>
-#include <string.h>
-#include <string>
-#include <time.h>
-
-class TSP
-{
-	public:
-		TSP(const double crossoverProbability, const double mutationProbability);
-
-		/* The constants used in this project */
-		static const unsigned int chromosones = 30, cities = 20, xMin = 0, xMax = 1000, yMin = 0, yMax = 500;
-
-		/* Generate a random population of chromosones */
-		void randomPopulation();
-
-		/* Create a new population using crossover and mutation */
-		void nextPopulation();
-
-		/* Returns the fitness of the best chromosone */
-		double getBestFitness() const;
-
-		/* Returns a string representation of the best path */
-		std::string getBestPathString() const;
-
-		/* Returns the total distance of the best chromosone path */
-		double getLowestTotalDistance() const;
-
-		/* Returns the populations average length */
-		double getAverageDistance() const;
-	private:
-		const double crossoverProbability, mutationProbability;
-
-		/* Gets the total distance of the supplied path */
-		double totalDistance(int const * const chromosone) const;
-
-		/* The coordinates for each city, (x,y) for the first city is found in (citiesX[0], citiesY[0]) */
-		double citiesX[cities], citiesY[cities];
-
-		/* The chromosone containing the shortest path */
-		int *bestChromosone;
-
-		/* Contains the current population of chromosones */
-		int (* solutions)[cities],
-			/* The two chromosones with the best fitness functions */
-			//bestChromosone1[cities], bestChromosone2[cities],
-			/* Used to store the new chromosones when creating a new population */
-			(* newPopulation)[cities];
-
-		/* Returns a random double r, 0 <= r <= max */
-		static double randomInclusive(const double max);
-
-		/* Returns a random double r, 0 <= r < max */
-		static double randomExclusive(const double max);
-
-		/* True if the two chromosones represent the same path */
-		static bool areChromosonesEqual(int const * const chromosoneA, int const * const chromosoneB);
-
-		/* Evaluate the fitness the supplied chromosone */
-		double evaluateFitness(const int * const chromosone) const;
-
-		/* Selects a chromosone from the current population using Roulette Wheel Selection.
-		 * Using the algorithm described in http://www.obitko.com/tutorials/genetic-algorithms/selection.php.
-		 */
-		int * rouletteSelection(double const * const fitness) const;
-
-		/* Replace the element at offspringIndex with the first element found in other that does not exist in offspringToRepair */
-		void repairOffspring(int * const offspringToRepair, int missingIndex, const int * const other);
-
-		/* Might swap one gene with another, depending on the mutation probability */
-		void mutate(int * const chromosone);
-
-		/* Cross over the parents to form new offspring using Multi-Point Crossover, collisions are handled as shown in lecture 5.
-		 * The chromosones might be a copy of their parents, depending on the crossover probability.
-		 */
-		void crossover(const int * const parentA, const int * const parentB, int * const offspringA, int * const offspringB);
-
-		/* Checks if the supplied chromosone is in newPopulation */
-		bool hasDuplicate(const int * const chromosone, size_t populationCount);
-
-		/* Copies the supplied chromosone to the new population */
-		void copyToNewPopulation(const int * const chromosone, size_t index);
-
-		/* Make the chromosone represent a path, which is chosen by random */
-		static void setRandomPath(int * const chromosone);
-};
-
-using namespace std;
-
-TSP::TSP(double crossoverProbability, double mutationProbability) : crossoverProbability(crossoverProbability),
-	mutationProbability(mutationProbability), solutions(new int[chromosones][cities]), newPopulation(new int[chromosones][cities])
-{
-	/* Seed the random number generator */
-  //srand((unsigned int)time(NULL));
-  srand(17);
-	/* Use the same number to generate a specific sequence */
-	//srand(0);
-	/* Set random coordinates */
-	for(size_t coordinateIndex = 0; coordinateIndex < cities; ++coordinateIndex)
-	{
-		/* 0 <= x <= xMax */
-		citiesX[coordinateIndex] = randomInclusive(xMax);
-		/* 0 <= y <= yMax */
-		citiesY[coordinateIndex] = randomInclusive(yMax);
-	}
-
-	/* Generate random population */
-	randomPopulation();
-}
-
-void TSP::randomPopulation()
-{
-	/* Iterate throught each chromosone... */
-	for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
-	{
-		/* ... and give it a random path */
-		setRandomPath(solutions[chromosoneIndex]);
-	}
-}
-
-double TSP::getBestFitness() const
-{
-	return evaluateFitness(bestChromosone);
-}
-
-double TSP::getAverageDistance() const
-{
-	double distance = 0;
-	for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
-	{
-		distance += totalDistance(solutions[chromosoneIndex]);
-	}
-	return distance/chromosones;
-}
-
-string TSP::getBestPathString() const
-{
-	stringstream path;
-	for(size_t gene = 0; gene < cities; ++gene)
-	{
-		if(gene != 0)
-		{
-			path << ",";
-		}
-		path << bestChromosone[gene];
-	}
-	return path.str();
-}
-
-double TSP::getLowestTotalDistance() const
-{
-	return totalDistance(bestChromosone);
-}
-
-void TSP::nextPopulation()
-{
-	double fitness[chromosones];
-	/* Fill an array with a fitness score for each chromosone,
-	 * the index of a score corresponds with the chromosone's index in solutions[index]
-	 */
-	for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
-	{
-		fitness[chromosoneIndex] = evaluateFitness(solutions[chromosoneIndex]);
-	}
-	
-	/* Use elitism, find and copy over the two best chromosones to the new population */
-	int eliteIndex1 = 0, eliteIndex2 = 0;
-	/* find the best solution */
-	eliteIndex1 = max_element(fitness, fitness + chromosones) - fitness;
-	this->bestChromosone = solutions[eliteIndex1];
-
-	double highestFitness = 0;
-	/* Find the second best solution */
-	for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
-	{
-		if(chromosoneIndex != eliteIndex1 && fitness[chromosoneIndex] > highestFitness)
-		{
-			highestFitness = fitness[chromosoneIndex];
-			eliteIndex2 = chromosoneIndex;
-		}
-	}
-
-	/* Keep track of how many chromosones exists in the new population */
-	size_t offspringCount = 0;
-	/* Copy over the two best solutions to the new population */
-	copyToNewPopulation(solutions[eliteIndex1], offspringCount);
-	++offspringCount;
-	copyToNewPopulation(solutions[eliteIndex2], offspringCount);
-	++offspringCount;
-
-	/* Create the rest of the new population, break this loop when the new population is complete */
-	while(true)
-	{
-		int * parentA;
-		int * parentB;
-		parentA = rouletteSelection(fitness);
-		parentB = rouletteSelection(fitness);
-		while (parentB == parentA)
-		{
-			parentB = rouletteSelection(fitness);
-		}
-		int offspringA[cities];
-		int offspringB[cities];
-		crossover(parentA, parentB, offspringA, offspringB);
-		mutate(offspringA);
-		mutate(offspringB);
-		
-		/* Add to new population if an equal chromosone doesn't exist already */
-		if(!hasDuplicate(offspringA, offspringCount))
-		{
-			copyToNewPopulation(offspringA, offspringCount);
-			++offspringCount;
-		}
-		/* We need to check if the new population is filled */
-		if(offspringCount == chromosones)
-		{
-			break;
-		}
-		if(!hasDuplicate(offspringB, offspringCount))
-		{
-			copyToNewPopulation(offspringB, offspringCount);
-			++offspringCount;
-		}
-		/* Check again so that we don't accidentaly write all over the heap and have to spend an evening wondering why the heap is corrupt... :) */
-		if(offspringCount == chromosones)
-		{
-			break;
-		}
-	}
-
-	/*
-	 * We now have a new population,
-	 * now it needs to replace the current population
-	 * so that we don't go through the same population every time we run this function
-	 */
-	for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
-	{
-		memcpy(solutions[chromosoneIndex], newPopulation[chromosoneIndex], sizeof(int) * cities);
-	}
-}
-
-bool TSP::hasDuplicate(const int * const chromosone, size_t populationCount)
-{
-	/* Iterate throught each chromosone in newPopulation and compare them gene by gene */
-	for(size_t chromosoneIndex = 0; chromosoneIndex < populationCount; ++chromosoneIndex)
-	{
-		int genesCompared = 0;
-		for(size_t gene = 0; gene < cities; ++gene)
-		{
-			if(chromosone[gene] != newPopulation[chromosoneIndex][gene])
-			{
-				/* These chromosones are not equal! */
-				break;
-			}
-			++genesCompared;
-		}
-
-		if(genesCompared == cities)
-		{
-			return true;
-		}
-	}
-
-	return false;
-}
-
-void TSP::mutate(int * const chromosone)
-{
-	/* 0.0 <= random <= 1 */
-	{
-		double random = randomInclusive(1);
-		/* Nope, didn't happen */
-		if(random > mutationProbability)
-		{
-			return;
-		}
-	}
-
-	int tmp;
-	int random1 = (int)randomExclusive(cities);
-	int random2 = (int)randomExclusive(cities);
-	while(random1 == random2)
-	{
-		random2 = (int)randomExclusive(cities);
-	}
-
-	tmp = chromosone[random1];
-	chromosone[random1] = chromosone[random2];
-	chromosone[random2] = tmp;
-
-}
-
-void TSP::crossover(int const * const parentA, const int * const parentB, int * offspringA, int * offspringB)
-{
-	{
-		/* There is a chance we don't perform a crossover,
-		 * in that case the offspring is a copy of the parents
-		 */
-		/* 0.0 <= random <= 1 */
-		double random = randomInclusive(1);
-		/* The offspring is a copy of their parents */
-		if(random > crossoverProbability)
-		{
-			memcpy(offspringA, parentA, sizeof(int) * cities);
-			memcpy(offspringB, parentB, sizeof(int) * cities);
-			return;
-		}
-	}
-	/* Perform multi-point crossover to generate offspring */
-
-	/* 0 <= cuttOffIndex <= cities */
-	int cuttOffIndex1 = (int)randomInclusive(cities);
-	int cuttOffIndex2 = (int)randomInclusive(cities);
-	while(cuttOffIndex2 == cuttOffIndex1)
-	{
-		cuttOffIndex2 = (int)randomExclusive(cities);
-	}
-
-	unsigned int start;
-	unsigned int end;
-	if(cuttOffIndex1 < cuttOffIndex2)
-	{
-		start = cuttOffIndex1;
-		end = cuttOffIndex2;
-	}
-	else
-	{
-		start = cuttOffIndex2;
-		end = cuttOffIndex1;
-	}
-	/* Offspring A is initially copy of parent A */
-	memcpy(offspringA, parentA, sizeof(int) * cities);
-	/* Offspring B is initially copy of parent B */
-	memcpy(offspringB, parentB, sizeof(int) * cities);
-
-	/* Put a sequence of parent B in offspring A */
-	memcpy(offspringA + start, parentB + start, sizeof(int) * (end - start));
-	/* Put a sequence of parent A in offspring B */
-	memcpy(offspringB + start, parentA + start, sizeof(int) * (end - start));
-
-	/* Mark collisions in offspring with -1*/
-	for(size_t cityIndex = 0; cityIndex  < cities; ++cityIndex)
-	{
-		/* Index is part of the parent sequence */
-		if((cityIndex  >= start && cityIndex  < end)) {
-			/* Do nothing, we want to keep this sequence intact */
-		}
-		else
-		{
-			/* Check if the item at cityIndex also occurs somewhere in the copied substring */
-			for(size_t substringIndex = start; substringIndex < end; ++substringIndex)
-			{
-				/* A duplicate, mark it */
-				if(offspringA[cityIndex] == offspringA[substringIndex])
-				{
-					offspringA[cityIndex] = -1;
-				}
-				if(offspringB[cityIndex] == offspringB[substringIndex])
-				{
-					offspringB[cityIndex] = -1;
-				}
-			}
-		}
-
-	}
-
-	/*
-	* Go through the offspring,
-	* if an element is marked we fill the hole with an element from the other offspring
-	*/
-	for(size_t offspringIndex = 0; offspringIndex < cities; ++offspringIndex)
-	{
-		/* There is a hole here */
-		if(offspringA[offspringIndex] == -1)
-		{
-			repairOffspring(offspringA, offspringIndex, offspringB);
-		}
-		if(offspringB[offspringIndex] == -1)
-		{
-			repairOffspring(offspringB, offspringIndex, offspringA);
-		}
-	}
-}
-
-void TSP::repairOffspring(int * const offspringToRepair, int missingIndex, const int * const other)
-{
-	/* Iterate through the other offspring until we find an element which doesn't exist in the offspring we are repairing */
-	for(size_t patchIndex = 0; patchIndex < cities; ++patchIndex)
-	{
-		/* Look for other[patchIndex] in offspringToRepair */
-		int *missing = find(offspringToRepair, offspringToRepair + cities, other[patchIndex]);
-
-		/* The element at other[patchIndex] is missing from offspringToRepair */
-		if(missing == (offspringToRepair + cities))
-		{
-			//cout << "1:" << offspringToRepair[missingIndex] << endl;
-			offspringToRepair[missingIndex] = other[patchIndex];
-			//cout << "2:" << offspringToRepair[missingIndex] << endl;
-			break;
-		}
-	}
-}
-
-void TSP::copyToNewPopulation(int const * const chromosone, size_t index)
-{
-	assert(index < chromosones && "Index out of bounds");
-	for(size_t i = 0; i < cities; ++i)
-	{
-		newPopulation[index][i] = chromosone[i];
-	}
-
-}
-
-int * TSP::rouletteSelection(double const * const fitness) const
-{
-	double sum = 0;
-	/* Calculate sum of all chromosome fitnesses in population */
-	for(size_t i = 0; i < chromosones; ++i)
-	{
-		sum += fitness[i];
-	}
-
-	/* 0.0 <= random <= sum */
-	double random = randomInclusive(sum);
-
-	sum = 0;
-	/* Go through the population and sum fitnesses from 0 to sum s. When the sum s is greater or equal to r; stop and return the chromosome where you are */
-	for(size_t i = 0; i < chromosones; ++i)
-	{
-		sum += fitness[i];
-		if(sum >= random)
-		{
-			return solutions[i];
-		}
-	}
-	assert(false && "A chromosone should have been picked by now");
-	return(NULL);
-}
-
-void TSP::setRandomPath(int * chromosone)
-{
-	for(size_t i = 0; i < cities; ++i)
-	{
-		chromosone[i] = i;
-	}
-
-	/*
-	 * Shuffle the chromosone using the Fisher–Yates shuffle.
-	 */
-	for(size_t i = cities-1; i > 0; --i)
-	{
-		/* 0 <= random <= i */
-		int random = (int)randomInclusive(i);
-		int temp = chromosone[i];
-		chromosone[i] = chromosone[random];
-		chromosone[random] = temp;
-	}
-}
-
-double TSP::evaluateFitness(int const * const chromosone) const
-{
-	return 1/totalDistance(chromosone);
-}
-
-double TSP::totalDistance(int const * const chromosone) const
-{
-	double distance = 0;
-	/* Calculate the total distance between all cities */
-	for(size_t i = 0; i < cities-1; ++i)
-	{
-		double dx = citiesX[chromosone[i]] - citiesX[chromosone[i+1]];
-		double dy = citiesY[chromosone[i]] - citiesY[chromosone[i+1]];
-
-		/* The distance between two points is the square root of (dx^2+dy^2) */
-		distance += sqrt((pow(dx, 2.0) + pow(dy, 2.0)));
-	}
-	/* We complete the tour by adding the distance between the last and the first city */
-	double dx = citiesX[chromosone[cities-1]] - citiesX[chromosone[0]];
-	double dy = citiesY[chromosone[cities-1]] - citiesY[chromosone[0]];
-	distance += sqrt((pow(dx, 2.0) + pow(dy, 2.0)));
-
-	return distance;
-}
-
-double TSP::randomInclusive(double max)
-{
-	/* Generate random number r, 0.0 <= r <= max */
-	//return ((double)rand() / (double)RAND_MAX * max);
-	return ((double)rand() * max) / (double)RAND_MAX;
-}
-
-double TSP::randomExclusive(double max)
-{
-	/* Generate random number r, 0.0 <= r < max */
-	//return ((double)rand() / ((double)RAND_MAX + 1) * max);
-	return ((double)rand() * max) / ((double)RAND_MAX + 1);
-}
-
-int main(int argc, const char *argv[])
-{
-	/* 90% mutation probability, 2% mutation probability */
-	TSP *tsp = new TSP(0.9, 0.02);
-	size_t generations = 0, generationsWithoutImprovement = 0;
-	double bestFitness = -1;
-	double initialAverage = tsp->getAverageDistance();
-	/* We'll stop when we've gone 10k generations without improvement */
-	while(generations < 10000)
-	{
-		tsp->nextPopulation();
-		++generations;
-		double newFitness = tsp->getBestFitness();
-		/* The new fitness is higher, the chromosone is better */
-		if(newFitness > bestFitness)
-		{
-			bestFitness = newFitness;
-			generationsWithoutImprovement = 0;
-		 //cout << "Best goal function: " << tsp->getBestFitness() << endl;
-		}
-		else
-		{
-			++generationsWithoutImprovement;
-		}
-	}
- //cout << "DONE!" << endl;
-	cout << "Number of generations: " << generations << endl;
-	cout << "Best chromosone info: " << endl;
-	cout << "\t-Path: " << tsp->getBestPathString() << endl;
-	cout << "\t-Goal function: " << tsp->getBestFitness() << endl;
-	cout << "\t-Distance: " << tsp->getLowestTotalDistance() << endl;
-	cout << "Average distance: " << tsp->getAverageDistance() << endl;
-	cout << "Initial average: " << initialAverage << endl;
-	delete tsp;
-	return 0;
-}
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/cc_param_defaults.json b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/cc_param_defaults.json
deleted file mode 100644
index 067a26573a08ea6956e407833fa9ca17faafa4bf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/cc_param_defaults.json
+++ /dev/null
@@ -1 +0,0 @@
-{"max-pipeline-region-insns": {"default": 200, "max": 0, "min": 0}, "ipa-cp-loop-hint-bonus": {"default": 64, "max": 0, "min": 0}, "lim-expensive": {"default": 20, "max": 0, "min": 0}, "uninit-control-dep-attempts": {"default": 1000, "max": 0, "min": 1}, "lto-partitions": {"default": 32, "max": 0, "min": 1}, "max-inline-recursive-depth-auto": {"default": 8, "max": 0, "min": 0}, "max-unroll-times": {"default": 8, "max": 0, "min": 0}, "max-tail-merge-comparisons": {"default": 10, "max": 0, "min": 0}, "early-inlining-insns": {"default": 11, "max": 0, "min": 0}, "prefetch-latency": {"default": 200, "max": 0, "min": 0}, "partial-inlining-entry-probability": {"default": 70, "max": 0, "min": 0}, "integer-share-limit": {"default": 251, "max": 2, "min": 2}, "tm-max-aggregate-size": {"default": 9, "max": 0, "min": 0}, "ira-max-conflict-table-size": {"default": 1000, "max": 0, "min": 0}, "asan-instrument-reads": {"default": 1, "max": 1, "min": 0}, "lto-min-partition": {"default": 1000, "max": 0, "min": 0}, "hot-bb-frequency-fraction": {"default": 1000, "max": 0, "min": 0}, "min-vect-loop-bound": {"default": 1, "max": 0, "min": 1}, "max-crossjump-edges": {"default": 100, "max": 0, "min": 0}, "sms-dfa-history": {"default": 0, "max": 0, "min": 0}, "tracer-max-code-growth": {"default": 100, "max": 0, "min": 0}, "max-pipeline-region-blocks": {"default": 15, "max": 0, "min": 0}, "gcse-after-reload-partial-fraction": {"default": 3, "max": 0, "min": 0}, "asan-stack": {"default": 1, "max": 1, "min": 0}, "asan-memintrin": {"default": 1, "max": 1, "min": 0}, "large-function-insns": {"default": 2700, "max": 0, "min": 0}, "scev-max-expr-size": {"default": 100, "max": 0, "min": 0}, "iv-consider-all-candidates-bound": {"default": 30, "max": 0, "min": 0}, "max-partial-antic-length": {"default": 100, "max": 0, "min": 0}, "prefetch-min-insn-to-mem-ratio": {"default": 3, "max": 0, "min": 0}, "min-crossjump-insns": {"default": 5, "max": 0, "min": 1}, "asan-use-after-return": {"default": 1, "max": 1, "min": 0}, "allow-load-data-races": {"default": 1, "max": 1, "min": 0}, "max-jump-thread-duplication-stmts": {"default": 15, "max": 0, "min": 0}, "tracer-min-branch-probability": {"default": 50, "max": 100, "min": 0}, "l2-cache-size": {"default": 512, "max": 0, "min": 0}, "max-cse-insns": {"default": 1000, "max": 0, "min": 0}, "sched-pressure-algorithm": {"default": 1, "max": 2, "min": 1}, "max-unrolled-insns": {"default": 200, "max": 0, "min": 0}, "ipa-cp-value-list-size": {"default": 8, "max": 0, "min": 0}, "graphite-max-nb-scop-params": {"default": 10, "max": 0, "min": 0}, "max-completely-peel-times": {"default": 16, "max": 0, "min": 0}, "min-inline-recursive-probability": {"default": 10, "max": 0, "min": 0}, "max-stores-to-sink": {"default": 2, "max": 0, "min": 0}, "sink-frequency-threshold": {"default": 75, "max": 100, "min": 0}, "builtin-expect-probability": {"default": 90, "max": 100, "min": 0}, "max-average-unrolled-insns": {"default": 80, "max": 0, "min": 0}, "tracer-min-branch-ratio": {"default": 10, "max": 100, "min": 0}, "inline-unit-growth": {"default": 30, "max": 0, "min": 0}, "max-early-inliner-iterations": {"default": 1, "max": 0, "min": 0}, "hot-bb-count-ws-permille": {"default": 999, "max": 1000, "min": 0}, "max-gcse-memory": {"default": 52428800, "max": 0, "min": 0}, "ggc-min-expand": {"default": 30, "max": 0, "min": 0}, "tree-reassoc-width": {"default": 0, "max": 0, "min": 0}, "max-once-peeled-insns": {"default": 400, "max": 0, "min": 0}, "max-inline-recursive-depth": {"default": 8, "max": 0, "min": 0}, "max-inline-insns-recursive": {"default": 450, "max": 0, "min": 0}, "ira-loop-reserved-regs": {"default": 2, "max": 0, "min": 0}, "align-loop-iterations": {"default": 4, "max": 0, "min": 0}, "gcse-cost-distance-ratio": {"default": 10, "max": 0, "min": 0}, "sched-mem-true-dep-cost": {"default": 1, "max": 0, "min": 0}, "gcse-unrestricted-cost": {"default": 3, "max": 0, "min": 0}, "max-inline-insns-recursive-auto": {"default": 450, "max": 0, "min": 0}, "max-cse-path-length": {"default": 10, "max": 0, "min": 1}, "switch-conversion-max-branch-ratio": {"default": 8, "max": 0, "min": 1}, "max-tracked-strlens": {"default": 1000, "max": 0, "min": 0}, "inline-min-speedup": {"default": 10, "max": 0, "min": 0}, "max-cselib-memory-locations": {"default": 500, "max": 0, "min": 0}, "max-tail-merge-iterations": {"default": 2, "max": 0, "min": 0}, "max-inline-insns-auto": {"default": 40, "max": 0, "min": 0}, "min-insn-to-prefetch-ratio": {"default": 9, "max": 0, "min": 0}, "max-slsr-cand-scan": {"default": 50, "max": 999999, "min": 1}, "min-nondebug-insn-uid": {"default": 0, "max": 0, "min": 1}, "max-sched-region-blocks": {"default": 10, "max": 0, "min": 0}, "vect-max-version-for-alignment-checks": {"default": 6, "max": 0, "min": 0}, "max-vartrack-size": {"default": 50000000, "max": 0, "min": 0}, "loop-max-datarefs-for-datadeps": {"default": 1000, "max": 0, "min": 0}, "asan-instrument-writes": {"default": 1, "max": 1, "min": 0}, "asan-globals": {"default": 1, "max": 1, "min": 0}, "large-function-growth": {"default": 100, "max": 0, "min": 0}, "max-last-value-rtl": {"default": 10000, "max": 0, "min": 0}, "selsched-max-sched-times": {"default": 2, "max": 0, "min": 0}, "sms-max-ii-factor": {"default": 100, "max": 0, "min": 0}, "max-hoist-depth": {"default": 30, "max": 0, "min": 0}, "comdat-sharing-probability": {"default": 20, "max": 0, "min": 0}, "allow-store-data-races": {"default": 1, "max": 1, "min": 0}, "omega-max-vars": {"default": 128, "max": 0, "min": 0}, "iv-max-considered-uses": {"default": 250, "max": 0, "min": 0}, "max-inline-insns-single": {"default": 400, "max": 0, "min": 0}, "simultaneous-prefetches": {"default": 3, "max": 0, "min": 0}, "ipa-max-agg-items": {"default": 16, "max": 0, "min": 0}, "max-peel-times": {"default": 16, "max": 0, "min": 0}, "min-size-for-stack-sharing": {"default": 32, "max": 0, "min": 0}, "ira-max-loops-num": {"default": 100, "max": 0, "min": 0}, "tracer-dynamic-coverage": {"default": 75, "max": 100, "min": 0}, "max-gcse-insertion-ratio": {"default": 20, "max": 0, "min": 0}, "tracer-min-branch-probability-feedback": {"default": 80, "max": 100, "min": 0}, "max-sched-insn-conflict-delay": {"default": 3, "max": 10, "min": 1}, "max-peeled-insns": {"default": 100, "max": 0, "min": 0}, "max-dse-active-local-stores": {"default": 5000, "max": 0, "min": 0}, "max-variable-expansions-in-unroller": {"default": 1, "max": 0, "min": 0}, "max-delay-slot-live-search": {"default": 333, "max": 0, "min": 0}, "min-spec-prob": {"default": 40, "max": 0, "min": 0}, "loop-invariant-max-bbs-in-loop": {"default": 10000, "max": 0, "min": 0}, "selsched-insns-to-rename": {"default": 2, "max": 0, "min": 0}, "max-completely-peel-loop-nest-depth": {"default": 8, "max": 0, "min": 0}, "allow-packed-store-data-races": {"default": 1, "max": 1, "min": 0}, "omega-eliminate-redundant-constraints": {"default": 0, "max": 1, "min": 0}, "omega-max-geqs": {"default": 256, "max": 0, "min": 0}, "l1-cache-line-size": {"default": 32, "max": 0, "min": 0}, "case-values-threshold": {"default": 0, "max": 0, "min": 0}, "max-pending-list-length": {"default": 32, "max": 0, "min": 0}, "sccvn-max-alias-queries-per-access": {"default": 1000, "max": 0, "min": 0}, "max-vartrack-expr-depth": {"default": 12, "max": 0, "min": 0}, "loop-block-tile-size": {"default": 51, "max": 0, "min": 0}, "sms-loop-average-count-threshold": {"default": 0, "max": 0, "min": 0}, "vect-max-peeling-for-alignment": {"default": -1, "max": 64, "min": -1}, "selsched-max-lookahead": {"default": 50, "max": 0, "min": 0}, "omega-max-keys": {"default": 500, "max": 0, "min": 0}, "sccvn-max-scc-size": {"default": 10000, "max": 0, "min": 10}, "predictable-branch-outcome": {"default": 2, "max": 50, "min": 0}, "ssp-buffer-size": {"default": 8, "max": 0, "min": 1}, "max-delay-slot-insn-search": {"default": 100, "max": 0, "min": 0}, "sms-min-sc": {"default": 2, "max": 1, "min": 1}, "lra-max-considered-reload-pseudos": {"default": 500, "max": 0, "min": 0}, "tracer-dynamic-coverage-feedback": {"default": 95, "max": 100, "min": 0}, "omega-max-eqs": {"default": 128, "max": 0, "min": 0}, "max-fields-for-field-sensitive": {"default": 0, "max": 0, "min": 0}, "max-sched-region-insns": {"default": 100, "max": 0, "min": 0}, "large-stack-frame-growth": {"default": 1000, "max": 0, "min": 0}, "omega-max-wild-cards": {"default": 18, "max": 0, "min": 0}, "max-sched-extend-regions-iters": {"default": 0, "max": 0, "min": 0}, "max-unswitch-insns": {"default": 50, "max": 0, "min": 0}, "ipcp-unit-growth": {"default": 10, "max": 0, "min": 0}, "max-unswitch-level": {"default": 3, "max": 0, "min": 0}, "l1-cache-size": {"default": 64, "max": 0, "min": 0}, "max-grow-copy-bb-insns": {"default": 8, "max": 0, "min": 0}, "max-iterations-computation-cost": {"default": 10, "max": 0, "min": 0}, "ipa-cp-array-index-hint-bonus": {"default": 48, "max": 0, "min": 0}, "ggc-min-heapsize": {"default": 4096, "max": 0, "min": 0}, "align-threshold": {"default": 100, "max": 0, "min": 1}, "graphite-max-bbs-per-function": {"default": 100, "max": 0, "min": 0}, "max-vartrack-reverse-op-size": {"default": 50, "max": 0, "min": 0}, "ipa-sra-ptr-growth-factor": {"default": 2, "max": 0, "min": 0}, "max-completely-peeled-insns": {"default": 100, "max": 0, "min": 0}, "ipa-cp-eval-threshold": {"default": 500, "max": 0, "min": 0}, "large-stack-frame": {"default": 256, "max": 0, "min": 0}, "max-modulo-backtrack-attempts": {"default": 40, "max": 0, "min": 0}, "omega-hash-table-size": {"default": 550, "max": 0, "min": 0}, "max-goto-duplication-insns": {"default": 8, "max": 0, "min": 0}, "max-sched-ready-insns": {"default": 100, "max": 0, "min": 0}, "max-iterations-to-track": {"default": 1000, "max": 0, "min": 0}, "scev-max-expr-complexity": {"default": 10, "max": 0, "min": 0}, "cxx-max-namespaces-for-diagnostic-help": {"default": 1000, "max": 0, "min": 0}, "max-reload-search-insns": {"default": 100, "max": 0, "min": 0}, "use-canonical-types": {"default": 1, "max": 1, "min": 0}, "gcse-after-reload-critical-fraction": {"default": 10, "max": 0, "min": 0}, "sched-state-edge-prob-cutoff": {"default": 10, "max": 100, "min": 0}, "sched-spec-prob-cutoff": {"default": 40, "max": 100, "min": 0}, "unlikely-bb-count-fraction": {"default": 20, "max": 10000, "min": 1}, "slp-max-insns-in-bb": {"default": 1000, "max": 0, "min": 0}, "max-peel-branches": {"default": 32, "max": 0, "min": 0}, "large-unit-insns": {"default": 10000, "max": 0, "min": 0}, "iv-always-prune-cand-set-bound": {"default": 10, "max": 0, "min": 0}, "vect-max-version-for-alias-checks": {"default": 10, "max": 0, "min": 0}, "max-predicted-iterations": {"default": 100, "max": 0, "min": 0}, "allow-packed-load-data-races": {"default": 1, "max": 1, "min": 0}}
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags.py
deleted file mode 100755
index 1edc5bb8a3ce886e968a5f7d7d2e4f362ff8940b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags.py
+++ /dev/null
@@ -1,412 +0,0 @@
-#!/usr/bin/env python
-import adddeps  # fix sys.path
-
-import math
-import argparse
-import ast
-import collections
-import json
-import logging
-import opentuner
-import os
-import random
-import re
-import shutil
-import subprocess
-import sys
-
-from opentuner.resultsdb.models import Result, TuningRun
-from opentuner.search import manipulator
-
-FLAGS_WORKING_CACHE_FILE = 'cc_flags.json'
-PARAMS_DEFAULTS_CACHE_FILE = 'cc_param_defaults.json'
-PARAMS_DEF_PATH = '~/gcc-4.9.0/gcc/params.def'
-PARAMS_WORKING_CACHE_FILE = 'cc_params.json'
-
-log = logging.getLogger('gccflags')
-
-argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-argparser.add_argument('source', help='source file to compile')
-argparser.add_argument('--compile-template',
-                       default='{cc} {source} -o {output} -lpthread {flags}',
-                       help='command to compile {source} into {output} with'
-                            ' {flags}')
-argparser.add_argument('--compile-limit', type=float, default=30,
-                       help='kill gcc if it runs more than {default} sec')
-argparser.add_argument('--scaler', type=int, default=4,
-                       help='by what factor to try increasing parameters')
-argparser.add_argument('--cc', default='g++', help='g++ or gcc')
-argparser.add_argument('--output', default='./tmp.bin',
-                       help='temporary file for compiler to write to')
-argparser.add_argument('--debug', action='store_true',
-                       help='on gcc errors try to find minimal set '
-                            'of args to reproduce error')
-argparser.add_argument('--force-killall', action='store_true',
-                       help='killall cc1plus before each collection')
-argparser.add_argument('--memory-limit', default=1024 ** 3, type=int,
-                       help='memory limit for child process')
-argparser.add_argument('--no-cached-flags', action='store_true',
-                       help='regenerate the lists of legal flags each time')
-argparser.add_argument('--flags-histogram', action='store_true',
-                       help='print out a histogram of flags')
-argparser.add_argument('--flag-importance',
-                       help='Test the importance of different flags from a '
-                            'given json file.')
-
-
-class GccFlagsTuner(opentuner.measurement.MeasurementInterface):
-  def __init__(self, *pargs, **kwargs):
-    super(GccFlagsTuner, self).__init__(program_name=args.source, *pargs,
-                                        **kwargs)
-    self.gcc_version = self.extract_gcc_version()
-    self.cc_flags = self.extract_working_flags()
-    self.cc_param_defaults = self.extract_param_defaults()
-    self.cc_params = self.extract_working_params()
-
-    # these bugs are hardcoded for now
-    # sets of options which causes gcc to barf
-    if True:
-      # These bugs were for gcc 4.7 on ubuntu
-      self.cc_bugs = (['-fipa-matrix-reorg', '-fwhole-program'],
-                      ['-fno-tree-coalesce-inlined-vars'],
-                      ['-fno-inline-atomics'],
-                      ['-ftoplevel-reorder', '-fno-unit-at-a-time'])
-    else:
-      # Bugs for gcc 4.9 (work in progress, incomplete list)
-      self.cc_bugs = (['-ftoplevel-reorder', '-fno-unit-at-a-time'], )
-
-    self.result_list = {}
-    self.parallel_compile = True
-    try:
-      os.stat('./tmp')
-    except OSError:
-      os.mkdir('./tmp')
-    self.run_baselines()
-
-  def run_baselines(self):
-    log.info("baseline perfs -O0=%.4f -O1=%.4f -O2=%.4f -O3=%.4f",
-             *[self.run_with_flags(['-O%d' % i], None).time
-               for i in range(4)])
-
-  def extract_gcc_version(self):
-    m = re.search(r'([0-9]+)[.]([0-9]+)[.]([0-9]+)', subprocess.check_output([
-        self.args.cc, '--version']))
-    if m:
-      gcc_version = tuple(map(int, m.group(1, 2, 3)))
-    else:
-      gcc_version = None
-    log.debug('gcc version %s', gcc_version)
-    return gcc_version
-
-  def extract_working_flags(self):
-    """
-    Figure out which gcc flags work (don't cause gcc to barf) by running
-    each one.
-    """
-    if os.path.isfile(FLAGS_WORKING_CACHE_FILE) and not args.no_cached_flags:
-      # use cached version
-      found_cc_flags = json.load(open(FLAGS_WORKING_CACHE_FILE))
-    else:
-      # extract flags from --help=optimizers
-      optimizers, err = subprocess.Popen([self.args.cc, '--help=optimizers'],
-                                         stdout=subprocess.PIPE).communicate()
-      found_cc_flags = re.findall(r'^  (-f[a-z0-9-]+) ', optimizers,
-                                  re.MULTILINE)
-      log.info('Determining which of %s possible gcc flags work',
-               len(found_cc_flags))
-      found_cc_flags = filter(self.check_if_flag_works, found_cc_flags)
-      json.dump(found_cc_flags, open(FLAGS_WORKING_CACHE_FILE, 'w'))
-    return found_cc_flags
-
-  def extract_param_defaults(self):
-    """
-    Get the default, minimum, and maximum for each gcc parameter.
-    Requires source code for gcc to be in your home directory.
-    This example ships with a cached version so it does not require source.
-    """
-    if os.path.isfile(PARAMS_DEFAULTS_CACHE_FILE) and not args.no_cached_flags:
-      # use cached version
-      param_defaults = json.load(open(PARAMS_DEFAULTS_CACHE_FILE))
-    else:
-      # default values of params need to be extracted from source code,
-      # since they are not in --help
-      param_defaults = dict()
-      params_def = open(os.path.expanduser(PARAMS_DEF_PATH)).read()
-      for m in re.finditer(r'DEFPARAM *\((([^")]|"[^"]*")*)\)', params_def):
-        param_def_str = (m.group(1)
-                         #  Hacks!!!
-                         .replace('GGC_MIN_EXPAND_DEFAULT', '30')
-                         .replace('GGC_MIN_HEAPSIZE_DEFAULT', '4096')
-                         .replace('50 * 1024 * 1024', '52428800'))
-        try:
-          name, desc, default, param_min, param_max = ast.literal_eval(
-              '[' + param_def_str.split(',', 1)[1] + ']')
-          param_defaults[name] = {'default': default,
-                                  'min': param_min,
-                                  'max': param_max}
-        except:
-          log.exception("error with %s", param_def_str)
-      json.dump(param_defaults, open(PARAMS_DEFAULTS_CACHE_FILE, 'w'))
-    return param_defaults
-
-  def extract_working_params(self):
-    """
-    Figure out which gcc params work (don't cause gcc to barf) by running
-    each one to test.
-    """
-    params, err = subprocess.Popen(
-        [self.args.cc, '--help=params'], stdout=subprocess.PIPE).communicate()
-    all_params = re.findall(r'^  ([a-z0-9-]+) ', params, re.MULTILINE)
-    all_params = sorted(set(all_params) &
-                        set(self.cc_param_defaults.keys()))
-    if os.path.isfile(PARAMS_WORKING_CACHE_FILE) and not args.no_cached_flags:
-      # use cached version
-      return json.load(open(PARAMS_WORKING_CACHE_FILE))
-    else:
-      log.info('Determining which of %s possible gcc params work',
-               len(all_params))
-      working_params = []
-      for param in all_params:
-        if self.check_if_flag_works('--param={}={}'.format(
-                param, self.cc_param_defaults[param]['default'])):
-          working_params.append(param)
-      json.dump(working_params, open(PARAMS_WORKING_CACHE_FILE, 'w'))
-      return working_params
-
-  def check_if_flag_works(self, flag, try_inverted=True):
-    cmd = args.compile_template.format(source=args.source, output=args.output,
-                                       flags=flag, cc=args.cc)
-    compile_result = self.call_program(cmd, limit=args.compile_limit)
-    if compile_result['returncode'] != 0:
-      log.warning("removing flag %s because it results in compile error", flag)
-      return False
-    if 'warning: this target' in compile_result['stderr']:
-      log.warning("removing flag %s because not supported by target", flag)
-      return False
-    if 'has been renamed' in compile_result['stderr']:
-      log.warning("removing flag %s because renamed", flag)
-      return False
-    if try_inverted and flag[:2] == '-f':
-      if not self.check_if_flag_works(invert_gcc_flag(flag),
-                                      try_inverted=False):
-        log.warning("Odd... %s works but %s does not", flag,
-                    invert_gcc_flag(flag))
-        return False
-    return True
-
-  def manipulator(self):
-    m = manipulator.ConfigurationManipulator()
-    m.add_parameter(manipulator.IntegerParameter('-O', 0, 3))
-    for flag in self.cc_flags:
-      m.add_parameter(manipulator.EnumParameter(flag, ['on', 'off', 'default']))
-    for param in self.cc_params:
-      defaults = self.cc_param_defaults[param]
-      if defaults['max'] <= defaults['min']:
-        defaults['max'] = float('inf')
-      defaults['max'] = min(defaults['max'],
-                            max(1, defaults['default']) * args.scaler)
-      defaults['min'] = max(defaults['min'],
-                            max(1, defaults['default']) / args.scaler)
-
-      if param == 'l1-cache-line-size':
-        # gcc requires this to be a power of two or it internal errors
-        m.add_parameter(manipulator.PowerOfTwoParameter(param, 4, 256))
-      elif defaults['max'] > 128:
-        m.add_parameter(manipulator.LogIntegerParameter(
-            param, defaults['min'], defaults['max']))
-      else:
-        m.add_parameter(manipulator.IntegerParameter(
-            param, defaults['min'], defaults['max']))
-
-    return m
-
-  def cfg_to_flags(self, cfg):
-    flags = ['-O%d' % cfg['-O']]
-    for flag in self.cc_flags:
-      if cfg[flag] == 'on':
-        flags.append(flag)
-      elif cfg[flag] == 'off':
-        flags.append(invert_gcc_flag(flag))
-
-    for param in self.cc_params:
-      flags.append('--param=%s=%d' % (param, cfg[param]))
-
-    # workaround sets of flags that trigger compiler crashes/hangs
-    for bugset in self.cc_bugs:
-      if len(set(bugset) & set(flags)) == len(bugset):
-        flags.remove(bugset[-1])
-    return flags
-
-  def make_command(self, cfg):
-    return args.compile_template.format(source=args.source, output=args.output,
-                                        flags=' '.join(self.cfg_to_flags(cfg)),
-                                        cc=args.cc)
-
-  def get_tmpdir(self, result_id):
-    return './tmp/%d' % result_id
-
-  def cleanup(self, result_id):
-    tmp_dir = self.get_tmpdir(result_id)
-    shutil.rmtree(tmp_dir)
-
-  def compile_and_run(self, desired_result, input, limit):
-    cfg = desired_result.configuration.data
-    compile_result = self.compile(cfg, 0)
-    return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
-  compile_results = {'ok': 0, 'timeout': 1, 'error': 2}
-
-  def run_precompiled(self, desired_result, input, limit, compile_result,
-                      result_id):
-    if self.args.force_killall:
-      os.system('killall -9 cc1plus 2>/dev/null')
-    # Make sure compile was successful
-    if compile_result == self.compile_results['timeout']:
-      return Result(state='TIMEOUT', time=float('inf'))
-    elif compile_result == self.compile_results['error']:
-      return Result(state='ERROR', time=float('inf'))
-
-    tmp_dir = self.get_tmpdir(result_id)
-    output_dir = '%s/%s' % (tmp_dir, args.output)
-    try:
-      run_result = self.call_program([output_dir], limit=limit,
-                                     memory_limit=args.memory_limit)
-    except OSError:
-      return Result(state='ERROR', time=float('inf'))
-
-    if run_result['returncode'] != 0:
-      if run_result['timeout']:
-        return Result(state='TIMEOUT', time=float('inf'))
-      else:
-        log.error('program error')
-        return Result(state='ERROR', time=float('inf'))
-
-    return Result(time=run_result['time'])
-
-  def debug_gcc_error(self, flags):
-    def fails(subflags):
-      cmd = args.compile_template.format(source=args.source, output=args.output,
-                                         flags=' '.join(subflags),
-                                         cc=args.cc)
-      compile_result = self.call_program(cmd, limit=args.compile_limit)
-      return compile_result['returncode'] != 0
-
-    if self.args.debug:
-      while len(flags) > 8:
-        log.error("compile error with %d flags, diagnosing...", len(flags))
-        tmpflags = filter(lambda x: random.choice((True, False)), flags)
-        if fails(tmpflags):
-          flags = tmpflags
-
-      # linear scan
-      minimal_flags = []
-      for i in xrange(len(flags)):
-        tmpflags = minimal_flags + flags[i + 1:]
-        if not fails(tmpflags):
-          minimal_flags.append(flags[i])
-      log.error("compiler crashes/hangs with flags: %s", minimal_flags)
-
-  def compile(self, config_data, result_id):
-    flags = self.cfg_to_flags(config_data)
-    return self.compile_with_flags(flags, result_id)
-
-  def compile_with_flags(self, flags, result_id):
-    tmp_dir = self.get_tmpdir(result_id)
-    try:
-      os.stat(tmp_dir)
-    except OSError:
-      os.mkdir(tmp_dir)
-    output_dir = '%s/%s' % (tmp_dir, args.output)
-    cmd = args.compile_template.format(source=args.source, output=output_dir,
-                                       flags=' '.join(flags),
-                                       cc=args.cc)
-
-    compile_result = self.call_program(cmd, limit=args.compile_limit,
-                                       memory_limit=args.memory_limit)
-    if compile_result['returncode'] != 0:
-      if compile_result['timeout']:
-        log.warning("gcc timeout")
-        return self.compile_results['timeout']
-      else:
-        log.warning("gcc error %s", compile_result['stderr'])
-        self.debug_gcc_error(flags)
-        return self.compile_results['error']
-    return self.compile_results['ok']
-
-  def run_with_flags(self, flags, limit):
-    return self.run_precompiled(None, None, limit,
-                                self.compile_with_flags(flags, 0), 0)
-
-  def save_final_config(self, configuration):
-    """called at the end of tuning"""
-    print "Best flags written to gccflags_final_config.{json,cmd}"
-    self.manipulator().save_to_file(configuration.data,
-                                    'gccflags_final_config.json')
-    with open('gccflags_final_config.cmd', 'w') as fd:
-      fd.write(self.make_command(configuration.data))
-
-  def flags_histogram(self, session):
-    counter = collections.Counter()
-    q = session.query(TuningRun).filter_by(state='COMPLETE')
-    total = q.count()
-    for tr in q:
-      print tr.program.name
-      for flag in self.cfg_to_flags(tr.final_config.data):
-        counter[flag] += 1.0 / total
-    print counter.most_common(20)
-
-  def flag_importance(self):
-    """
-    Test the importance of each flag by measuring the performance with that
-    flag removed.  Print out a table for paper
-    """
-    with open(self.args.flag_importance) as fd:
-      best_cfg = json.load(fd)
-    flags = self.cfg_to_flags(best_cfg)
-    counter = collections.Counter()
-    baseline_time = self.flags_mean_time(flags)
-    for flag in flags[1:]:
-      delta_flags = [f for f in flags if f != flag]
-      flag_time = self.flags_mean_time(delta_flags)
-      impact = max(0.0, flag_time - baseline_time)
-      if math.isinf(impact):
-        impact = 0.0
-      counter[flag] = impact
-      print flag, '{:.4f}'.format(impact)
-    total_impact = sum(counter.values())
-    remaining_impact = total_impact
-    print r'\bf Flag & \bf Importance \\\hline'
-    for flag, impact in counter.most_common(20):
-      print r'{} & {:.1f}\% \\\hline'.format(flag, 100.0 * impact / total_impact)
-      remaining_impact -= impact
-    print r'{} other flags & {:.1f}% \\\hline'.format(
-      len(flags) - 20, 100.0 * remaining_impact / total_impact)
-
-  def flags_mean_time(self, flags, trials=10):
-    precompiled = self.compile_with_flags(flags, 0)
-    total = 0.0
-    for _ in xrange(trials):
-      total += self.run_precompiled(None, None, None, precompiled, 0).time
-    return total / trials
-
-  def prefix_hook(self, session):
-    if self.args.flags_histogram:
-      self.flags_histogram(session)
-      sys.exit(0)
-    if self.args.flag_importance:
-      self.flag_importance()
-      sys.exit(0)
-
-
-
-def invert_gcc_flag(flag):
-  assert flag[:2] == '-f'
-  if flag[2:5] != 'no-':
-    return '-fno-' + flag[2:]
-  return '-f' + flag[5:]
-
-
-if __name__ == '__main__':
-  opentuner.init_logging()
-  args = argparser.parse_args()
-  GccFlagsTuner.main(args)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags_minimal.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags_minimal.py
deleted file mode 100755
index 0363b984a8c67064102e9025ce57d388c2585514..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/gccflags/gccflags_minimal.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-#
-# Autotune flags to g++ to optimize the performance of apps/raytracer.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import EnumParameter
-from opentuner import IntegerParameter
-from opentuner import MeasurementInterface
-from opentuner import Result
-
-GCC_FLAGS = [
-  'align-functions', 'align-jumps', 'align-labels',
-  'align-loops', 'asynchronous-unwind-tables',
-  'branch-count-reg', 'branch-probabilities',
-  # ... (176 total)
-]
-
-# (name, min, max)
-GCC_PARAMS = [
-  ('early-inlining-insns', 0, 1000),
-  ('gcse-cost-distance-ratio', 0, 100),
-  ('iv-max-considered-uses', 0, 1000),
-  # ... (145 total)
-]
-
-
-class GccFlagsTuner(MeasurementInterface):
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    manipulator.add_parameter(
-      IntegerParameter('opt_level', 0, 3))
-    for flag in GCC_FLAGS:
-      manipulator.add_parameter(
-        EnumParameter(flag,
-                      ['on', 'off', 'default']))
-    for param, min, max in GCC_PARAMS:
-      manipulator.add_parameter(
-        IntegerParameter(param, min, max))
-    return manipulator
-
-  def compile(self, cfg, id):
-    """
-    Compile a given configuration in parallel
-    """
-    gcc_cmd = 'g++ apps/raytracer.cpp -o ./tmp{0}.bin'.format(id)
-    gcc_cmd += ' -O{0}'.format(cfg['opt_level'])
-    for flag in GCC_FLAGS:
-      if cfg[flag] == 'on':
-        gcc_cmd += ' -f{0}'.format(flag)
-      elif cfg[flag] == 'off':
-        gcc_cmd += ' -fno-{0}'.format(flag)
-    for param, min, max in GCC_PARAMS:
-      gcc_cmd += ' --param {0}={1}'.format(
-        param, cfg[param])
-    return self.call_program(gcc_cmd)
-  
-  def run_precompiled(self, desired_result, input, limit, compile_result, id):
-    """
-    Run a compile_result from compile() sequentially and return performance
-    """
-    assert compile_result['returncode'] == 0
-
-    try:    
-        run_result = self.call_program('./tmp{0}.bin'.format(id))
-        assert run_result['returncode'] == 0
-    finally:
-        self.call_program('rm ./tmp{0}.bin'.format(id))
-
-    return Result(time=run_result['time'])
-
-  def compile_and_run(self, desired_result, input, limit):
-    """
-    Compile and run a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    compile_result = self.compile(cfg, 0)
-    return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
-if __name__ == '__main__':
-  argparser = opentuner.default_argparser()
-  GccFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/.gitignore
deleted file mode 100644
index ebdc2a395f4c8b509233d88992512c4cf4ae3364..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-dump-call-graph
-*.callgraph
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.cpp
deleted file mode 100644
index 6f1c97ffb85967223bf4e2ebc16d8ae0c2bcd02b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.cpp
+++ /dev/null
@@ -1,93 +0,0 @@
-#include "Halide.h"
-#include <stdio.h>
-
-using namespace Halide;
-
-int main(int argc, char **argv) {
-  // if (argc < 2) {
-  //     printf("Usage: bilateral_grid <s_sigma>\n");
-  //     // printf("Spatial sigma is a compile-time parameter, please provide it as an argument.\n"
-  //     //        "(llvm's ptx backend doesn't handle integer mods by non-consts yet)\n");
-  //     return 0;
-  // }
-
-    ImageParam input(Float(32), 2);
-    float r_sigma = 0.1;
-   // int s_sigma = atoi(argv[1]);
-    int s_sigma = 4;
-    Var x("x"), y("y"), z("z"), c("c");
-
-    // Add a boundary condition
-    Func clamped("clamped");
-    clamped(x, y) = input(clamp(x, 0, input.width()-1),
-                          clamp(y, 0, input.height()-1));
-
-    // Construct the bilateral grid
-    RDom r(0, s_sigma, 0, s_sigma);
-    Expr val = clamped(x * s_sigma + r.x - s_sigma/2, y * s_sigma + r.y - s_sigma/2);
-    val = clamp(val, 0.0f, 1.0f);
-    Expr zi = cast<int>(val * (1.0f/r_sigma) + 0.5f);
-    Func grid("grid"), histogram("histogram");
-    histogram(x, y, zi, c) += select(c == 0, val, 1.0f);
-
-    // Introduce a dummy function, so we can schedule the histogram within it
-    grid(x, y, z, c) = histogram(x, y, z, c);
-
-    // Blur the grid using a five-tap filter
-    Func blurx("blurx"), blury("blury"), blurz("blurz");
-    blurx(x, y, z, _) = grid(x-2, y, z, _) + grid(x-1, y, z, _)*4 + grid(x, y, z, _)*6 + grid(x+1, y, z, _)*4 + grid(x+2, y, z, _);
-    blury(x, y, z, _) = blurx(x, y-2, z, _) + blurx(x, y-1, z, _)*4 + blurx(x, y, z, _)*6 + blurx(x, y+1, z, _)*4 + blurx(x, y+2, z, _);
-    blurz(x, y, z, _) = blury(x, y, z-2, _) + blury(x, y, z-1, _)*4 + blury(x, y, z, _)*6 + blury(x, y, z+1, _)*4 + blury(x, y, z+2, _);
-
-    // Take trilinear samples to compute the output
-    val = clamp(clamped(x, y), 0.0f, 1.0f);
-    Expr zv = val * (1.0f/r_sigma);
-    zi = cast<int>(zv);
-    Expr zf = zv - zi;
-    Expr xf = cast<float>(x % s_sigma) / s_sigma;
-    Expr yf = cast<float>(y % s_sigma) / s_sigma;
-    Expr xi = x/s_sigma;
-    Expr yi = y/s_sigma;
-    Func interpolated("interpolated");
-    interpolated(x, y, _) =
-        lerp(lerp(lerp(blurz(xi, yi, zi, _), blurz(xi+1, yi, zi, _), xf),
-                  lerp(blurz(xi, yi+1, zi, _), blurz(xi+1, yi+1, zi, _), xf), yf),
-             lerp(lerp(blurz(xi, yi, zi+1, _), blurz(xi+1, yi, zi+1, _), xf),
-                  lerp(blurz(xi, yi+1, zi+1, _), blurz(xi+1, yi+1, zi+1, _), xf), yf), zf);
-
-    // Normalize
-    Func bilateral_grid("bilateral_grid");
-    bilateral_grid(x, y) = interpolated(x, y, 0)/interpolated(x, y, 1);
-
-    AUTOTUNE_HOOK(bilateral_grid);
-
-    char *target = getenv("HL_TARGET");
-    if (target && std::string(target) == "ptx") {
-
-        // GPU schedule
-        grid.compute_root().reorder(z, c, x, y).cuda_tile(x, y, 8, 8);
-
-        // Compute the histogram into shared memory before spilling it to global memory
-        histogram.store_at(grid, Var("blockidx")).compute_at(grid, Var("threadidx"));
-
-        blurx.compute_root().cuda_tile(x, y, z, 16, 16, 1);
-        blury.compute_root().cuda_tile(x, y, z, 16, 16, 1);
-        blurz.compute_root().cuda_tile(x, y, z, 8, 8, 4);
-        bilateral_grid.compute_root().cuda_tile(x, y, s_sigma, s_sigma);
-    } else {
-
-        // CPU schedule
-        grid.compute_root().reorder(c, z, x, y).parallel(y);
-        histogram.compute_at(grid, x).unroll(c);
-        blurx.compute_root().parallel(z).vectorize(x, 4);
-        blury.compute_root().parallel(z).vectorize(x, 4);
-        blurz.compute_root().parallel(z).vectorize(x, 4);
-        bilateral_grid.compute_root().parallel(y).vectorize(x, 4);
-    }
-
-    BASELINE_HOOK(bilateral_grid);
-
-   //bilateral_grid.compile_to_file("bilateral_grid", r_sigma, input);
-
-    return 0;
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.settings
deleted file mode 100644
index 7b829b779a9f7d05ef7b677ea307430728e29f16..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/bilateral_grid.settings
+++ /dev/null
@@ -1,10 +0,0 @@
-{"input_size": "2048, 2048",
- "functions": [
-  {"name": "clamped", "vars": ["x", "y"], "calls": []},
-  {"name": "histogram", "vars": ["x", "y", "c"], "calls": ["clamped"]},
-  {"name": "grid", "vars": ["x", "y", "z", "c"], "calls": ["histogram"]},
-  {"name": "blurx", "vars": ["x", "y", "z"], "calls": ["grid"]},
-  {"name": "blury", "vars": ["x", "y", "z"], "calls": ["blurx"]},
-  {"name": "blurz", "vars": ["x", "y", "z"], "calls": ["blury"]},
-  {"name": "interpolated", "vars": ["x", "y"], "calls": ["blurz", "clamped"]},
-  {"name": "bilateral_grid", "vars": ["x", "y"], "calls": ["interpolated"]}]}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.cpp
deleted file mode 100644
index 7a38dd45fd8bf48ecc1d7489efe991dd320c0b63..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-#include <Halide.h>
-using namespace Halide;
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-int main(int argc, char **argv) {
-
-    ImageParam in_img(UInt(16), 2);
-    Func blur_x("blur_x"), blur_y("blur_y");
-    Var x("x"), y("y"), xi("xi"), yi("yi");
-
-    Func input;
-    input(x,y) = in_img(clamp(x, 1, in_img.width()-1),
-                        clamp(y, 1, in_img.height())-1);
-
-    // The algorithm
-    blur_x(x, y) = (input(x, y) + input(x+1, y) + input(x+2, y))/3;
-    blur_y(x, y) = (blur_x(x, y) + blur_x(x, y+1) + blur_x(x, y+2))/3;
-
-    AUTOTUNE_HOOK(blur_y);
-
-    // How to schedule it
-    blur_y.split(y, y, yi, 8).parallel(y).vectorize(x, 8);
-    blur_x.store_at(blur_y, y).compute_at(blur_y, yi).vectorize(x, 8);  
-
-    BASELINE_HOOK(blur_y);
-
-    blur_y.compile_to_file("halide_blur", in_img); 
-
-    return 0;
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.settings
deleted file mode 100644
index af0deeac34966616ff0f0af7a008c0c6f74ef2cb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/halide_blur.settings
+++ /dev/null
@@ -1,4 +0,0 @@
-{"input_size": "4096, 4096",
- "functions": [
-               {"name": "blur_x", "vars": ["x", "y"], "calls": []},
-               {"name": "blur_y", "vars": ["x", "y"], "calls": ["blur_x"]}]}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.cpp
deleted file mode 100644
index 74d141721db7e22667505f35d1c894d081ae064d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
-    struct timeval tv;
-    gettimeofday(&tv, NULL);
-    static bool first_call = true;
-    static time_t first_sec = 0;
-    if (first_call) {
-        first_call = false;
-        first_sec = tv.tv_sec;
-    }
-    assert(tv.tv_sec >= first_sec);
-    return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
-    ImageParam input(Float(32), 3, "input");
-
-    const unsigned int levels = 3;
-
-    Func downsampled[levels];
-    Func downx[levels];
-    Func interpolated[levels];
-    Func upsampled[levels];
-    Func upsampledx[levels];
-    Var x("x"), y("y"), c("c");
-
-    downsampled[0] = Func("downsampled");
-    downx[0] = Func("downx");
-    interpolated[0] = Func("interpolated");
-    upsampled[0] = Func("upsampled");
-    upsampledx[0] = Func("upsampledx");
-
-    Func clamped("clamped");
-    clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
-    // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
-    // rewrite it in a way that doesn't trigger the bug. The rewritten
-    // form assumes the input alpha is zero or one.
-    // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
-    downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
-    for (unsigned int l = 1; l < levels; ++l) {
-        downx[l] = Func("downx");
-        downsampled[l] = Func("downsampled");
-        downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
-                             2.0f * downsampled[l-1](x*2, y, c) +
-                             downsampled[l-1](x*2+1, y, c)) * 0.25f;
-        downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
-                                   2.0f * downx[l](x, y*2, c) +
-                                   downx[l](x, y*2+1, c)) * 0.25f;
-    }
-    interpolated[levels-1] = Func("interpolated");
-    interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
-    for (unsigned int l = levels-2; l < levels; --l) {
-        upsampledx[l] = Func("upsampledx");
-        upsampled[l] = Func("upsampled");
-        interpolated[l] = Func("interpolated");
-        upsampledx[l](x, y, c) = select((x % 2) == 0,
-                                        interpolated[l+1](x/2, y, c),
-                                        0.5f * (interpolated[l+1](x/2, y, c) +
-                                                interpolated[l+1](x/2+1, y, c)));
-        upsampled[l](x, y, c) = select((y % 2) == 0,
-                                       upsampledx[l](x, y/2, c),
-                                       0.5f * (upsampledx[l](x, y/2, c) +
-                                               upsampledx[l](x, y/2+1, c)));
-        interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
-    }
-
-    Func normalize("normalize");
-    normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
-    Func final("final");
-    final(x, y, c) = normalize(x, y, c);
-
-    AUTOTUNE_HOOK(final);
-
-    int sched;
-    char *target = getenv("HL_TARGET");
-    if (target && std::string(target) == "ptx") {
-        sched = 4;
-    } else {
-        sched = 2;
-    }
-
-    switch (sched) {
-    case 0:
-    {
-        //std::cout << "Flat schedule." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root();
-            interpolated[l].compute_root();
-        }
-        final.compute_root();
-        break;
-    }
-    case 1:
-    {
-        //std::cout << "Flat schedule with vectorization." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root().vectorize(x,4);
-            interpolated[l].compute_root().vectorize(x,4);
-        }
-        final.compute_root();
-        break;
-    }
-    case 2:
-    {
-        Var xi, yi;
-        //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
-        clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-        for (unsigned int l = 1; l < levels-1; ++l) {
-            if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].unroll(x, 2).unroll(y, 2);
-        }
-        final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
-        final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
-        break;
-    }
-    case 3:
-    {
-        //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            if (l + 4 < levels) {
-                Var yo,yi;
-                downsampled[l].compute_root().vectorize(x,4);
-                interpolated[l].compute_root().vectorize(x,4);
-            } else {
-                downsampled[l].compute_root();
-                interpolated[l].compute_root();
-            }
-        }
-        final.compute_root();
-        break;
-    }
-    case 4:
-    {
-        //std::cout << "GPU schedule." << std::endl;
-
-        // Some gpus don't have enough memory to process the entire
-        // image, so we process the image in tiles.
-        Var yo, yi, xo, xi;
-        final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
-        final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
-        normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
-        // Start from level 1 to save memory - level zero will be computed on demand
-        for (unsigned int l = 1; l < levels; ++l) {
-            int tile_size = 32 >> l;
-            if (tile_size < 1) tile_size = 1;
-            if (tile_size > 16) tile_size = 16;
-            downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
-            interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
-        }
-
-        break;
-    }
-    default:
-        assert(0 && "No schedule with this number.");
-    }
-
-    BASELINE_HOOK(final);
-
-#if 0
-    // JIT compile the pipeline eagerly, so we don't interfere with timing
-    final.compile_jit();
-
-    // Image<float> in_png = load<float>(argv[1]);
-    Image<float> out(2048, 2048, 3);
-    // assert(in_png.channels() == 4);
-    // input.set(in_png);
-    final.infer_input_bounds(out);
-
-    std::cout << "Running... " << std::endl;
-    double min = std::numeric_limits<double>::infinity();
-    const unsigned int iters = 20;
-
-    for (unsigned int x = 0; x < iters; ++x) {
-        double before = now();
-        final.realize(out);
-        double after = now();
-        double amt = after - before;
-
-        std::cout << "   " << amt * 1000 << std::endl;
-        if (amt < min) min = amt;
-
-    }
-    std::cout << " took " << min * 1000 << " msec." << std::endl;
-
-    // vector<Argument> args;
-    // args.push_back(input);
-    // final.compile_to_assembly("test.s", args);
-    // save(out, argv[2]);
-#endif
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.settings
deleted file mode 100644
index cffd184ed6bc406bb88fb52d1e67ac2349df9532..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simple.settings
+++ /dev/null
@@ -1,185 +0,0 @@
-{
-  "functions": [
-    {
-      "calls": [], 
-      "name": "clamped", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "clamped"
-      ], 
-      "name": "downsampled", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$2"
-      ], 
-      "name": "downsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$3"
-      ], 
-      "name": "downsampled$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled"
-      ], 
-      "name": "downx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$2"
-      ], 
-      "name": "downx$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$3"
-      ], 
-      "name": "interpolated$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$2", 
-        "upsampled$2"
-      ], 
-      "name": "interpolated$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled", 
-        "upsampled$3"
-      ], 
-      "name": "interpolated$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$4"
-      ], 
-      "name": "normalize", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$2"
-      ], 
-      "name": "upsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$3"
-      ], 
-      "name": "upsampled$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$2"
-      ], 
-      "name": "upsampledx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$3"
-      ], 
-      "name": "upsampledx$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "normalize"
-      ], 
-      "name": "final", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }
-  ], 
-  "input_size": "2048, 2048, 3"
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.cpp
deleted file mode 100644
index cf570558360236d16b21379901e40b3ee8481fd4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
-    struct timeval tv;
-    gettimeofday(&tv, NULL);
-    static bool first_call = true;
-    static time_t first_sec = 0;
-    if (first_call) {
-        first_call = false;
-        first_sec = tv.tv_sec;
-    }
-    assert(tv.tv_sec >= first_sec);
-    return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
-    ImageParam input(Float(32), 3, "input");
-
-    const unsigned int levels = 2;
-
-    Func downsampled[levels];
-    Func downx[levels];
-    Func interpolated[levels];
-    Func upsampled[levels];
-    Func upsampledx[levels];
-    Var x("x"), y("y"), c("c");
-
-    downsampled[0] = Func("downsampled");
-    downx[0] = Func("downx");
-    interpolated[0] = Func("interpolated");
-    upsampled[0] = Func("upsampled");
-    upsampledx[0] = Func("upsampledx");
-
-    Func clamped("clamped");
-    clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
-    // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
-    // rewrite it in a way that doesn't trigger the bug. The rewritten
-    // form assumes the input alpha is zero or one.
-    // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
-    downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
-    for (unsigned int l = 1; l < levels; ++l) {
-        downx[l] = Func("downx");
-        downsampled[l] = Func("downsampled");
-        downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
-                             2.0f * downsampled[l-1](x*2, y, c) +
-                             downsampled[l-1](x*2+1, y, c)) * 0.25f;
-        downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
-                                   2.0f * downx[l](x, y*2, c) +
-                                   downx[l](x, y*2+1, c)) * 0.25f;
-    }
-    interpolated[levels-1] = Func("interpolated");
-    interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
-    for (unsigned int l = levels-2; l < levels; --l) {
-        upsampledx[l] = Func("upsampledx");
-        upsampled[l] = Func("upsampled");
-        interpolated[l] = Func("interpolated");
-        upsampledx[l](x, y, c) = select((x % 2) == 0,
-                                        interpolated[l+1](x/2, y, c),
-                                        0.5f * (interpolated[l+1](x/2, y, c) +
-                                                interpolated[l+1](x/2+1, y, c)));
-        upsampled[l](x, y, c) = select((y % 2) == 0,
-                                       upsampledx[l](x, y/2, c),
-                                       0.5f * (upsampledx[l](x, y/2, c) +
-                                               upsampledx[l](x, y/2+1, c)));
-        interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
-    }
-
-    Func normalize("normalize");
-    normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
-    Func final("final");
-    final(x, y, c) = normalize(x, y, c);
-
-    AUTOTUNE_HOOK(final);
-
-    int sched;
-    char *target = getenv("HL_TARGET");
-    if (target && std::string(target) == "ptx") {
-        sched = 4;
-    } else {
-        sched = 2;
-    }
-
-    switch (sched) {
-    case 0:
-    {
-        //std::cout << "Flat schedule." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root();
-            interpolated[l].compute_root();
-        }
-        final.compute_root();
-        break;
-    }
-    case 1:
-    {
-        //std::cout << "Flat schedule with vectorization." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root().vectorize(x,4);
-            interpolated[l].compute_root().vectorize(x,4);
-        }
-        final.compute_root();
-        break;
-    }
-    case 2:
-    {
-        Var xi, yi;
-        //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
-        clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-        for (unsigned int l = 1; l < levels-1; ++l) {
-            if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].unroll(x, 2).unroll(y, 2);
-        }
-        final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
-        final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
-        break;
-    }
-    case 3:
-    {
-        //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            if (l + 4 < levels) {
-                Var yo,yi;
-                downsampled[l].compute_root().vectorize(x,4);
-                interpolated[l].compute_root().vectorize(x,4);
-            } else {
-                downsampled[l].compute_root();
-                interpolated[l].compute_root();
-            }
-        }
-        final.compute_root();
-        break;
-    }
-    case 4:
-    {
-        //std::cout << "GPU schedule." << std::endl;
-
-        // Some gpus don't have enough memory to process the entire
-        // image, so we process the image in tiles.
-        Var yo, yi, xo, xi;
-        final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
-        final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
-        normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
-        // Start from level 1 to save memory - level zero will be computed on demand
-        for (unsigned int l = 1; l < levels; ++l) {
-            int tile_size = 32 >> l;
-            if (tile_size < 1) tile_size = 1;
-            if (tile_size > 16) tile_size = 16;
-            downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
-            interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
-        }
-
-        break;
-    }
-    default:
-        assert(0 && "No schedule with this number.");
-    }
-
-    BASELINE_HOOK(final);
-
-#if 0
-    // JIT compile the pipeline eagerly, so we don't interfere with timing
-    final.compile_jit();
-
-    // Image<float> in_png = load<float>(argv[1]);
-    Image<float> out(2048, 2048, 3);
-    // assert(in_png.channels() == 4);
-    // input.set(in_png);
-    final.infer_input_bounds(out);
-
-    std::cout << "Running... " << std::endl;
-    double min = std::numeric_limits<double>::infinity();
-    const unsigned int iters = 20;
-
-    for (unsigned int x = 0; x < iters; ++x) {
-        double before = now();
-        final.realize(out);
-        double after = now();
-        double amt = after - before;
-
-        std::cout << "   " << amt * 1000 << std::endl;
-        if (amt < min) min = amt;
-
-    }
-    std::cout << " took " << min * 1000 << " msec." << std::endl;
-
-    // vector<Argument> args;
-    // args.push_back(input);
-    // final.compile_to_assembly("test.s", args);
-    // save(out, argv[2]);
-#endif
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.settings
deleted file mode 100644
index 5f22d20f5f9cf355bfd07ca408264f73031a14d0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate-simplest.settings
+++ /dev/null
@@ -1,124 +0,0 @@
-{
-  "functions": [
-    {
-      "calls": [], 
-      "name": "clamped", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "clamped"
-      ], 
-      "name": "downsampled", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$2"
-      ], 
-      "name": "downsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled"
-      ], 
-      "name": "downx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$2"
-      ], 
-      "name": "interpolated$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled", 
-        "upsampled$2"
-      ], 
-      "name": "interpolated$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$3"
-      ], 
-      "name": "normalize", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$2"
-      ], 
-      "name": "upsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$2"
-      ], 
-      "name": "upsampledx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "normalize"
-      ], 
-      "name": "final", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }
-  ], 
-  "input_size": "2048, 2048, 3"
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.cpp
deleted file mode 100644
index 1ca4ae5bd352fa524ed6aeafbe5795c913e1f6b8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
-    struct timeval tv;
-    gettimeofday(&tv, NULL);
-    static bool first_call = true;
-    static time_t first_sec = 0;
-    if (first_call) {
-        first_call = false;
-        first_sec = tv.tv_sec;
-    }
-    assert(tv.tv_sec >= first_sec);
-    return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
-    ImageParam input(Float(32), 3, "input");
-
-    const unsigned int levels = 10;
-
-    Func downsampled[levels];
-    Func downx[levels];
-    Func interpolated[levels];
-    Func upsampled[levels];
-    Func upsampledx[levels];
-    Var x("x"), y("y"), c("c");
-
-    downsampled[0] = Func("downsampled");
-    downx[0] = Func("downx");
-    interpolated[0] = Func("interpolated");
-    upsampled[0] = Func("upsampled");
-    upsampledx[0] = Func("upsampledx");
-
-    Func clamped("clamped");
-    clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
-    // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
-    // rewrite it in a way that doesn't trigger the bug. The rewritten
-    // form assumes the input alpha is zero or one.
-    // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
-    downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
-    for (unsigned int l = 1; l < levels; ++l) {
-        downx[l] = Func("downx");
-        downsampled[l] = Func("downsampled");
-        downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
-                             2.0f * downsampled[l-1](x*2, y, c) +
-                             downsampled[l-1](x*2+1, y, c)) * 0.25f;
-        downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
-                                   2.0f * downx[l](x, y*2, c) +
-                                   downx[l](x, y*2+1, c)) * 0.25f;
-    }
-    interpolated[levels-1] = Func("interpolated");
-    interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
-    for (unsigned int l = levels-2; l < levels; --l) {
-        upsampledx[l] = Func("upsampledx");
-        upsampled[l] = Func("upsampled");
-        interpolated[l] = Func("interpolated");
-        upsampledx[l](x, y, c) = select((x % 2) == 0,
-                                        interpolated[l+1](x/2, y, c),
-                                        0.5f * (interpolated[l+1](x/2, y, c) +
-                                                interpolated[l+1](x/2+1, y, c)));
-        upsampled[l](x, y, c) = select((y % 2) == 0,
-                                       upsampledx[l](x, y/2, c),
-                                       0.5f * (upsampledx[l](x, y/2, c) +
-                                               upsampledx[l](x, y/2+1, c)));
-        interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
-    }
-
-    Func normalize("normalize");
-    normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
-    Func final("final");
-    final(x, y, c) = normalize(x, y, c);
-
-    AUTOTUNE_HOOK(final);
-
-    int sched;
-    char *target = getenv("HL_TARGET");
-    if (target && std::string(target) == "ptx") {
-        sched = 4;
-    } else {
-        sched = 2;
-    }
-
-    switch (sched) {
-    case 0:
-    {
-        //std::cout << "Flat schedule." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root();
-            interpolated[l].compute_root();
-        }
-        final.compute_root();
-        break;
-    }
-    case 1:
-    {
-        //std::cout << "Flat schedule with vectorization." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            downsampled[l].compute_root().vectorize(x,4);
-            interpolated[l].compute_root().vectorize(x,4);
-        }
-        final.compute_root();
-        break;
-    }
-    case 2:
-    {
-        Var xi, yi;
-        //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
-        clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-        for (unsigned int l = 1; l < levels-1; ++l) {
-            if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
-            interpolated[l].unroll(x, 2).unroll(y, 2);
-        }
-        final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
-        final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
-        break;
-    }
-    case 3:
-    {
-        //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
-        for (unsigned int l = 0; l < levels; ++l) {
-            if (l + 4 < levels) {
-                Var yo,yi;
-                downsampled[l].compute_root().vectorize(x,4);
-                interpolated[l].compute_root().vectorize(x,4);
-            } else {
-                downsampled[l].compute_root();
-                interpolated[l].compute_root();
-            }
-        }
-        final.compute_root();
-        break;
-    }
-    case 4:
-    {
-        //std::cout << "GPU schedule." << std::endl;
-
-        // Some gpus don't have enough memory to process the entire
-        // image, so we process the image in tiles.
-        Var yo, yi, xo, xi;
-        final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
-        final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
-        normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
-        // Start from level 1 to save memory - level zero will be computed on demand
-        for (unsigned int l = 1; l < levels; ++l) {
-            int tile_size = 32 >> l;
-            if (tile_size < 1) tile_size = 1;
-            if (tile_size > 16) tile_size = 16;
-            downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
-            interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
-        }
-
-        break;
-    }
-    default:
-        assert(0 && "No schedule with this number.");
-    }
-
-    BASELINE_HOOK(final);
-
-#if 0
-    // JIT compile the pipeline eagerly, so we don't interfere with timing
-    final.compile_jit();
-
-    // Image<float> in_png = load<float>(argv[1]);
-    Image<float> out(2048, 2048, 3);
-    // assert(in_png.channels() == 4);
-    // input.set(in_png);
-    final.infer_input_bounds(out);
-
-    std::cout << "Running... " << std::endl;
-    double min = std::numeric_limits<double>::infinity();
-    const unsigned int iters = 20;
-
-    for (unsigned int x = 0; x < iters; ++x) {
-        double before = now();
-        final.realize(out);
-        double after = now();
-        double amt = after - before;
-
-        std::cout << "   " << amt * 1000 << std::endl;
-        if (amt < min) min = amt;
-
-    }
-    std::cout << " took " << min * 1000 << " msec." << std::endl;
-
-    // vector<Argument> args;
-    // args.push_back(input);
-    // final.compile_to_assembly("test.s", args);
-    // save(out, argv[2]);
-#endif
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.settings
deleted file mode 100644
index 3a51d8062674581182fb204ddb943f85cd3b4de4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/interpolate.settings
+++ /dev/null
@@ -1,612 +0,0 @@
-{
-  "functions": [
-    {
-      "calls": [], 
-      "name": "clamped", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "clamped"
-      ], 
-      "name": "downsampled", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$10"
-      ], 
-      "name": "downsampled$10", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$2"
-      ], 
-      "name": "downsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$3"
-      ], 
-      "name": "downsampled$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$4"
-      ], 
-      "name": "downsampled$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$5"
-      ], 
-      "name": "downsampled$5", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$6"
-      ], 
-      "name": "downsampled$6", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$7"
-      ], 
-      "name": "downsampled$7", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$8"
-      ], 
-      "name": "downsampled$8", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downx$9"
-      ], 
-      "name": "downsampled$9", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$9"
-      ], 
-      "name": "downx$10", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled"
-      ], 
-      "name": "downx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$2"
-      ], 
-      "name": "downx$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$3"
-      ], 
-      "name": "downx$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$4"
-      ], 
-      "name": "downx$5", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$5"
-      ], 
-      "name": "downx$6", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$6"
-      ], 
-      "name": "downx$7", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$7"
-      ], 
-      "name": "downx$8", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$8"
-      ], 
-      "name": "downx$9", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$2", 
-        "upsampled$9"
-      ], 
-      "name": "interpolated$10", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled", 
-        "upsampled$10"
-      ], 
-      "name": "interpolated$11", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$10"
-      ], 
-      "name": "interpolated$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$9", 
-        "upsampled$2"
-      ], 
-      "name": "interpolated$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$8", 
-        "upsampled$3"
-      ], 
-      "name": "interpolated$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$7", 
-        "upsampled$4"
-      ], 
-      "name": "interpolated$5", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$6", 
-        "upsampled$5"
-      ], 
-      "name": "interpolated$6", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$5", 
-        "upsampled$6"
-      ], 
-      "name": "interpolated$7", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$4", 
-        "upsampled$7"
-      ], 
-      "name": "interpolated$8", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "downsampled$3", 
-        "upsampled$8"
-      ], 
-      "name": "interpolated$9", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$11"
-      ], 
-      "name": "normalize", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$10"
-      ], 
-      "name": "upsampled$10", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$2"
-      ], 
-      "name": "upsampled$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$3"
-      ], 
-      "name": "upsampled$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$4"
-      ], 
-      "name": "upsampled$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$5"
-      ], 
-      "name": "upsampled$5", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$6"
-      ], 
-      "name": "upsampled$6", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$7"
-      ], 
-      "name": "upsampled$7", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$8"
-      ], 
-      "name": "upsampled$8", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "upsampledx$9"
-      ], 
-      "name": "upsampled$9", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$10"
-      ], 
-      "name": "upsampledx$10", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$2"
-      ], 
-      "name": "upsampledx$2", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$3"
-      ], 
-      "name": "upsampledx$3", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$4"
-      ], 
-      "name": "upsampledx$4", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$5"
-      ], 
-      "name": "upsampledx$5", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$6"
-      ], 
-      "name": "upsampledx$6", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$7"
-      ], 
-      "name": "upsampledx$7", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$8"
-      ], 
-      "name": "upsampledx$8", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "interpolated$9"
-      ], 
-      "name": "upsampledx$9", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }, 
-    {
-      "calls": [
-        "normalize"
-      ], 
-      "name": "final", 
-      "update_calls": [], 
-      "vars": [
-        "x", 
-        "y", 
-        "c"
-      ]
-    }
-  ], 
-  "input_size": "1024, 1024, 3"
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.cpp
deleted file mode 100644
index e2cb008790ac0161a2365388744cb9b482a6d7b8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.cpp
+++ /dev/null
@@ -1,93 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-Var x("x"), y("y"), c("c");
-
-Func haar_x(Func in) {
-    Func out;
-    out(x, y, c) = select(c == 0, 
-                          (in(2*x, y) + in(2*x+1, y)),
-                          (in(2*x, y) - in(2*x+1, y)))/2;
-    out.unroll(c, 2);
-    return out;
-}
-
-Func inverse_haar_x(Func in) {
-    Func out;
-    out(x, y) = select(x%2 == 0, 
-                       in(x/2, y, 0) + in(x/2, y, 1),
-                       in(x/2, y, 0) - in(x/2, y, 1));
-    out.unroll(x, 2);
-    return out;
-}
-
-
-const float D0 = 0.4829629131445341f;
-const float D1 = 0.83651630373780772f;
-const float D2 = 0.22414386804201339f;
-const float D3 = -0.12940952255126034f;
-
-/*
-const float D0 = 0.34150635f;
-const float D1 = 0.59150635f;
-const float D2 = 0.15849365f;
-const float D3 = -0.1830127f;
-*/
-
-Func daubechies_x(Func in) {
-    Func out;
-    out(x, y, c) = select(c == 0, 
-                          D0*in(2*x-1, y) + D1*in(2*x, y) + D2*in(2*x+1, y) + D3*in(2*x+2, y),
-                          D3*in(2*x-1, y) - D2*in(2*x, y) + D1*in(2*x+1, y) - D0*in(2*x+2, y));
-   //out.unroll(c, 2);
-    return out;
-}
-
-Func inverse_daubechies_x(Func in) {
-    Func out("inv_daub_x");
-    out(x, y) = select(x%2 == 0,
-                       D2*in(x/2, y, 0) + D1*in(x/2, y, 1) + D0*in(x/2+1, y, 0) + D3*in(x/2+1, y, 1),
-                       D3*in(x/2, y, 0) - D0*in(x/2, y, 1) + D1*in(x/2+1, y, 0) - D2*in(x/2+1, y, 1));
-   //out.unroll(x, 2);
-    return out;
-}
-
-int main(int argc, char **argv) {
-
-    ImageParam image(Float(32), 2);
-    ImageParam wavelet(Float(32), 3);
-
-    // Add a boundary condition for daubechies
-    Func clamped;
-    clamped(x, y) = image(clamp(x, 0, image.width()-1),
-                          clamp(y, 0, image.height()-1));
-    Func wavelet_clamped("wavelet_clamped");
-    wavelet_clamped(x, y, c) = wavelet(clamp(x, 0, wavelet.width()-1),
-                                       clamp(y, 0, wavelet.height()-1), c);
-
-
-  // Func inv_haar_x = inverse_haar_x(wavelet_clamped);
-  // inv_haar_x.compile_to_file("inverse_haar_x", wavelet);
-
-  // Func for_haar_x = haar_x(clamped);
-  // for_haar_x.compile_to_file("haar_x", image);
-
-    Func inv_daub_x = inverse_daubechies_x(wavelet_clamped);
-    //inv_daub_x.compile_to_file("inverse_daubechies_x", wavelet);
-
-    AUTOTUNE_HOOK(inv_daub_x);
-    inv_daub_x.unroll(x, 2).vectorize(x, 8).parallel(y);
-    BASELINE_HOOK(inv_daub_x);
-
-  // Func for_daub_x = daubechies_x(clamped);
-    //for_daub_x.compile_to_file("daubechies_x", image);
-
-    return 0;
-}
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.settings b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.settings
deleted file mode 100644
index 6fbb5c4006dd77fee03f8635de2b22079eb0a908..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/apps/wavelet.settings
+++ /dev/null
@@ -1,4 +0,0 @@
-{"input_size": "2048, 2048",
- "functions": [
-    {"name": "wavelet_clamped", "vars": ["x", "y", "c"], "calls": []},
-    {"name": "inv_daub_x", "vars": ["x", "y"], "calls": ["wavelet_clamped"]}]}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/halidetuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/halidetuner.py
deleted file mode 100755
index 08e6732575557e41736fb71a321ee7190e181e7e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/halidetuner.py
+++ /dev/null
@@ -1,682 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-#
-# Example of synthesizing Halide schedules using OpenTuner.  This program
-# expects a compiled version of Halide to exist at ~/Halide or at the location
-# specified by --halide-dir.
-#
-# Halide programs must be modified by:
-#  1) Inserting AUTOTUNE_HOOK(Func) directly after the algorithm definition
-#     in main()
-#  2) Creating a settings file that describes the functions and variables
-#     (see apps/halide_blur.settings for an example)
-#
-# Halide can be found here: https://github.com/halide/Halide
-#
-
-import adddeps  # fix sys.path
-
-import argparse
-import collections
-import hashlib
-import json
-import logging
-import math
-import os
-import re
-import subprocess
-import tempfile
-import textwrap
-from cStringIO import StringIO
-from fn import _
-from pprint import pprint
-
-import opentuner
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import PowerOfTwoParameter
-from opentuner.search.manipulator import PermutationParameter
-from opentuner.search.manipulator import BooleanParameter
-from opentuner.search.manipulator import ScheduleParameter
-
-
-COMPILE_CMD = (
-  '{args.cxx} "{cpp}" -o "{bin}" -I "{args.halide_dir}/include" '
-  '"{args.halide_dir}/bin/$BUILD_PREFIX/libHalide.a" -ldl -lcurses -lpthread {args.cxxflags} '
-  '-DAUTOTUNE_N="{args.input_size}" -DAUTOTUNE_TRIALS={args.trials} '
-  '-DAUTOTUNE_LIMIT={limit} -fno-rtti')
-
-log = logging.getLogger('halide')
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('source', help='Halide source file annotated with '
-                                   'AUTOTUNE_HOOK')
-parser.add_argument('--halide-dir', default=os.path.expanduser('~/Halide'),
-                    help='Installation directory for Halide')
-parser.add_argument('--input-size',
-                    help='Input size to test with')
-parser.add_argument('--trials', default=3, type=int,
-                    help='Number of times to test each schedule')
-parser.add_argument('--nesting', default=2, type=int,
-                    help='Maximum depth for generated loops')
-parser.add_argument('--max-split-factor', default=8, type=int,
-                    help='The largest value a single split() can add')
-parser.add_argument('--compile-command', default=COMPILE_CMD,
-                    help='How to compile generated C++ code')
-parser.add_argument('--cxx', default='c++',
-                    help='C++ compiler to use (e.g., g++ or clang++)')
-parser.add_argument('--cxxflags', default='',
-                    help='Extra flags to the C++ compiler')
-parser.add_argument('--tmp-dir',
-                    default=('/run/shm' if os.access('/run/shm', os.W_OK)
-                             else '/tmp'),
-                    help='Where to store generated tests')
-parser.add_argument('--settings-file',
-                    help='Override location of json encoded settings')
-parser.add_argument('--debug-error',
-                    help='Stop on errors matching a given string')
-parser.add_argument('--limit', type=float, default=30,
-                    help='Kill compile + runs taking too long (seconds)')
-parser.add_argument('--memory-limit', type=int, default=1024 ** 3,
-                    help='Set memory ulimit on unix based systems')
-parser.add_argument('--enable-unroll', action='store_true',
-                    help='Enable .unroll(...) generation')
-parser.add_argument('--enable-store-at', action='store_true',
-                    help='Never generate .store_at(...)')
-parser.add_argument('--gated-store-reorder', action='store_true',
-                    help='Only reorder storage if a special parameter is given')
-group = parser.add_mutually_exclusive_group()
-group.add_argument('--random-test', action='store_true',
-                   help='Generate a random configuration and run it')
-group.add_argument('--random-source', action='store_true',
-                   help='Generate a random configuration and print source ')
-group.add_argument('--make-settings-file', action='store_true',
-                   help='Create a skeleton settings file from call graph')
-
-
-# class HalideRandomConfig(opentuner.search.technique.SearchTechnique):
-#   def desired_configuration(self):
-#     '''
-#     inject random configs with no compute_at() calls to kickstart the search process
-#     '''
-#     cfg = self.manipulator.random()
-#     for k in cfg.keys():
-#       if re.match('.*_compute_level', k):
-#         cfg[k] = LoopLevel.INLINE
-#     return cfg
-#
-# technique.register(bandittechniques.AUCBanditMetaTechnique([
-#         HalideRandomConfig(),
-#         differentialevolution.DifferentialEvolutionAlt(),
-#         evolutionarytechniques.UniformGreedyMutation(),
-#         evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
-#       ], name = "HalideMetaTechnique"))
-
-
-class HalideTuner(opentuner.measurement.MeasurementInterface):
-  def __init__(self, args):
-    # args.technique = ['HalideMetaTechnique']
-    super(HalideTuner, self).__init__(args, program_name=args.source)
-    timing_prefix = open(os.path.join(os.path.dirname(__file__),
-                                      'timing_prefix.h')).read()
-    self.template = timing_prefix + open(args.source).read()
-    self.min_collection_cost = float('inf')
-    if not args.settings_file:
-      args.settings_file = os.path.splitext(args.source)[0] + '.settings'
-    if not args.make_settings_file:
-      with open(args.settings_file) as fd:
-        self.settings = json.load(fd)
-      self.post_dominators = post_dominators(self.settings)
-      if not args.input_size:
-        args.input_size = self.settings['input_size']
-    else:
-      self.settings = None
-      self.post_dominators = None
-      args.input_size = '1, 1'
-    # set "program_version" based on hash of halidetuner.py, program source
-    h = hashlib.md5()
-    #with open(__file__) as src:
-    #  h.update(src.read())
-    with open(args.source) as src:
-      h.update(src.read())
-    self._version = h.hexdigest()
-
-  def compute_order_parameter(self, func):
-    name = func['name']
-    schedule_vars = []
-    schedule_deps = dict()
-    for var in func['vars']:
-      schedule_vars.append((var, 0))
-      for i in xrange(1, self.args.nesting):
-        schedule_vars.append((var, i))
-        schedule_deps[(var, i - 1)] = [(var, i)]
-    return ScheduleParameter('{0}_compute_order'.format(name), schedule_vars,
-                             schedule_deps)
-
-  def manipulator(self):
-    """
-    The definition of the manipulator is meant to mimic the Halide::Schedule
-    data structure and defines the configuration space to search
-    """
-    manipulator = HalideConfigurationManipulator(self)
-    manipulator.add_parameter(HalideComputeAtScheduleParameter(
-      'schedule', self.args, self.settings['functions'],
-      self.post_dominators))
-    for func in self.settings['functions']:
-      name = func['name']
-      manipulator.add_parameter(PermutationParameter(
-        '{0}_store_order'.format(name), func['vars']))
-      manipulator.add_parameter(
-        BooleanParameter('{0}_store_order_enabled'.format(name)))
-      manipulator.add_parameter(self.compute_order_parameter(func))
-      for var in func['vars']:
-        manipulator.add_parameter(PowerOfTwoParameter(
-          '{0}_vectorize'.format(name), 1, self.args.max_split_factor))
-        manipulator.add_parameter(PowerOfTwoParameter(
-          '{0}_unroll'.format(name), 1, self.args.max_split_factor))
-        manipulator.add_parameter(BooleanParameter(
-          '{0}_parallel'.format(name)))
-        for nesting in xrange(1, self.args.nesting):
-          manipulator.add_parameter(PowerOfTwoParameter(
-            '{0}_splitfactor_{1}_{2}'.format(name, nesting, var),
-            1, self.args.max_split_factor))
-
-    return manipulator
-
-  def cfg_to_schedule(self, cfg):
-    """
-    Produce a Halide schedule from a configuration dictionary
-    """
-    o = StringIO()
-    cnt = 0
-    temp_vars = list()
-    schedule = ComputeAtStoreAtParser(cfg['schedule'], self.post_dominators)
-    compute_at = schedule.compute_at
-    store_at = schedule.store_at
-
-    # build list of all used variable names
-    var_names = dict()
-    var_name_order = dict()
-    for func in self.settings['functions']:
-      name = func['name']
-      compute_order = cfg['{0}_compute_order'.format(name)]
-      for var in func['vars']:
-        var_names[(name, var, 0)] = var
-        for nesting in xrange(1, self.args.nesting):
-          split_factor = cfg.get('{0}_splitfactor_{1}_{2}'.format(
-            name, nesting, var), 0)
-          if split_factor > 1 and (name, var, nesting - 1) in var_names:
-            var_names[(name, var, nesting)] = '_{var}{cnt}'.format(
-              func=name, var=var, nesting=nesting, cnt=cnt)
-            temp_vars.append(var_names[(name, var, nesting)])
-          cnt += 1
-      var_name_order[name] = [var_names[(name, v, n)] for v, n in compute_order
-                              if (name, v, n) in var_names]
-
-    # set a schedule for each function
-    for func in self.settings['functions']:
-      name = func['name']
-      inner_var_name = var_name_order[name][-1] # innermost variable in the reordered list for this func
-      vectorize = cfg['{0}_vectorize'.format(name)]
-      if self.args.enable_unroll:
-        unroll = cfg['{0}_unroll'.format(name)]
-      else:
-        unroll = 1
-
-      print >> o, 'Halide::Func(funcs["%s"])' % name
-
-      for var in func['vars']:
-        # handle all splits
-        for nesting in xrange(1, self.args.nesting):
-          split_factor = cfg.get('{0}_splitfactor_{1}_{2}'.format(
-            name, nesting, var), 0)
-          if split_factor <= 1:
-            break
-
-          for nesting2 in xrange(nesting + 1, self.args.nesting):
-            split_factor2 = cfg.get('{0}_splitfactor_{1}_{2}'.format(
-              name, nesting2, var), 0)
-            if split_factor2 <= 1:
-              break
-            split_factor *= split_factor2
-          var_name = var_names[(name, var, nesting)]
-          last_var_name = var_names[(name, var, nesting - 1)]
-          
-          # apply unroll, vectorize factors to all surrounding splits iff we're the innermost var
-          if var_name == inner_var_name:
-            split_factor *= unroll
-            split_factor *= vectorize
-
-          print >> o, '.split({0}, {0}, {1}, {2})'.format(
-            last_var_name, var_name, split_factor)
-
-      # drop unused variables and truncate (Halide supports only 10 reorders)
-      if len(var_name_order[name]) > 1:
-        print >> o, '.reorder({0})'.format(
-            ', '.join(reversed(var_name_order[name][:10])))
-
-      # reorder_storage
-      store_order_enabled = cfg['{0}_store_order_enabled'.format(name)]
-      if store_order_enabled or not self.args.gated_store_reorder:
-        store_order = cfg['{0}_store_order'.format(name)]
-        if len(store_order) > 1:
-          print >> o, '.reorder_storage({0})'.format(', '.join(store_order))
-
-      if unroll > 1:
-        # apply unrolling to innermost var
-        print >> o, '.unroll({0}, {1})'.format(
-          var_name_order[name][-1], unroll * vectorize)
-
-      if vectorize > 1:
-        # apply vectorization to innermost var
-        print >> o, '.vectorize({0}, {1})'.format(
-          var_name_order[name][-1], vectorize)
-      
-      # compute_at(not root)
-      if (compute_at[name] is not None and
-              len(var_name_order[compute_at[name][0]]) >= compute_at[name][1]):
-        at_func, at_idx = compute_at[name]
-        try:
-          at_var = var_name_order[at_func][-at_idx]
-          print >> o, '.compute_at(Halide::Func(funcs["{0}"]), {1})'.format(at_func, at_var)
-          if not self.args.enable_store_at:
-            pass  # disabled
-          elif store_at[name] is None:
-            print >> o, '.store_root()'
-          elif store_at[name] != compute_at[name]:
-            at_func, at_idx = store_at[name]
-            at_var = var_name_order[at_func][-at_idx]
-            print >> o, '.store_at(Halide::Func(funcs["{0}"]), {1})'.format(at_func, at_var)
-        except IndexError:
-          # this is expected when at_idx is too large
-          # TODO: implement a cleaner fix
-          pass
-      # compute_root
-      else:
-        parallel = cfg['{0}_parallel'.format(name)]
-        if parallel:
-          # only apply parallelism to outermost var of root funcs
-          print >> o, '.parallel({0})'.format(var_name_order[name][0])
-        print >> o, '.compute_root()'
-
-      print >> o, ';'
-
-    if temp_vars:
-      return 'Halide::Var {0};\n{1}'.format(
-        ', '.join(temp_vars), o.getvalue())
-    else:
-      return o.getvalue()
-
-  def schedule_to_source(self, schedule):
-    """
-    Generate a temporary Halide cpp file with schedule inserted
-    """
-
-    def repl_autotune_hook(match):
-      tmpl = '''
-    {
-        std::map<std::string, Halide::Internal::Function> funcs = Halide::Internal::find_transitive_calls((%(func)s).function());
-
-        %(sched)s
-
-        _autotune_timing_stub(%(func)s);
-    }'''
-      return tmpl % {"sched": schedule.replace('\n', '\n        '), "func": match.group(1)}
-
-    source = re.sub(r'\n\s*AUTOTUNE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
-                    repl_autotune_hook, self.template)
-    return source
-
-  def run_schedule(self, schedule, limit):
-    """
-    Generate a temporary Halide cpp file with schedule inserted and run it
-    with our timing harness found in timing_prefix.h.
-    """
-    return self.run_source(self.schedule_to_source(schedule), limit)
-
-  def run_baseline(self):
-    """
-    Generate a temporary Halide cpp file with schedule inserted and run it
-    with our timing harness found in timing_prefix.h.
-    """
-
-    def repl_autotune_hook(match):
-      return '\n\n_autotune_timing_stub(%s);' % match.group(1)
-
-    source = re.sub(r'\n\s*BASELINE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
-                    repl_autotune_hook, self.template)
-    return self.run_source(source)
-
-  def run_source(self, source, limit=0, extra_args=''):
-    cmd = ''
-    with tempfile.NamedTemporaryFile(suffix='.cpp', prefix='halide',
-                                     dir=self.args.tmp_dir) as cppfile:
-      cppfile.write(source)
-      cppfile.flush()
-      # binfile = os.path.splitext(cppfile.name)[0] + '.bin'
-      # binfile = '/tmp/halide.bin'
-      binfile = ''
-      with tempfile.NamedTemporaryFile(suffix='.bin', prefix='halide',
-                                               dir=self.args.tmp_dir, delete=False) as binfiletmp:
-
-        binfile = binfiletmp.name # unique temp file to allow multiple concurrent tuner runs
-      assert(binfile)
-      cmd = self.args.compile_command.format(
-        cpp=cppfile.name, bin=binfile, args=self.args,
-        limit=math.ceil(limit) if limit < float('inf') else 0)
-      cmd += ' ' + extra_args
-      compile_result = self.call_program(cmd, limit=self.args.limit,
-                                         memory_limit=self.args.memory_limit)
-      if compile_result['returncode'] != 0:
-        log.error('compile failed: %s', compile_result)
-        return None
-
-    try:
-      result = self.call_program(binfile,
-                                 limit=self.args.limit,
-                                 memory_limit=self.args.memory_limit)
-      stdout = result['stdout']
-      stderr = result['stderr']
-      returncode = result['returncode']
-
-      if result['timeout']:
-        log.info('compiler timeout %d (%.2f+%.0f cost)', self.args.limit,
-                 compile_result['time'], self.args.limit)
-        return float('inf')
-      elif returncode == 142 or returncode == -14:
-        log.info('program timeout %d (%.2f+%.2f cost)', math.ceil(limit),
-                 compile_result['time'], result['time'])
-        return None
-      elif returncode != 0:
-        log.error('invalid schedule (returncode=%d): %s', returncode,
-                  stderr.strip())
-        with tempfile.NamedTemporaryFile(suffix='.cpp', prefix='halide-error',
-                                         dir=self.args.tmp_dir, delete=False) as errfile:
-          errfile.write(source)
-          log.error('failed schedule logged to %s.\ncompile as `%s`.', errfile.name, cmd)
-        if self.args.debug_error is not None and (
-            self.args.debug_error in stderr
-        or self.args.debug_error == ""):
-          self.debug_schedule('/tmp/halideerror.cpp', source)
-        return None
-      else:
-        try:
-          time = json.loads(stdout)['time']
-        except:
-          log.exception('error parsing output: %s', result)
-          return None
-        log.info('success: %.4f (collection cost %.2f + %.2f)',
-                 time, compile_result['time'], result['time'])
-        self.min_collection_cost = min(
-          self.min_collection_cost, result['time'])
-        return time
-    finally:
-      os.unlink(binfile)
-
-  def run_cfg(self, cfg, limit=0):
-    try:
-      schedule = self.cfg_to_schedule(cfg)
-    except:
-      log.exception('error generating schedule')
-      return None
-    return self.run_schedule(schedule, limit)
-
-  def run(self, desired_result, input, limit):
-    time = self.run_cfg(desired_result.configuration.data, limit)
-    if time is not None:
-      return opentuner.resultsdb.models.Result(time=time)
-    else:
-      return opentuner.resultsdb.models.Result(state='ERROR',
-                                               time=float('inf'))
-
-  def save_final_config(self, configuration):
-    """called at the end of tuning"""
-    print 'Final Configuration:'
-    print self.cfg_to_schedule(configuration.data)
-
-  def debug_log_schedule(self, filename, source):
-    open(filename, 'w').write(source)
-    print 'offending schedule written to {0}'.format(filename)
-
-  def debug_schedule(self, filename, source):
-    self.debug_log_schedule(filename, source)
-    raw_input('press ENTER to continue')
-
-  def make_settings_file(self):
-    dump_call_graph_dir = os.path.join(os.path.dirname(__file__),
-                                       'dump-call-graph')
-    if not os.path.isdir(dump_call_graph_dir):
-      subprocess.check_call(['git', 'clone',
-                             'http://github.com/halide/dump-call-graph.git'])
-      assert os.path.isdir(dump_call_graph_dir)
-
-    dump_call_graph_cpp = os.path.join(dump_call_graph_dir, 'DumpCallGraph.cpp')
-    callgraph_file = self.args.settings_file + '.callgraph'
-
-    def repl_autotune_hook(match):
-      return r'''dump_call_graph("%s", %s);
-                 printf("{\"time\": 0}\n");
-                 exit(0);''' % (callgraph_file, match.group(1))
-
-    source = re.sub(r'\n\s*AUTOTUNE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
-                    repl_autotune_hook, self.template)
-    # TODO: BUG! - this only works correctly if given an absolute path to the
-    # program (or explicit settings file). Otherwise it generates the callgraph
-    # in a tmp dir somewhere and fails to find it in a local path here.
-    source = open(dump_call_graph_cpp).read() + source
-    self.run_source(source, extra_args='-I{0}'.format(dump_call_graph_dir))
-    callgraph = json.load(open(callgraph_file))
-    settings = {'input_size': '1024, 1024', 'functions': callgraph}
-    json.dump(settings, open(self.args.settings_file, 'w'), sort_keys=True,
-              indent=2)
-    print textwrap.dedent('''
-
-      {0} has been generated based on call graph of program.
-
-      This file likely needs some manual tweaks in order to work correctly.
-      The input size should be changed to have the right number of dimensions.
-      Any naming differences between variable names and function names must
-      be applied manually.  Some temporary variables not in the source code
-      need to be manually removed.
-
-    '''.format(self.args.settings_file))
-
-
-class ComputeAtStoreAtParser(object):
-  """
-  A recursive descent parser to force proper loop nesting, and enforce post
-  dominator scheduling constraints
-
-  For each function input will have tokens like:
-  ('foo', 's') = store_at location for foo
-  ('foo', '2'), ('foo', '1') = opening the loop nests for foo,
-                               the inner 2 variables
-  ('foo', 'c') = the computation of foo, and closing all loop nests
-
-  The order of these tokens define a loop nest tree which we reconstruct
-  """
-
-  def __init__(self, tokens, post_dominators):
-    self.tokens = list(tokens)  # input, processed back to front
-    self.post_dominators = post_dominators
-    self.compute_at = dict()
-    self.store_at = dict()
-    self.process_root()
-
-  def process_root(self):
-    old_len = len(self.tokens)
-    out = []
-    while self.tokens:
-      if self.tokens[-1][1] == 's':
-        # store at root
-        self.store_at[self.tokens[-1][0]] = None
-        out.append(self.tokens.pop())
-      else:
-        self.process_loopnest(out, [])
-    self.tokens = list(reversed(out))
-    assert old_len == len(self.tokens)
-
-  def process_loopnest(self, out, stack):
-    func, idx = self.tokens[-1]
-    out.append(self.tokens.pop())
-    if idx != 'c':
-      raise Exception('Invalid schedule')
-
-    self.compute_at[func] = None
-    for targ_func, targ_idx in reversed(stack):
-      if targ_func in self.post_dominators[func]:
-        self.compute_at[func] = (targ_func, targ_idx)
-        break
-
-    close_tokens = [(f, i) for f, i in self.tokens if f == func and i != 's']
-    while close_tokens:
-      if self.tokens[-1] == close_tokens[-1]:
-        # proper nesting
-        close_tokens.pop()
-        out.append(self.tokens.pop())
-      elif self.tokens[-1][1] == 'c':
-        self.process_loopnest(out, stack + close_tokens[-1:])
-      elif self.tokens[-1][1] == 's':
-        # self.tokens[-1] is computed at this level
-        if func in self.post_dominators[self.tokens[-1][0]]:
-          self.store_at[self.tokens[-1][0]] = close_tokens[-1]
-        else:
-          self.store_at[self.tokens[-1][0]] = None
-        out.append(self.tokens.pop())
-      else:
-        # improper nesting, just close the loop and search/delete close_tokens
-        out.extend(reversed(close_tokens))
-        self.tokens = [x for x in self.tokens if x not in close_tokens]
-        break
-
-
-class HalideConfigurationManipulator(ConfigurationManipulator):
-  def __init__(self, halide_tuner):
-    super(HalideConfigurationManipulator, self).__init__()
-    self.halide_tuner = halide_tuner
-
-  def hash_config(self, config):
-    """
-    Multiple configs can lead to the same schedule, so we provide a custom
-    hash function that hashes the resulting schedule instead of the raw config.
-    This will lead to fewer duplicate tests.
-    """
-    self.normalize(config)
-    try:
-      schedule = self.halide_tuner.cfg_to_schedule(config)
-      return hashlib.sha256(schedule).hexdigest()
-    except:
-      log.warning('error hashing config', exc_info=True)
-      return super(HalideConfigurationManipulator, self).hash_config(config)
-
-
-class HalideComputeAtScheduleParameter(ScheduleParameter):
-  def __init__(self, name, args, functions, post_dominators):
-    """
-    Custom ScheduleParameter that normalizes using ComputeAtStoreAtParser
-    """
-    super(HalideComputeAtScheduleParameter, self).__init__(
-      name, *self.gen_nodes_deps(args, functions))
-    self.post_dominators = post_dominators
-
-  def gen_nodes_deps(self, args, functions):
-    """
-    Compute the list of nodes and point-to-point deps to provide to base class
-    """
-    nodes = list()
-    deps = collections.defaultdict(list)
-    for func in functions:
-      last = None
-      for idx in reversed(['c'] + # 'c' = compute location (and close loops)
-          range(1, len(func['vars']) * args.nesting + 1) +
-          ['s']):  # 's' = storage location
-        name = (func['name'], idx)
-        if last is not None:
-          # variables must go in order
-          deps[last].append(name)
-        last = name
-        nodes.append(name)
-        if idx == 'c':
-          # computes must follow call graph order
-          for callee in func['calls']:
-            deps[(callee, 'c')].append(name)
-    return nodes, deps
-
-  def normalize(self, cfg):
-    """
-    First enforce basic point-to-point deps (in base class), then call
-    ComputeAtStoreAtParser to normalize schedule.
-    """
-    super(HalideComputeAtScheduleParameter, self).normalize(cfg)
-    cfg[self.name] = ComputeAtStoreAtParser(cfg[self.name],
-                                            self.post_dominators).tokens
-
-
-def post_dominators(settings):
-  """
-  Compute post dominator tree using textbook iterative algorithm for the
-  call graph defined in settings
-  """
-  functions = [f['name'] for f in settings['functions']]
-  calls = dict([(f['name'], set(f['calls'])) for f in settings['functions']])
-  inverse_calls = collections.defaultdict(set)
-  for k, callees in calls.items():
-    for v in callees:
-      inverse_calls[v].add(k)
-  dom = {functions[-1]: set([functions[-1]])}
-  for f in functions[:-1]:
-    dom[f] = set(functions)
-  change = True
-  while change:
-    change = False
-    for f in functions[:-1]:
-      old = dom[f]
-      dom[f] = set([f]) | reduce(
-        _ & _, [dom[c] for c in inverse_calls[f]], set(functions))
-      if old != dom[f]:
-        change = True
-  return dom
-
-
-def random_test(args):
-  """
-  Generate and run a random schedule
-  """
-
-  opentuner.tuningrunmain.init_logging()
-  m = HalideTuner(args)
-  cfg = m.manipulator().random()
-  pprint(cfg)
-  print
-  schedule = m.cfg_to_schedule(cfg)
-  print schedule
-  print
-  print 'Schedule', m.run_schedule(schedule, 30)
-  print 'Baseline', m.run_baseline()
-
-
-def random_source(args):
-  """
-  Dump the source code of a random schedule
-  """
-  opentuner.tuningrunmain.init_logging()
-  m = HalideTuner(args)
-  cfg = m.manipulator().random()
-  schedule = m.cfg_to_schedule(cfg)
-  source = m.schedule_to_source(schedule)
-  print source
-
-
-def main(args):
-  if args.random_test:
-    random_test(args)
-  elif args.random_source:
-    random_source(args)
-  elif args.make_settings_file:
-    opentuner.tuningrunmain.init_logging()
-    HalideTuner(args).make_settings_file()
-  else:
-    HalideTuner.main(args)
-
-
-if __name__ == '__main__':
-  main(parser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/timing_prefix.h b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/timing_prefix.h
deleted file mode 100644
index d8bbc5f57b6177f3a88a28d57fef2d72bf8c3050..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/halide/timing_prefix.h
+++ /dev/null
@@ -1,100 +0,0 @@
-#include <Halide.h>
-#include <stdio.h>
-#include <sys/time.h>
-#include <unistd.h>
-
-#include <map>
-#include <string>
-
-// How many times to run (and take min)
-// #define AUTOTUNE_TRIALS 3
-
-// Limit in seconds to try running for (0 = no limit)
-// #define AUTOTUNE_LIMIT 0
-
-// Size to run with
-// #define AUTOTUNE_N 1024, 1024
-
-inline void _autotune_timing_stub(Halide::Func& func) {
-    func.compile_jit();
-
-    // TODO: this assumes scalar/non-Tuple outputs - should generalize to a Realization
-    std::vector<Halide::Type> out_types = func.output_types();
-    std::vector<buffer_t> out_raw_bufs;
-    std::vector<Halide::Buffer> out_bufs;
-
-    for (int i = 0; i < out_types.size(); i++) {
-        // Use the Buffer constructor as a helper to set up the buffer_t,
-        // but then throw away its allocation which we don't really want.
-        Halide::Buffer bufinit(out_types[i], AUTOTUNE_N);
-        out_raw_bufs.push_back(*bufinit.raw_buffer());
-        out_raw_bufs[i].host = NULL;
-        // TODO: free the host pointer?!
-        out_bufs.push_back(Halide::Buffer(out_types[i], &out_raw_bufs[i]));
-        assert(out_bufs[i].host_ptr() == NULL); // make sure we don't have an allocation
-    }
-    Halide::Realization output(out_bufs);
-    func.infer_input_bounds(output);
-    // assert(output[0].host_ptr()); // for now, the API doesn't seem to allocate outputs
-    
-    // TODO: this should go into Func::infer_input_bounds(Realization)
-    for (int i = 0; i < output.size(); i++) {
-        assert(!output[i].host_ptr()); // for now, the API doesn't seem to allocate outputs
-        buffer_t buf = *output[i].raw_buffer();
-        
-        // Figure out how much memory to allocate for this buffer
-        size_t min_idx = 0, max_idx = 0;
-        for (int d = 0; d < 4; d++) {
-            if (buf.stride[d] > 0) {
-                min_idx += buf.min[d] * buf.stride[d];
-                max_idx += (buf.min[d] + buf.extent[d] - 1) * buf.stride[d];
-            } else {
-                max_idx += buf.min[d] * buf.stride[d];
-                min_idx += (buf.min[d] + buf.extent[d] - 1) * buf.stride[d];
-            }
-        }
-        size_t total_size = (max_idx - min_idx);
-        while (total_size & 0x1f) total_size++;
-
-        // Allocate enough memory with the right dimensionality.
-        Halide::Buffer buffer(output[i].type(), total_size,
-                      buf.extent[1] > 0 ? 1 : 0,
-                      buf.extent[2] > 0 ? 1 : 0,
-                      buf.extent[3] > 0 ? 1 : 0);
-
-        // Rewrite the buffer fields to match the ones returned
-        for (int d = 0; d < 4; d++) {
-            buffer.raw_buffer()->min[d] = buf.min[d];
-            buffer.raw_buffer()->stride[d] = buf.stride[d];
-            buffer.raw_buffer()->extent[d] = buf.extent[d];
-        }
-        
-        output[i] = buffer;
-    }
-
-    timeval t1, t2;
-    double rv = 0;
-    const unsigned int timeout = AUTOTUNE_LIMIT;
-    alarm(timeout);
-    for (int i = 0; i < AUTOTUNE_TRIALS; i++) {
-      gettimeofday(&t1, NULL);
-      func.realize(output);
-      gettimeofday(&t2, NULL);
-      alarm(0); // disable alarm
-      double t = (t2.tv_sec - t1.tv_sec) + (t2.tv_usec - t1.tv_usec)/1000000.0;
-      if(i == 0 || t < rv)
-        rv = t;
-    }
-    printf("{\"time\": %.10f}\n", rv);
-    exit(0);
-}
-
-
-#ifndef AUTOTUNE_HOOK
-#define AUTOTUNE_HOOK(x)
-#endif
-
-#ifndef BASELINE_HOOK
-#define BASELINE_HOOK(x)
-#endif
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/HPL.dat.mako b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/HPL.dat.mako
deleted file mode 100644
index 93354a2292a3bb3ddec1e2278e49b039b72d6bb1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/HPL.dat.mako
+++ /dev/null
@@ -1,31 +0,0 @@
-HPLinpack benchmark input file
-Innovative Computing Laboratory, University of Tennessee
-HPL.out      output file name (if any)
-0            device out (6=stdout,7=stderr,file)
-1            # of problems sizes (N)
-${size}        Ns
-1            # of NBs
-${blocksize}		      NBs
-${row_or_colmajor_pmapping}            PMAP process mapping (0=Row-,1=Column-major)
-1            # of process grids (P x Q)
-2	        Ps  PxQ must equal nprocs
-2           Qs
-16.0         threshold
-1            # of panel fact
-${pfact}            PFACTs (0=left, 1=Crout, 2=Right)
-1            # of recursive stopping criterium
-${nbmin}            NBMINs (>= 1)
-1            # of panels in recursion
-${ndiv}            NDIVs
-1            # of recursive panel fact.
-${rfact}            RFACTs (0=left, 1=Crout, 2=Right)
-1            # of broadcast
-${bcast}            BCASTs (0=1rg,1=1rM,2=2rg,3=2rM,4=Lng,5=LnM)
-1            # of lookahead depth
-${depth}            DEPTHs (>=0)
-${swap}            SWAP (0=bin-exch,1=long,2=mix)
-${swapping_threshold}           swapping threshold (default had 64)
-${L1_transposed}            L1 in (0=transposed,1=no-transposed) form
-${U_transposed}            U  in (0=transposed,1=no-transposed) form
-1            Equilibration (0=no,1=yes)
-${mem_alignment}            memory alignment in double (> 0) (4,8,16)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/hpl.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/hpl.py
deleted file mode 100644
index 4cbbe798249b61eae23b5142337a056ef58e83bd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/hpl/hpl.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import adddeps #fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
-                                          IntegerParameter,
-                                          FloatParameter)
-from opentuner.search.objective import MinimizeTime
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-
-log = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-
-parser.add_argument('--size', type=int, default=800,
-                    help='dimensions for the HPL matrix')
-parser.add_argument('--nprocs', type=int, default=4,
-                    help='number of processors for each HPL run (minimum=4)')
-parser.add_argument('--xhpl', type=str, default="hpl-2.1/bin/OSX/xhpl",
-                    help='location of xhpl binary')
-
-class HPLinpack(MeasurementInterface):
-    def run(self, desired_result, input, limit):
-        self.output_hpl_datfile(desired_result.configuration.data)
-        import subprocess, os
-        binary = self.args.xhpl
-        subprocess.call(["mpirun", "-np", str(self.args.nprocs), binary])
-        
-        val = self.get_time_from_hpl_output()
-        
-        return opentuner.resultsdb.models.Result(time=val)
-        
-    def manipulator(self):
-        #FIXME: should some of these be expressed as booleans or switch parameters?
-        #FIXME: how to express P and Q, given PxQ=nprocs, with nprocs being fixed?
-        #FIXME: how to express logscaled parameter with a particular base?
-        manipulator = ConfigurationManipulator()
-        manipulator.add_parameter(IntegerParameter("blocksize", 1, 64))
-        manipulator.add_parameter(IntegerParameter("row_or_colmajor_pmapping", 0, 1))
-        manipulator.add_parameter(IntegerParameter("pfact", 0, 2))
-        manipulator.add_parameter(IntegerParameter("nbmin", 1, 4))
-        manipulator.add_parameter(IntegerParameter("ndiv", 2, 2))
-        manipulator.add_parameter(IntegerParameter("rfact", 0, 4))
-        manipulator.add_parameter(IntegerParameter("bcast", 0, 5))
-        manipulator.add_parameter(IntegerParameter("depth", 0, 4))
-        manipulator.add_parameter(IntegerParameter("swap", 0, 2))
-        manipulator.add_parameter(IntegerParameter("swapping_threshold", 64, 128))
-        manipulator.add_parameter(IntegerParameter("L1_transposed", 0, 1))
-        manipulator.add_parameter(IntegerParameter("U_transposed", 0, 1))
-        manipulator.add_parameter(IntegerParameter("mem_alignment", 4, 16))
-        
-        return manipulator
-        
-    def output_hpl_datfile(self, params):
-        """HPL uses an input file to express the parameters, and this uses mako to render it."""
-        params["size"] = self.args.size
-        from mako.template import Template
-        template = Template(filename="HPL.dat.mako")
-        with open("HPL.dat", "w") as f:
-            f.write(template.render(**params))
-            
-    def get_time_from_hpl_output(self, fname="HPL.out"):
-        """Returns the elapsed time only, from the HPL output file"""
-        #FIXME: clean up with REs
-        elapsed = 0.0
-        with open(fname) as f:
-            line = f.readline()
-            while (line[0:3] != "T/V"):
-                line = f.readline()
-            line = f.readline()
-            while (line[0:3] != "T/V"):
-                line = f.readline()
-            f.readline() # line of dashes
-            splitted = f.readline().split()
-            elapsed = float(splitted[5])
-        
-        return elapsed
-                    
-    
-    def program_name(self):
-        return "HPL"
-    
-    def program_version(self):
-      return "size=%d,nprocs=%d" % (self.args.size, self.args.nprocs)
-
-    def save_final_config(self, configuration):
-      '''
-      called at the end of autotuning with the best resultsdb.models.Configuration
-      '''
-      print "Final configuration", configuration.data
-            
-if __name__ == '__main__':
-  args = parser.parse_args()
-  HPLinpack.main(args)
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/README.md b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/README.md
deleted file mode 100644
index f094e987f5e48d72aef426b39ef268e86f47e3c0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-This is an OpenTuner-based tuner that learns a series of button presses that complete the first level of Super Mario Bros. for the original Nintendo Entertainment System.
-
-## Dependencies
-
-- FCEUX, a NES emulator
-- `xvfb-run`, to run the emulator headless (optional, but speeds up tuning)
-- Super Mario Bros., assumed to be named `smb.nes`, which we can't help you get for legal reasons
-
-## Running
-
-Run the tuner with `./mario.py --technique=PSO_GA_Bandit`; it will launch FCEUX to run trials.  You can experiment with other techniques or `--parallelism` (the number of trials to run in parallel) too.
-
-You can implement your own configuration representation by subclassing Representation and passing `--representation=YourRepresentation`.  Your Representation class needs to provide a ConfigurationManipulator populated with parameters and a method to translate these parameters to button presses.  There are already a few representations implemented to use as examples.
-
-You can implement your own fitness function by subclassing FitnessFunction and passing `--fitness-function=YourFunction`.  Your function receives a win/loss boolean, the number of pixels moved to the right when the trial ended, and the number of frames that elapsed during the trial.  Lower fitness scores are better.  There are a few existing fitness functions; in particular, `ProgressTimesAverageSpeed` also tries to optimize speed.
-
-If you want to watch the trials (or don't have `xvfb-run` available), pass `--headful`.
-
-## Playing the results
-
-When a tuning run completes, the best configuration (as judged by the fitness function) is written to `<hostname>-<tuningrun>.fm2`.  This file can be played back in FCEUX to watch the best configuration.
-
-You can also use the `--tuning-run=` option (passing the tuning run number in the best configuration `.fm2`) to generate a new-bests `.fm2`, which will contain each tuning trial that was the best configuration found so far during the tuning run, concatenated back-to-back.  You also need to pass `--database` pointing to the database containing that tuning run, and if you passed `--representation` or `--fitness-function` during the tuning run, you need to pass the same values for those parameters.  So your final command might look like `./mario.py --tuning-run=42 --database=opentuner.db/hostname.db --representation=NaiveRepresentation --fitness-function=ProgressTimesAverageSpeed > new-bests-42.fm2`.
-
-## TODO
-
-- use the [fm2 format](http://www.fceux.com/web/help/fceux.html?fm2.html)'s subtitle support in new-bests movies to show run number and fitness score
-
-## Links
-
-- [Videos showing OpenTuner playing Super Mario Bros](https://www.youtube.com/playlist?list=PLngnz1zPEA08FWy8wF9JbGqjlm-elHmlb)
-- [Slides describing representation and results](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf) (see slide 16)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/fceux-hook.lua b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/fceux-hook.lua
deleted file mode 100644
index ce00288149936154f5dc64f94cdbcbcba04d4758..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/fceux-hook.lua
+++ /dev/null
@@ -1,25 +0,0 @@
-player_state_addr = 0x000E;
-player_state_dying = 6;
-player_float_addr = 0x001D;
-player_float_flagpole = 3;
-player_page_addr = 0x006D;
-player_horizpos_addr = 0x0086;
-minimum_frames = 197;
-
-emu.speedmode("maximum");
-while true do
-	if (emu.framecount() > minimum_frames) then
-		--dead?
-		local dead = memory.readbyte(player_state_addr) == player_state_dying;
-		--flagpole?
-		local won = memory.readbyte(player_float_addr) == player_float_flagpole;
-		if (dead or won) then
-			local str = (dead and "died" or "won");
-			local x_pos = math.floor(memory.readbyteunsigned(player_page_addr)*256 + memory.readbyteunsigned(player_horizpos_addr));
-			local framecount = emu.framecount();
-			io.write(str, " ", x_pos, " ", framecount, "\n");
-			os.exit(0);
-		end;
-	end;
-	emu.frameadvance();
-end
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/mario.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/mario.py
deleted file mode 100755
index d388321e1f3b4f70a5d1262f43b89702ea924c79..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/mario/mario.py
+++ /dev/null
@@ -1,341 +0,0 @@
-#!/usr/bin/env python2
-
-"""OpenTuner plays Super Mario Bros. for NES
-
-We write a movie file and ask the emulator to play it back while running
-fceux-hook.lua, which checks for death/flagpole and prints the fitness to
-stdout where OpenTuner, as the parent process, can read it.
-"""
-
-import adddeps #fix sys.path
-import argparse
-import base64
-import pickle
-import tempfile
-import subprocess
-import re
-import zlib
-import abc
-import sys
-import os
-import traceback
-import collections
-import socket
-
-import opentuner
-from opentuner.search.manipulator import ConfigurationManipulator, IntegerParameter, EnumParameter, BooleanParameter
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-from opentuner.search.objective import MinimizeTime
-
-def instantiate(class_name):
-  return getattr(sys.modules[__name__], class_name)()
-
-argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-argparser.add_argument('--tuning-run', type=int, help='concatenate new bests from given tuning run into single movie')
-argparser.add_argument('--headful', action='store_true', help='run headful (not headless) for debugging or live demo')
-argparser.add_argument('--xvfb-delay', type=int, default=0, help='delay between launching xvfb and fceux')
-argparser.add_argument('--representation', default='DurationRepresentation', type=instantiate, help='name of representation class')
-argparser.add_argument('--fitness-function', default='Progress', type=instantiate, help='name of fitness function class')
-
-def call_or_die(command, failmsg=None):
-  try:
-    p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    stdout, stderr = p.communicate()
-    return stdout, stderr, p.returncode
-  except:
-    print "Failed to execute", command
-    traceback.print_exc()
-    print "Child traceback:"
-    print sys.exc_info()[1].child_traceback
-    if failmsg:
-      print failmsg
-    sys.exit(1)
-
-# Functions for building FCEUX movie files (.fm2 files)
-
-def fm2_line(up, down, left, right, a, b, start, select, reset=False):
-  """formats one frame of input with the given button presses"""
-  return ''.join(('|1|' if reset else '|0|') +
-    ('R' if right else '.') +
-    ('L' if left else '.') +
-    ('D' if down else '.') +
-    ('U' if up else '.') +
-    ('T' if start else '.') +
-    ('D' if select else '.') +
-    ('B' if b else '.') +
-    ('A' if a else '.') +
-    '|........||')
-
-def maxd(iterable, default):
-  try:
-    return max(iterable)
-  except ValueError:
-    return default
-
-def fm2_lines(up, down, left, right, a, b, start, select, reset=set(), minFrame=None, maxFrame=None):
-  """formats many frames using the given button-press sets"""
-  if minFrame is None:
-    minFrame = 0
-  if maxFrame is None:
-    maxFrame = max(maxd(up, 0), maxd(down, 0), maxd(left, 0), maxd(right, 0), maxd(a, 0), maxd(b, 0), maxd(start, 0), maxd(select, 0), maxd(reset, 0)) + 1
-  lines = list()
-  for i in xrange(minFrame, maxFrame):
-    lines.append(fm2_line(i in up, i in down, i in left, i in right, i in a, i in b, i in start, i in select, i in reset))
-  return lines
-
-def fm2_smb_header():
-  return ["version 3",
-    "emuVersion 9828",
-    "romFilename smb.nes",
-    "romChecksum base64:jjYwGG411HcjG/j9UOVM3Q==",
-    "guid 51473540-E9D7-11E3-ADFC-46CE3219C4E0",
-    "fourscore 0",
-    "port0 1",
-    "port1 1",
-    "port2 0"]
-
-def fm2_smb(left, right, down, b, a, header=True, padding=True, minFrame=None, maxFrame=None):
-  reset = set()
-  start = set()
-  if padding:
-    left = set([x+196 for x in left])
-    right = set([x+196 for x in right])
-    down = set([x+196 for x in down])
-    b = set([x+196 for x in b])
-    a = set([x+196 for x in a])
-    reset.add(0)
-    start.add(33)
-  lines = fm2_lines(set(), down, left, right, a, b, start, set(), reset, minFrame, maxFrame)
-  if header:
-    return "\n".join(fm2_smb_header() + lines)
-  else:
-    return "\n".join(lines)
-
-display_numbers = collections.deque()
-
-def run_movie(fm2, args):
-  with tempfile.NamedTemporaryFile(suffix=".fm2", delete=True) as f:
-    f.write(fm2)
-    f.flush()
-    cmd = []
-    if not args.headful:
-      display = display_numbers.pop()
-      cmd += ["xvfb-run", "-n", display, "-w", str(args.xvfb_delay), "-e", "/dev/stderr"]
-    cmd += ["fceux", "--playmov", f.name, "--loadlua",
-        "fceux-hook.lua", "--nogui", "--volume", "0", "--no-config", "1",
-        "smb.nes"]
-    stdout, stderr, returncode = call_or_die(cmd)
-    if not args.headful:
-      display_numbers.append(display)
-  match = re.search(r"^(won|died) (\d+) (\d+)$", stdout, re.MULTILINE)
-  if not match:
-    print stderr
-    print stdout
-    raise ValueError
-  wl = match.group(1)
-  x_pos = int(match.group(2))
-  framecount = int(match.group(3))
-  return (wl, x_pos, framecount)
-
-class Representation(object):
-  """Interface for pluggable tuning representations."""
-  __metaclass__ = abc.ABCMeta
-
-  @abc.abstractmethod
-  def manipulator():
-    """Return a ConfigurationManipulator for this representation."""
-    pass
-
-  @abc.abstractmethod
-  def interpret(cfg):
-    """Unpack this representation into button-press sets (L, R, D, B, A)."""
-    pass
-
-class NaiveRepresentation(Representation):
-  """Uses a parameter per (button, frame) pair."""
-  def manipulator(self):
-    m = ConfigurationManipulator()
-    for i in xrange(0, 12000):
-      m.add_parameter(BooleanParameter('L{}'.format(i)))
-      m.add_parameter(BooleanParameter('R{}'.format(i)))
-      m.add_parameter(BooleanParameter('D{}'.format(i)))
-      m.add_parameter(BooleanParameter('B{}'.format(i)))
-      m.add_parameter(BooleanParameter('A{}'.format(i)))
-    return m
-
-  def interpret(self, cfg):
-    left = set()
-    right = set()
-    down = set()
-    running = set()
-    jumping = set()
-    for i in xrange(0, 12000):
-      if cfg['L{}'.format(i)]:
-        left.add(i)
-      if cfg['R{}'.format(i)]:
-        right.add(i)
-      if cfg['D{}'.format(i)]:
-        down.add(i)
-      if cfg['B{}'.format(i)]:
-        running.add(i)
-      if cfg['A{}'.format(i)]:
-        jumping.add(i)
-    return left, right, down, running, jumping
-
-class DurationRepresentation(Representation):
-  def manipulator(self):
-    m = ConfigurationManipulator()
-    for i in xrange(0, 1000):
-      #bias 3:1 in favor of moving right
-      m.add_parameter(EnumParameter('move{}'.format(i), ["R", "L", "RB", "LB", "N", "LR", "LRB", "R2", "RB2", "R3", "RB3"]))
-      m.add_parameter(IntegerParameter('move_duration{}'.format(i), 1, 60))
-      #m.add_parameter(BooleanParameter("D"+str(i)))
-    for i in xrange(0, 1000):
-      m.add_parameter(IntegerParameter('jump_frame{}'.format(i), 0, 24000))
-      m.add_parameter(IntegerParameter('jump_duration{}'.format(i), 1, 32))
-    return m
-
-  def interpret(self, cfg):
-    left = set()
-    right = set()
-    down = set()
-    running = set()
-    start = 0
-    for i in xrange(0, 1000):
-      move = cfg['move{}'.format(i)]
-      move_duration = cfg['move_duration{}'.format(i)]
-      if "R" in move:
-        right.update(xrange(start, start + move_duration))
-      if "L" in move:
-        left.update(xrange(start, start + move_duration))
-      if "B" in move:
-        running.update(xrange(start, start + move_duration))
-      start += move_duration
-    jumping = set()
-    for i in xrange(0, 1000):
-      jump_frame = cfg['jump_frame{}'.format(i)]
-      jump_duration = cfg['jump_duration{}'.format(i)]
-      jumping.update(xrange(jump_frame, jump_frame + jump_duration))
-    return left, right, down, running, jumping
-
-class AlphabetRepresentation(Representation):
-  def manipulator(self):
-    m = ConfigurationManipulator()
-    for i in xrange(0, 400*60):
-      m.add_parameter(EnumParameter('{}'.format(i), xrange(0, 16)))
-    return m
-
-  def interpret(self, cfg):
-    left = set()
-    right = set()
-    down = set()
-    running = set()
-    jumping = set()
-    for i in xrange(0, 400*60):
-      bits = cfg[str(i)]
-      if bits & 1:
-        left.add(i)
-      if bits & 2:
-        right.add(i)
-      if bits & 4:
-        running.add(i)
-      if bits & 8:
-        jumping.add(i)
-      #if bits & 16:
-      #  down.add(i)
-    return left, right, down, running, jumping
-
-class FitnessFunction(object):
-  """Interface for pluggable fitness functions."""
-  __metaclass__ = abc.ABCMeta
-
-  @abc.abstractmethod
-  def __call__(won, x_pos, elapsed_frames):
-    """Return the fitness (float, lower is better)."""
-    pass
-
-class Progress(FitnessFunction):
-  def __call__(self, won, x_pos, elapsed_frames):
-    return -float(x_pos)
-
-class ProgressPlusTimeRemaining(FitnessFunction):
-  def __call__(self, won, x_pos, elapsed_frames):
-    """x_pos plus 1 for each frame remaining on the timer on a win.  This results in a large discontinuity at wins.  This was the fitness function used for the OpenTuner paper, though the paper only discussed time-to-first-win."""
-    return -float(x_pos + 400*60 - elapsed_frames) if won else -float(x_pos)
-
-class ProgressTimesAverageSpeed(FitnessFunction):
-  def __call__(self, won, x_pos, elapsed_frames):
-    return -x_pos * (float(x_pos)/elapsed_frames)
-
-class SMBMI(MeasurementInterface):
-  def __init__(self, args):
-    super(SMBMI, self).__init__(args)
-    self.parallel_compile = True
-    self.args = args
-
-  def manipulator(self):
-    return self.args.representation.manipulator()
-
-  def compile(self, cfg, id):
-    left, right, down, running, jumping = self.args.representation.interpret(cfg)
-    fm2 = fm2_smb(left, right, down, running, jumping)
-    try:
-      wl, x_pos, framecount = run_movie(fm2, self.args)
-    except ValueError:
-      return opentuner.resultsdb.models.Result(state='ERROR', time=float('inf'))
-    print wl, x_pos, framecount
-    return opentuner.resultsdb.models.Result(state='OK', time=self.args.fitness_function("won" in wl, x_pos, framecount))
-
-  def run_precompiled(self, desired_result, input, limit, compile_result, id):
-    return compile_result
-
-  def run(self, desired_result, input, limit):
-    pass
-
-  def save_final_config(self, cfg):
-    left, right, down, running, jumping = args.representation.interpret(cfg.data)
-    fm2 = fm2_smb(left, right, down, running, jumping)
-    _, _, framecount = run_movie(fm2, self.args)
-    filename = '{}-{}.fm2'.format(socket.gethostname(), self.driver.tuning_run.id)
-    with open(filename, 'w') as f:
-      f.write(fm2_smb(left, right, down, running, jumping, maxFrame=framecount))
-
-def new_bests_movie(args):
-  stdout, stderr, returncode = call_or_die(["sqlite3", args.database, "select configuration_id from result where tuning_run_id = %d and was_new_best = 1 order by collection_date;" % args.tuning_run])
-  if returncode:
-    print "Error retrieving new-best configurations:", stderr
-    sys.exit(1)
-  cids = stdout.split()
-  print '\n'.join(fm2_smb_header())
-  for cid in cids:
-    stdout, stderr, returncode = call_or_die(["sqlite3", args.database, "select quote(data) from configuration where id = %d;" % int(cid)])
-    if returncode:
-      print "Error retriving configuration data:", cid, stderr
-      sys.exit(1)
-    cfg = pickle.loads(zlib.decompress(base64.b16decode(stdout.strip()[2:-1])))
-    left, right, down, running, jumping = args.representation.interpret(cfg)
-    fm2 = fm2_smb(left, right, down, running, jumping)
-    _, _, framecount = run_movie(fm2, args)
-    print fm2_smb(left, right, down, running, jumping, header=False, maxFrame=framecount)
-
-if __name__ == '__main__':
-  args = argparser.parse_args()
-  call_or_die(["fceux", "--help"], failmsg="Is fceux on your PATH?")
-  if not args.headful:
-    call_or_die(["xvfb-run", "--help"], failmsg="Is xvfb-run on your PATH? (or, pass --headful)")
-    for n in xrange(99, 99 + args.parallelism):
-      display_numbers.append(str(n))
-  if args.tuning_run:
-    call_or_die(["sqlite3", "-version"], failmsg="Is sqlite3 on your PATH?")
-    if args.database is not None:
-      new_bests_movie(args)
-    else:
-      print "must specify --database"
-  else:
-    if os.path.isfile('smb.nes'):
-      SMBMI.main(args)
-    else:
-      print "smb.nes not found"
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/.gitignore
deleted file mode 100644
index a6e67132d61e0bd837b953376dc866031d5f742a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-linux_x86_64
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/README.md b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/README.md
deleted file mode 100644
index e4b446468658463cab275862143c95add38d0eb5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/README.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Source for PetaBricks binaries can be found at:
-  - https://github.com/petabricks/petabricks/
-  - https://code.google.com/p/petabricks/
-
-
-Basic usage for running the raw programs is:
-```
-./Prog --config=CONFIG -n=N --time --accuracy --max-sec=TIMEOUT --trials=1
-
---config=<STRING>
-    filename of the program configuration (see example in .cfg.default file)
---n=<INTEGER>
-    generate a random input of the given size and run it
---time
-    print timing results in xml format
---accuracy
-    print out accuracy of answer
---max-sec=<NUMBER> (default: 1.79769e+308)
-    terminate measurement if it exceeds the given number of seconds
-
-many more options are given by running ./Prog --help
-```
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/deps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/deps.py
deleted file mode 100644
index c03a106a85827c1c4faed505b78d4f18e168c7e9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/deps.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os
-import sys
-
-project_root = os.path.normpath(os.path.join(
-    os.path.dirname(os.path.abspath(__file__)), '../..'))
-sys.path.insert(0, project_root)
-
-
-try:
-  from lxml import etree
-except ImportError:
-  try:
-    # Python 2.5
-    import xml.etree.cElementTree as etree
-  except ImportError:
-    import xml.etree.ElementTree as etree
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/download_benchmarks.sh b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/download_benchmarks.sh
deleted file mode 100755
index aaf333b455a0414575b338625e45b58db8188c5b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/download_benchmarks.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/sh
-if test -e linux_x86_64
-then
-  echo "benchmarks already downloaded"
-else
-  wget -O- http://people.csail.mit.edu/jansel/petabricks_benchmarks_linux_x86_64.tar.bz2 | tar jxv
-fi
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/import_old_result.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/import_old_result.py
deleted file mode 100755
index 9add4ed035c50e2f7f21dc79f6af89571137b257..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/import_old_result.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python
-
-import adddeps  # fix sys.path
-
-import argparse
-import json
-import logging
-import os
-import re
-import sys
-import uuid
-import subprocess
-
-try:
-  from lxml import etree
-except ImportError:
-  import xml.etree.ElementTree as etree
-
-import opentuner
-from opentuner import resultsdb
-from datetime import datetime
-from datetime import timedelta
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('--database', default='opentuner.db/import.db')
-argparser.add_argument('--limit', type=float, default=10)
-argparser.add_argument('program')
-argparser.add_argument('candidatelog')
-
-
-def run(args, cfg):
-  limit = args.limit
-  cmd = [args.program,
-         '--time',
-         '--accuracy',
-         '--config=' + cfg,
-         '--max-sec=%.10f' % args.limit,
-         '-n=%d' % args.n]
-  p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-  out, err = p.communicate()
-
-  result = opentuner.resultsdb.models.Result()
-  try:
-    root = etree.XML(out)
-    result.time = float(root.find('stats/timing').get('average'))
-    result.accuracy = float(root.find('stats/accuracy').get('average'))
-    if result.time < limit + 3600:
-      result.state = 'OK'
-    else:
-      # time will be 2**31 if timeout
-      result.state = 'TIMEOUT'
-  except:
-    log.exception('run error')
-    log.warning('program crash, out = %s / err = %s', out, err)
-    result.state = 'ERROR'
-    result.time = float('inf')
-    result.accuracy = float('-inf')
-  return result
-
-
-def main(args):
-  if '://' not in args.database:
-    args.database = 'sqlite:///' + args.database
-  engine, Session = opentuner.resultsdb.connect(args.database)
-  session = Session()
-
-  program_settings = json.load(open(args.program + '.settings'))
-  args.n = program_settings['n']
-  args.technique = ['Imported']
-  objective = ThresholdAccuracyMinimizeTime(program_settings['accuracy'])
-
-  tuningrun = resultsdb.models.TuningRun(
-    uuid=uuid.uuid4().hex,
-    name='import',
-    args=args,
-    start_date=datetime.now(),
-    objective=objective,
-    program_version=resultsdb.models.ProgramVersion.get(
-      session, 'PetaBricksInterface', args.program, 'imported'),
-    state='COMPLETE',
-  )
-  session.add(tuningrun)
-
-  for gen, line in enumerate(open(args.candidatelog)):
-    if line[0] != '#':
-      line = re.split('\t', line)
-      date = tuningrun.start_date + timedelta(seconds=float(line[0]))
-      cfg = os.path.normpath(
-        os.path.join(os.path.dirname(args.candidatelog), '..', line[5]))
-      result = run(args, cfg)
-      result.was_new_best = True
-      result.tuning_run = tuningrun
-      result.collection_date = date
-      session.add(result)
-      desired_result = resultsdb.models.DesiredResult(
-        limit=args.limit,
-        tuning_run=tuningrun,
-        generation=gen,
-        requestor='Imported',
-        request_date=date,
-        start_date=date,
-        result=result,
-        state='COMPLETE')
-      session.add(desired_result)
-      tuningrun.end_date = date
-      print gen, date, result.time
-
-  session.commit()
-
-
-if __name__ == '__main__':
-  opentuner.tuningrunmain.init_logging()
-  sys.exit(main(argparser.parse_args()))
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/pbtuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/pbtuner.py
deleted file mode 100755
index 7163294494eec42b73a232b6ee00fb7164d2c691..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/pbtuner.py
+++ /dev/null
@@ -1,188 +0,0 @@
-#!/usr/bin/env python
-
-import adddeps  # fix sys.path
-
-import re
-import argparse
-import logging
-import subprocess
-import tempfile
-import json
-from pprint import pprint
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
-                                          IntegerParameter,
-                                          LogIntegerParameter,
-                                          FloatParameter,
-                                          LogFloatParameter,
-                                          SelectorParameter,
-                                          SwitchParameter,
-                                          PermutationParameter, )
-
-try:
-  from lxml import etree
-except ImportError:
-  import xml.etree.ElementTree as etree
-
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-
-log = logging.getLogger("pbtuner")
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('program',
-                    help='PetaBricks binary program to autotune')
-parser.add_argument('--program-cfg-default',
-                    help="override default program config exemplar location")
-parser.add_argument('--program-cfg-output',
-                    help="location final autotuned configuration is written")
-parser.add_argument('--program-settings',
-                    help="override default program settings file location")
-parser.add_argument('--program-input',
-                    help="use only a given input for autotuning")
-parser.add_argument('--upper-limit', type=float, default=30,
-                    help="time limit to apply to initial test")
-parser.add_argument('--test-config', action='store_true')
-
-
-class PetaBricksInterface(MeasurementInterface):
-  def __init__(self, args):
-    self.program_settings = json.load(open(args.program_settings))
-    input_manager = FixedInputManager(size=self.program_settings['n'])
-    objective = ThresholdAccuracyMinimizeTime(self.program_settings['accuracy'])
-
-    # pass many settings to parent constructor
-    super(PetaBricksInterface, self).__init__(
-        args, program_name=args.program,
-        program_version=self.file_hash(args.program),
-        input_manager=input_manager, objective=objective)
-
-  def build_config(self, cfg):
-    r = dict()
-
-    # direct copy
-    for k, v in cfg.iteritems():
-      if k[0] != '.':
-        r[k] = v
-
-    for name, choices in self.choice_sites.items():
-      param = self.manipulator.parameters_dict(cfg)['.' + name]
-      lvl = 0
-      for cutoff, choice in param.selector_iter(cfg):
-        lvl += 1
-        r['%s_lvl%d_rule' % (name, lvl)] = choice
-        if lvl > 1:
-          r['%s_lvl%d_cutoff' % (name, lvl)] = cutoff
-
-    return r
-
-  def run(self, desired_result, input, limit):
-    limit = min(limit, self.args.upper_limit)
-    with tempfile.NamedTemporaryFile(suffix='.petabricks.cfg') as cfgtmp:
-      for k, v in self.build_config(desired_result.configuration.data).items():
-        print >> cfgtmp, k, '=', v
-      cfgtmp.flush()
-      if args.program_input:
-        input_opts = ['--iogen-run=' + args.program_input,
-                      '--iogen-n=%d' % input.input_class.size]
-      else:
-        input_opts = ['-n=%d' % input.input_class.size]
-
-      cmd = [args.program,
-             '--time',
-             '--accuracy',
-             '--max-sec=%.8f' % limit,
-             '--config=' + cfgtmp.name] + input_opts
-      log.debug("cmd: %s", ' '.join(cmd))
-      p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-      out, err = p.communicate()
-
-    result = opentuner.resultsdb.models.Result()
-    try:
-      root = etree.XML(out)
-      result.time = float(root.find('stats/timing').get('average'))
-      result.accuracy = float(root.find('stats/accuracy').get('average'))
-      if result.time < limit + 3600:
-        result.state = 'OK'
-      else:
-        #time will be 2**31 if timeout
-        result.state = 'TIMEOUT'
-    except:
-      log.warning("program crash, out = %s / err = %s", out, err)
-      result.state = 'ERROR'
-      result.time = float('inf')
-      result.accuracy = float('-inf')
-    return result
-
-  def save_final_config(self, configuration):
-    """
-    called at the end of autotuning with the best
-    resultsdb.models.Configuration
-    """
-    with open(args.program_cfg_output, 'w') as fd:
-      cfg = self.build_config(configuration.data)
-      for k, v in sorted(cfg.items()):
-        print >> fd, k, '=', v
-    log.info("final configuration written to %s", args.program_cfg_output)
-
-  def manipulator(self):
-    """create the configuration manipulator, from example config"""
-    upper_limit = self.program_settings['n'] + 1
-    cfg = open(self.args.program_cfg_default).read()
-    manipulator = ConfigurationManipulator()
-
-    self.choice_sites = dict()
-
-    for m in re.finditer(r" *([a-zA-Z0-9_-]+)[ =]+([0-9e.+-]+) *"
-                         r"[#] *([a-z]+).* ([0-9]+) to ([0-9]+)", cfg):
-      k, v, valtype, minval, maxval = m.group(1, 2, 3, 4, 5)
-      minval = float(minval)
-      maxval = float(maxval)
-      if upper_limit:
-        maxval = min(maxval, upper_limit)
-      assert valtype == 'int'
-      #log.debug("param %s %f %f", k, minval, maxval)
-
-      m1 = re.match(r'(.*)_lvl[0-9]+_rule', k)
-      m2 = re.match(r'(.*)_lvl[0-9]+_cutoff', k)
-      if m1:
-        self.choice_sites[m1.group(1)] = int(maxval)
-      elif m2:
-        pass
-      elif k == 'worker_threads':
-        manipulator.add_parameter(IntegerParameter(k, 1, 16))
-      elif k == 'distributedcutoff':
-        pass
-      elif minval == 0 and maxval < 64:
-        manipulator.add_parameter(SwitchParameter(k, maxval))
-      else:
-        manipulator.add_parameter(LogIntegerParameter(k, minval, maxval))
-
-    for name, choices in self.choice_sites.items():
-      manipulator.add_parameter(
-        SelectorParameter('.' + name, range(choices + 1),
-                          upper_limit / choices))
-
-    self.manipulator = manipulator
-    return manipulator
-
-  def test_config(self):
-    pprint(self.manipulator().random())
-
-
-if __name__ == '__main__':
-  args = parser.parse_args()
-  if not args.program_cfg_default:
-    args.program_cfg_default = args.program + '.cfg.default'
-  if not args.program_cfg_output:
-    args.program_cfg_output = args.program + '.cfg'
-  if not args.program_settings:
-    args.program_settings = args.program + '.settings'
-  if args.test_config:
-    PetaBricksInterface(args).test_config()
-  else:
-    PetaBricksInterface.main(args)
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/testwrapper.sh b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/testwrapper.sh
deleted file mode 100755
index 2b6a94e57a6b4205638dd0560da79482494ac20b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/petabricks/testwrapper.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-COUNT=50
-for Z in `seq $COUNT`
-do
-  for T in `./pbtuner.py --list-techniques $@`;
-  do
-    echo $Z/$COUNT $T
-    ./pbtuner.py --technique=$T $@
-  done
-done
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/api_example.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/api_example.py
deleted file mode 100755
index e87a8fffe1544714247b4435a3b5ed7d3f92eb03..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/api_example.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/python
-"""
-Examples usage of a Python API interface to opentuner.
-
-Unlike the other examples, this code lets the user control the main() of
-the program and calls into opentuner to get new configurations to test.
-"""
-
-import adddeps  # add opentuner to path in dev mode
-
-import opentuner
-from opentuner.api import TuningRunManager
-from opentuner.measurement.interface import DefaultMeasurementInterface
-from opentuner.resultsdb.models import Result
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import IntegerParameter
-import logging
-import argparse
-
-log = logging.getLogger(__name__)
-
-
-def test_func(cfg):
-  x = cfg['x']
-  y = (x - 10) * (x - 10)
-  log.debug("f({}) -> {}".format(x, y))
-  return y
-
-
-def main():
-    parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-    args = parser.parse_args()
-    manipulator = ConfigurationManipulator()
-    manipulator.add_parameter(IntegerParameter('x', -200, 200))
-    interface = DefaultMeasurementInterface(args=args,
-                                            manipulator=manipulator,
-                                            project_name='examples',
-                                            program_name='api_test',
-                                            program_version='0.1')
-    api = TuningRunManager(interface, args)
-    for x in xrange(500):
-        desired_result = api.get_next_desired_result()
-        if desired_result is None:
-          # The search space for this example is very small, so sometimes
-          # the techniques have trouble finding a config that hasn't already
-          # been tested.  Change this to a continue to make it try again.
-          break
-        cfg = desired_result.configuration.data
-        result = Result(time=test_func(cfg))
-        api.report_result(desired_result, result)
-
-    best_cfg = api.get_best_configuration()
-    api.finish()
-    print 'best x found was', best_cfg['x']
-
-if __name__ == '__main__':
-  main()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/multiple_tuning_runs.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/multiple_tuning_runs.py
deleted file mode 100755
index 5e0918e3afe49ce7a819f36312d770cdb73a5003..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/py_api/multiple_tuning_runs.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/python
-"""
-Examples usage of a Python API interface to opentuner.
-
-Unlike the other examples, this code lets the user control the main() of
-the program and calls into opentuner to get new configurations to test.
-
-This version runs multiple tuning runs at once in a single process.
-"""
-
-import adddeps  # add opentuner to path in dev mode
-
-import opentuner
-from opentuner.api import TuningRunManager
-from opentuner.measurement.interface import DefaultMeasurementInterface
-from opentuner.resultsdb.models import Result
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import IntegerParameter
-import logging
-import argparse
-
-log = logging.getLogger(__name__)
-
-
-def test_func1(cfg):
-  x = cfg['x']
-  y = (x - 10) * (x - 10)
-  log.debug("f({}) -> {}".format(x, y))
-  return y
-
-
-def test_func2(cfg):
-  x = cfg['x']
-  y = (x + 10) * (x + 10)
-  log.debug("f({}) -> {}".format(x, y))
-  return y
-
-
-def test_func3(cfg):
-  x = cfg['x']
-  y = (x + 20) * (x + 20)
-  log.debug("f({}) -> {}".format(x, y))
-  return y
-
-
-def create_test_tuning_run(db):
-  parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  args = parser.parse_args()
-  args.database = db
-  manipulator = ConfigurationManipulator()
-  manipulator.add_parameter(IntegerParameter('x', -200, 200))
-  interface = DefaultMeasurementInterface(args=args,
-                                          manipulator=manipulator,
-                                          project_name='examples',
-                                          program_name='api_test',
-                                          program_version='0.1')
-  api = TuningRunManager(interface, args)
-  return api
-
-
-def main():
-    apis = [create_test_tuning_run('sqlite:////tmp/a.db'),
-            create_test_tuning_run('sqlite:////tmp/b.db'),
-            create_test_tuning_run('sqlite:////tmp/c.db')]
-    test_funcs = [test_func1, test_func2, test_func3]
-    for x in xrange(100):
-      for api, test_func in zip(apis, test_funcs):
-        desired_result = api.get_next_desired_result()
-        if desired_result is None:
-          continue
-        cfg = desired_result.configuration.data
-        result = Result(time=test_func(cfg))
-        api.report_result(desired_result, result)
-
-    best_cfgs = [api.get_best_configuration() for api in apis]
-    for api in apis:
-      api.finish()
-
-    print('best x configs: {}'.format(best_cfgs))
-
-if __name__ == '__main__':
-  main()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/.gitignore b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/.gitignore
deleted file mode 100644
index aa0571caf15bdf4665fee72a1d87051d12718127..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-rosenbrock.db
-*.db
-opentuner.log
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.makefile b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.makefile
deleted file mode 100755
index 7b9be87a9a8ba698083ad4ac2c228ed3f11ed8df..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.makefile
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/make -f
-# use -j4 to run in parallel
-
-FN         := rosenbrock
-DIMS       := 4
-TECHNIQUES := $(shell ./rosenbrock.py --list-techniques)
-define test_loop
-DB="sqlite:///opentuner.db/$$RUN.db";     \
-for TEQ in $(TECHNIQUES); do          \
-	./rosenbrock.py --function=$(FN)    \
-									--technique=$$TEQ  \
-									--dimensions=$(DIMS)   \
-									--database=$$DB;       \
-done;
-endef
-
-default: run.1 run.2 run.3 run.4 run.5 run.6 run.7 run.8 run.9 run.10 run.11 \
-run.12 run.13 run.14 run.15 run.16 run.17 run.18 run.19 run.20 run.21 run.22 \
-run.23 run.24 run.25 run.26 run.27 run.28 run.29 run.30
-
-run.%:
-	RUN=$* $(test_loop)
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.py
deleted file mode 100755
index da426f239bdcdd945eca8630db0c96a8a60544d6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/rosenbrock/rosenbrock.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a simple testcase purely for testing the autotuner
-#
-# http://en.wikipedia.org/wiki/Rosenbrock_function
-#
-# Also supports some other test functions taken from:
-# http://en.wikipedia.org/wiki/Test_functions_for_optimization
-#
-
-import adddeps  # fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.measurement import MeasurementInterface
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import FloatParameter
-
-log = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('--dimensions', type=int, default=2,
-                    help='dimensions for the Rosenbrock function')
-parser.add_argument('--domain', type=float, default=1000,
-                    help='bound for variables in each dimension')
-parser.add_argument('--function', default='rosenbrock',
-                    choices=('rosenbrock', 'sphere', 'beale'),
-                    help='function to use')
-
-
-class Rosenbrock(MeasurementInterface):
-  def run(self, desired_result, input, limit):
-    cfg = desired_result.configuration.data
-    val = 0.0
-    if self.args.function == 'rosenbrock':
-      # the actual rosenbrock function:
-      for d in xrange(self.args.dimensions - 1):
-        x0 = cfg[d]
-        x1 = cfg[d + 1]
-        val += 100.0 * (x1 - x0 ** 2) ** 2 + (x0 - 1) ** 2
-    elif self.args.function == 'sphere':
-      for d in xrange(self.args.dimensions):
-        xi = cfg[d]
-        val += xi ** 2
-    elif self.args.function == 'beale':
-      assert self.args.dimensions == 2
-      assert self.args.domain == 4.5
-      x = cfg[0]
-      y = cfg[1]
-      val = ((1.5 - x + x * y) ** 2 +
-             (2.25 - x + x * y ** 2) ** 2 +
-             (2.625 - x + x * y ** 3) ** 2)
-    return opentuner.resultsdb.models.Result(time=val)
-
-  def manipulator(self):
-    manipulator = ConfigurationManipulator()
-    for d in xrange(self.args.dimensions):
-      manipulator.add_parameter(FloatParameter(d,
-                                               -self.args.domain,
-                                               self.args.domain))
-    return manipulator
-
-  def program_name(self):
-    return self.args.function
-
-  def program_version(self):
-    return "%dx%d" % (self.args.dimensions, self.args.domain)
-
-  def save_final_config(self, configuration):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-
-if __name__ == '__main__':
-  args = parser.parse_args()
-  if args.function == 'beale':
-    # fixed for this function
-    args.domain = 4.5
-    args.dimensions = 2
-  Rosenbrock.main(args)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/att48_d.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/att48_d.txt
deleted file mode 100644
index b93e36ccfa194c574fd9473921fcee2d6820015c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/att48_d.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-         0      4727      1205      6363      3657      3130      2414       563       463      5654      1713      1604      2368      2201      1290      1004      3833      2258      3419      2267      2957       720      1700      5279      2578      6076      3465      2654      3625      3115      1574      3951      1748      2142      6755      2383      3306      1029      3530       825      2188      4820      3489      1947      6835      1542      2379      3744
-      4727         0      3588      2012      1842      6977      6501      5187      5028      2327      4148      4723      3635      3125      4907      3930      7463      6338      7243      5105      4043      4022      3677      2863      3106      1850      7173      6630      1204      6814      6001      3447      5253      2656      3123      6274      7183      5622      3085      4564      2756      1591      7027      6186      3472      5461      4390      2088
-      1205      3588         0      5163      2458      3678      3071      1742      1444      4462      1184      1520      1498      1103      1501       951      4298      2903      3967      2169      2209       652       828      4136      1518      4873      3954      3254      2446      3581      2441      2960      1966       950      5564      2916      3878      2035      2482      1027      1395      3617      3891      2686      5661      2023      1867      2560
-      6363      2012      5163         0      2799      8064      7727      6878      6581      1402      5366      5946      4679      4378      6225      5709      8417      7578      8296      6135      4802      5707      4982      2322      4178       320      8186      7800      2778      7859      7408      3763      6461      4223      1427      7451      8263      7131      3669      6011      4638      1681      7987      7502      1877      6758      5360      2844
-      3657      1842      2458      2799         0      5330      4946      4200      3824      2012      2573      3157      1924      1580      3427      3179      5749      4793      5577      3409      2223      3066      2185      1860      1401      2491      5486      5035       894      5141      4611      1669      3677      1590      3113      4682      5533      4352      1252      3227      2426      1169      5313      4706      3241      3962      2651       304
-      3130      6977      3678      8064      5330         0       743      3209      2670      6929      2831      2266      3407      3854      2178      4076       727       881       293      1930      3310      3672      3315      6199      3932      7745       365       482      5774       261      1659      4513      1746      4431      7910       769       207      2225      4435      2681      5053      6384       550      1224      7805      1670      2704      5230
-      2414      6501      3071      7727      4946       743         0      2468      1952      6673      2380      1795      3051      3405      1604      3382      1469       168      1020      1681      3110      2993      2827      6009      3552      7412      1104       267      5300       821       916      4348      1270      3890      7698       332       900      1484      4185      2049      4415      6051      1219       482      7635      1054      2432      4884
-       563      5187      1742      6878      4200      3209      2468         0       718      6203      2241      2051      2920      2762      1687      1304      3932      2331      3487      2669      3487      1175      2260      5840      3141      6596      3563      2728      4120      3240      1559      4507      2082      2658      7304      2512      3364       985      4091      1319      2544      5358      3632      1987      7391      1785      2879      4296
-       463      5028      1444      6581      3824      2670      1952       718         0      5789      1602      1343      2330      2291       970      1451      3376      1796      2959      1951      2835      1112      1725      5346      2628      6285      3007      2193      3889      2661      1122      3920      1372      2391      6883      1927      2845       611      3543       676      2590      4993      3039      1486      6934      1112      2196      3876
-      5654      2327      4462      1402      2012      6929      6673      6203      5789         0      4392      4947      3648      3501      5274      5183      7216      6535      7140      5022      3621      5077      4090       922      3207      1131      7014      6714      2437      6707      6477      2476      5432      3599      1102      6376      7121      6284      2497      5160      4318       937      6795      6507      1268      5773      4249      1914
-      1713      4148      1184      5366      2573      2831      2380      2241      1602      4392         0       586       766      1029       883      2040      3353      2224      3100      1049      1246      1625       503      3841      1196      5054      3042      2488      2945      2676      2087      2331      1114      1650      5459      2132      3037      1958      1997       931      2513      3701      2923      2137      5459      1394       711      2534
-      1604      4723      1520      5946      3157      2266      1795      2051      1343      4947       586         0      1299      1612       406      2208      2824      1639      2542       694      1586      1767      1050      4357      1770      5633      2498      1907      3520      2128      1558      2778       531      2171      6003      1552      2472      1538      2506       791      2912      4277      2403      1564      5983       827       892      3109
-      2368      3635      1498      4679      1924      3407      3051      2920      2330      3648       766      1299         0       646      1642      2446      3840      2905      3655      1488       730      2096       697      3076       533      4363      3567      3122      2453      3219      2842      1592      1791      1480      4706      2772      3610      2721      1232      1656      2550      3001      3403      2860      4697      2126       756      1836
-      2201      3125      1103      4378      1580      3854      3405      2762      2291      3501      1029      1612       646         0      1853      2026      4349      3247      4119      1997      1341      1753       606      3078       419      4070      4052      3517      1923      3690      3032      1866      2142       838      4593      3161      4060      2788      1380      1663      1932      2736      3915      3138      4647      2395      1351      1592
-      1290      4907      1501      6225      3427      2178      1604      1687       970      5274       883       406      1642      1853         0      2029      2803      1438      2466       986      1987      1593      1253      4716      2072      5915      2454      1764      3710      2082      1204      3164       497      2287      6342      1419      2379      1134      2867       554      2885      4569      2405      1289      6338       555      1297      3406
-      1004      3930       951      5709      3179      4076      3382      1304      1451      5183      2040      2208      2446      2026      2029         0      4759      3220      4368      2900      3151       442      1765      4960      2444      5443      4396      3610      2932      4034      2572      3891      2525      1590      6278      3313      4261      2033      3398      1476      1241      4287      4390      2928      6419      2428      2749      3337
-      3833      7463      4298      8417      5749       727      1469      3932      3376      7216      3353      2824      3840      4349      2803      4759         0      1601       477      2359      3617      4345      3851      6433      4372      8098       370      1206      6267       726      2384      4754      2335      4991      8148      1452       609      2949      4752      3331      5687      6746       437      1948      8005      2334      3098      5618
-      2258      6338      2903      7578      4793       881       168      2331      1796      6535      2224      1639      2905      3247      1438      3220      1601         0      1165      1563      2988      2829      2666      5882      3401      7263      1233       399      5138       923       794      4227      1117      3724      7565       286      1049      1348      4051      1881      4248      5903      1322       355      7508       887      2302      4736
-      3419      7243      3967      8296      5577       293      1020      3487      2959      7140      3100      2542      3655      4119      2466      4368       477      1165         0      2170      3520      3965      3588      6393      4183      7977       202       767      6041       438      1932      4706      2027      4711      8107      1061       132      2503      4652      2972      5344      6617       486      1501      7989      1962      2939      5469
-      2267      5105      2169      6135      3409      1930      1681      2669      1951      5022      1049       694      1488      1997       986      2900      2359      1563      2170         0      1430      2460      1547      4333      2019      5817      2079      1694      3910      1733      1813      2668       654      2694      6029      1366      2130      1991      2525      1474      3542      4455      1923      1641      5957      1071       777      3302
-      2957      4043      2209      4802      2223      3310      3110      3487      2835      3621      1246      1586       730      1341      1987      3151      3617      2988      3520      1430         0      2779      1387      2905      1062      4482      3398      3119      2922      3087      3115      1240      1953      2175      4607      2796      3501      3119      1136      2173      3268      3136      3189      3029      4527      2355       711      2042
-       720      4022       652      5707      3066      3672      2993      1175      1112      5077      1625      1767      2096      1753      1593       442      4345      2829      3965      2460      2779         0      1401      4781      2166      5427      3984      3212      2946      3620      2224      3603      2089      1496      6178      2906      3861      1719      3132      1040      1479      4211      3969      2553      6290      2012      2336      3189
-      1700      3677       828      4982      2185      3315      2827      2260      1725      4090       503      1050       697       606      1253      1765      3851      2666      3588      1547      1387      1401         0      3621       903      4675      3537      2954      2475      3169      2427      2254      1578      1148      5177      2598      3521      2194      1833      1074      2054      3340      3423      2541      5213      1801      1077      2190
-      5279      2863      4136      2322      1860      6199      6009      5840      5346       922      3841      4357      3076      3078      4716      4960      6433      5882      6393      4333      2905      4781      3621         0      2718      2042      6254      6024      2569      5966      5913      1687      4807      3384      1716      5699      6384      5787      1852      4687      4285      1272      6022      5892      1629      5178      3581      1639
-      2578      3106      1518      4178      1401      3932      3552      3141      2628      3207      1196      1770       533       419      2072      2444      4372      3401      4183      2019      1062      2166       903      2718         0      3864      4097      3635      1932      3748      3274      1448      2284      1164      4286      3283      4136      3086       967      1973      2285      2507      3935      3331      4312      2589      1284      1340
-      6076      1850      4873       320      2491      7745      7412      6596      6285      1131      5054      5633      4363      4070      5915      5443      8098      7263      7977      5817      4482      5427      4675      2042      3864         0      7866      7483      2515      7539      7101      3449      6146      3938      1375      7134      7944      6831      3349      5709      4397      1363      7667      7190      1798      6446      5041      2528
-      3465      7173      3954      8186      5486       365      1104      3563      3007      7014      3042      2498      3567      4052      2454      4396       370      1233       202      2079      3398      3984      3537      6254      4097      7866         0       839      5973       374      2019      4569      1996      4669      7970      1085       305      2581      4532      2976      5339      6509       287      1581      7844      1974      2838      5369
-      2654      6630      3254      7800      5035       482       267      2728      2193      6714      2488      1907      3122      3517      1764      3610      1206       399       767      1694      3119      3212      2954      6024      3635      7483       839         0      5427       558      1181      4349      1377      4044      7723       356       653      1744      4218      2241      4614      6121       955       743      7644      1231      2465      4957
-      3625      1204      2446      2778       894      5774      5300      4120      3889      2437      2945      3520      2453      1923      3710      2932      6267      5138      6041      3910      2922      2946      2475      2569      1932      2515      5973      5427         0      5612      4824      2550      4050      1498      3476      5071      5980      4470      2096      3388      1911      1501      5831      4994      3704      4264      3209      1196
-      3115      6814      3581      7859      5141       261       821      3240      2661      6707      2676      2128      3219      3690      2082      4034       726       923       438      1733      3087      3620      3169      5966      3748      7539       374       558      5612         0      1716      4280      1624      4298      7679       735       420      2263      4216      2606      4967      6179       400      1277      7567      1609      2501      5032
-      1574      6001      2441      7408      4611      1659       916      1559      1122      6477      2087      1558      2842      3032      1204      2572      2384       794      1932      1813      3115      2224      2427      5913      3274      7101      2019      1181      4824      1716         0      4330      1180      3346      7545      1023      1808       578      4062      1438      3693      5763      2115       440      7537       763      2404      4603
-      3951      3447      2960      3763      1669      4513      4348      4507      3920      2476      2331      2778      1592      1866      3164      3891      4754      4227      4706      2668      1240      3603      2254      1687      1448      3449      4569      4349      2550      4280      4330         0      3184      2510      3402      4031      4698      4281       533      3245      3612      2187      4339      4265      3296      3576      1941      1381
-      1748      5253      1966      6461      3677      1746      1270      2082      1372      5432      1114       531      1791      2142       497      2525      2335      1117      2027       654      1953      2089      1578      4807      2284      6146      1996      1377      4050      1624      1180      3184         0      2685      6475      1022      1952      1341      2963      1050      3358      4787      1926      1086      6436       422      1244      3619
-      2142      2656       950      4223      1590      4431      3890      2658      2391      3599      1650      2171      1480       838      2287      1590      4991      3724      4711      2694      2175      1496      1148      3384      1164      3938      4669      4044      1498      4298      3346      2510      2685         0      4697      3693      4636      2975      1981      1909      1124      2718      4565      3548      4830      2839      2140      1751
-      6755      3123      5564      1427      3113      7910      7698      7304      6883      1102      5459      6003      4706      4593      6342      6278      8148      7565      8107      6029      4607      6178      5177      1716      4286      1375      7970      7723      3476      7679      7545      3402      6475      4697         0      7393      8097      7370      3515      6249      5379      2001      7738      7556       461      6829      5267      3013
-      2383      6274      2916      7451      4682       769       332      2512      1927      6376      2132      1552      2772      3161      1419      3313      1452       286      1061      1366      2796      2906      2598      5699      3283      7134      1085       356      5071       735      1023      4031      1022      3693      7393         0       965      1542      3883      1913      4286      5772      1121       600      7322       902      2128      4608
-      3306      7183      3878      8263      5533       207       900      3364      2845      7121      3037      2472      3610      4060      2379      4261       609      1049       132      2130      3501      3861      3521      6384      4136      7944       305       653      5980       420      1808      4698      1952      4636      8097       965         0      2380      4629      2877      5250      6583       570      1380      7986      1866      2904      5432
-      1029      5622      2035      7131      4352      2225      1484       985       611      6284      1958      1538      2721      2788      1134      2033      2949      1348      2503      1991      3119      1719      2194      5787      3086      6831      2581      1744      4470      2263       578      4281      1341      2975      7370      1542      2380         0      3952      1127      3197      5518      2658      1002      7395       951      2429      4380
-      3530      3085      2482      3669      1252      4435      4185      4091      3543      2497      1997      2506      1232      1380      2867      3398      4752      4051      4652      2525      1136      3132      1833      1852       967      3349      4532      4218      2096      4216      4062       533      2963      1981      3515      3883      4629      3952         0      2873      3080      2012      4324      4046      3478      3328      1755      1000
-       825      4564      1027      6011      3227      2681      2049      1319       676      5160       931       791      1656      1663       554      1476      3331      1881      2972      1474      2173      1040      1074      4687      1973      5709      2976      2241      3388      2606      1438      3245      1050      1909      6249      1913      2877      1127      2873         0      2374      4392      2943      1659      6285      1012      1563      3254
-      2188      2756      1395      4638      2426      5053      4415      2544      2590      4318      2513      2912      2550      1932      2885      1241      5687      4248      5344      3542      3268      1479      2054      4285      2285      4397      5339      4614      1911      4967      3693      3612      3358      1124      5379      4286      5250      3197      3080      2374         0      3386      5284      3997      5585      3386      3125      2664
-      4820      1591      3617      1681      1169      6384      6051      5358      4993       937      3701      4277      3001      2736      4569      4287      6746      5903      6617      4455      3136      4211      3340      1272      2507      1363      6509      6121      1501      6179      5763      2187      4787      2718      2001      5772      6583      5518      2012      4392      3386         0      6314      5837      2205      5095      3680      1169
-      3489      7027      3891      7987      5313       550      1219      3632      3039      6795      2923      2403      3403      3915      2405      4390       437      1322       486      1923      3189      3969      3423      6022      3935      7667       287       955      5831       400      2115      4339      1926      4565      7738      1121       570      2658      4324      2943      5284      6314         0      1676      7603      1964      2662      5184
-      1947      6186      2686      7502      4706      1224       482      1987      1486      6507      2137      1564      2860      3138      1289      2928      1948       355      1501      1641      3029      2553      2541      5892      3331      7190      1581       743      4994      1277       440      4265      1086      3548      7556       600      1380      1002      4046      1659      3997      5837      1676         0      7521       744      2325      4670
-      6835      3472      5661      1877      3241      7805      7635      7391      6934      1268      5459      5983      4697      4647      6338      6419      8005      7508      7989      5957      4527      6290      5213      1629      4312      1798      7844      7644      3704      7567      7537      3296      6436      4830       461      7322      7986      7395      3478      6285      5585      2205      7603      7521         0      6805      5208      3102
-      1542      5461      2023      6758      3962      1670      1054      1785      1112      5773      1394       827      2126      2395       555      2428      2334       887      1962      1071      2355      2012      1801      5178      2589      6446      1974      1231      4264      1609       763      3576       422      2839      6829       902      1866       951      3328      1012      3386      5095      1964       744      6805         0      1644      3928
-      2379      4390      1867      5360      2651      2704      2432      2879      2196      4249       711       892       756      1351      1297      2749      3098      2302      2939       777       711      2336      1077      3581      1284      5041      2838      2465      3209      2501      2404      1941      1244      2140      5267      2128      2904      2429      1755      1563      3125      3680      2662      2325      5208      1644         0      2532
-      3744      2088      2560      2844       304      5230      4884      4296      3876      1914      2534      3109      1836      1592      3406      3337      5618      4736      5469      3302      2042      3189      2190      1639      1340      2528      5369      4957      1196      5032      4603      1381      3619      1751      3013      4608      5432      4380      1000      3254      2664      1169      5184      4670      3102      3928      2532         0
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_d.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_d.txt
deleted file mode 100644
index 0464ad3143b4dff3176414a0b343f762ae5379b7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_d.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-         0        29        82        46        68        52        72        42        51        55        29        74        23        72        46
-        29         0        55        46        42        43        43        23        23        31        41        51        11        52        21
-        82        55         0        68        46        55        23        43        41        29        79        21        64        31        51
-        46        46        68         0        82        15        72        31        62        42        21        51        51        43        64
-        68        42        46        82         0        74        23        52        21        46        82        58        46        65        23
-        52        43        55        15        74         0        61        23        55        31        33        37        51        29        59
-        72        43        23        72        23        61         0        42        23        31        77        37        51        46        33
-        42        23        43        31        52        23        42         0        33        15        37        33        33        31        37
-        51        23        41        62        21        55        23        33         0        29        62        46        29        51        11
-        55        31        29        42        46        31        31        15        29         0        51        21        41        23        37
-        29        41        79        21        82        33        77        37        62        51         0        65        42        59        61
-        74        51        21        51        58        37        37        33        46        21        65         0        61        11        55
-        23        11        64        51        46        51        51        33        29        41        42        61         0        62        23
-        72        52        31        43        65        29        46        31        51        23        59        11        62         0        59
-        46        21        51        64        23        59        33        37        11        37        61        55        23        59         0
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_s.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_s.txt
deleted file mode 100644
index 38afab553d2a9c23c1abda12a95f6367d5d093e2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/p01_s.txt
+++ /dev/null
@@ -1,16 +0,0 @@
- 1
-13
- 2
-15
- 9
- 5
- 7
- 3
-12
-14
-10
- 8
- 6
- 4
-11
- 1
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/tsp.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/tsp.py
deleted file mode 100755
index 0ddff5156497331daffef9b7385a20d63423bbd0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tsp/tsp.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a simple testcase purely for testing the autotuner on permutations
-#
-# http://en.wikipedia.org/wiki/Travelling_salesman_problem
-#
-
-import adddeps #fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
-                                          PermutationParameter)
-from opentuner.search.objective import MinimizeTime
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('data', help='distance matrix file')
-
-class TSP(MeasurementInterface):
-    def __init__(self, args):
-        super(TSP, self).__init__(args)
-        data = args.data
-        m = open(data).readlines()
-        self.distance = [[int(i) for i in l.split()] for l in m]
-
-    def run(self, desired_result, input, limit):
-        cfg = desired_result.configuration.data
-        p = cfg[0]      # cheating: should use manipulator function
-        t = self.eval_path(p)
-        return opentuner.resultsdb.models.Result(time=t)
-
-    def eval_path(self, p):
-        """ Given permutation of cities as a list of indices,
-        return total path length """
-        out = sum(self.distance[p[i]][p[i+1]] for i in range(len(p)-1))
-##        print out, p
-        return out
-
-    def manipulator(self):
-        manipulator = ConfigurationManipulator()
-        manipulator.add_parameter(PermutationParameter(0, range(len(self.distance))))
-        return manipulator
-
-    def solution(self):
-        p = [1,13,2,15,9,5,7,3,12,14,10,8,6,4,11]
-        return self.eval_path(p)
-
-
-
-if __name__ == '__main__':
-  args = parser.parse_args()
-  TSP.main(args)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/#accuracy_tuner.py# b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/#accuracy_tuner.py#
deleted file mode 100644
index 2110d0d692831e37f30023af05b92a0d91d9623c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/#accuracy_tuner.py#
+++ /dev/null
@@ -1,203 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-  
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-    
-
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-    """
-    Compile and run a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    run_result_call_program = self.call_program(run_cmd)
-    #print run_result_call_program
-
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-    
-    return Result
-         
-
-  def save_final_config(self, configuration):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir  
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner.py
deleted file mode 100644
index 5977fe7ee5b4780139d2c5a865c8231361cf0f2c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner.py
+++ /dev/null
@@ -1,198 +0,0 @@
-#!/usr/bin/env python
-#
-
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-  
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-    """
-    Compile and run a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    run_result_call_program = self.call_program(run_cmd)
-    #print run_result_call_program
-
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-    
-    return Result
-         
-
-  def save_final_config(self, configuration):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir  
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner_piped.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner_piped.py
deleted file mode 100644
index 6d46c5762ead377292337c47d045ee5e58322954..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/accuracy_tuner_piped.py
+++ /dev/null
@@ -1,269 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def extractTotalOverhead(file_name):
-
-  total_comps = 0.0
-  file = open(file_name, "r")
-  for x in file:
-    words = x.split()
-    total_comps += float(words[opt_confs_index])
-  
-  print total_comps 
-  return total_comps
-
-
-def getAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  acc_str = file.read()
-  file.close()
-
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-    
-  print accuracy
-  return accuracy
-
-
-
-def kill(proc_pid):
-  process = psutil.Process(proc_pid)
-  for proc in process.children(recursive=True):
-    proc.kill()
-  process.kill()
-    
-
-
-def createFlagsFile(file_name, cfg):
-
-  f = open(file_name, "w+")
-  cmd_config = ""
-  for flag in tuning_flags:
-    flag_value = cfg[flag]
-    cmd_config += str(flag_value) + "\n"
-    
-  f.write(cmd_config)
-  f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
-  def __init__(self, args):
-    objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
-    input_manager = FixedInputManager(size=num_flags)
-    self.configs_list = []
-
-    super(ClangFlagsTuner, self).__init__(
-        args, program_name=args.binary,
-        program_version=self.file_hash(args.binary),
-        input_manager=input_manager, objective=objective)
-
-
-    FNULL = open(os.devnull, 'wb')
-    #run_result_call_program = self.call_program(run_cmd)
-    self.start_process = subprocess.Popen([binary_name, "opentuner_run"]) #,  stdout=FNULL);
-
-    try:
-      os.mkfifo("/tmp/myfifo")
-    except OSError, e:
-      print("FIFO exists")
-
-    
-
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag, flag_ranges
-                      # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-                      )) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  
-  def run(self, desired_result, input, limit):
-
-    """
-    Run  a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    
-    # NOTE: creates the file with flags read by the runtime
-    createFlagsFile("opentuner_flags", cfg)
-    
-    run_cmd = binary_name
-    print run_cmd
-    #run_result_call_program = self.call_program(run_cmd)      
-  
-    # Using Named Pipes to signal execution to the DNN outer thread
-    fifo = open("/tmp/myfifo", "w")
-    fifo.write("start_run")
-    fifo.close()
-
-    print "Waiting for process to signal back - when done processing one run"
-
-    fifo2 = open("/tmp/myfifo", "r")
-    fifo2.read()
-    fifo2.close()
-
-    print "Process Signalled back"
-
-    total_comps = extractTotalOverhead("accuracy_summary")
-    accuracy = getAccuracy("final_accuracy")
-
-    
-    #Result = opentuner.resultsdb.models.Result(time=total_comps)
-    Result = opentuner.resultsdb.models.Result()
-    Result.time = total_comps
-    Result.accuracy = accuracy
-
-    if accuracy > accuracy_threshold:
-      if accuracy not in evaluated_configs:
-        config_tuple = (total_comps, accuracy, cfg)
-        self.configs_list.append(config_tuple)
-        evaluated_configs[accuracy] = 1
-        shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-        
-    print "done with one run"
-    
-    return Result
-
-
-  def save_final_config(self, configuration):
-
-    print "Dumping High Confidence results"
-    sleep(5)
-    
-    # Only dumping files with 95% confidence
-    dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
-    select_top_results(orig_result_dir + "/high_confidence")
-
-    
-    #self.start_process.kill()
-    kill(self.start_process.pid)
-    
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    print "Final configuration", configuration.data
-
-    return
-
-    
-    if not os.path.exists(result_dir):
-      os.mkdir(result_dir)
-    
-    createFlagsFile("opentuner_flags", configuration.data)
-    run_cmd = binary_name
-    run_result_call_program = self.call_program(run_cmd)
-
-    accuracy = getAccuracy("final_accuracy")
-    shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
-    sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
-    print sorted_list[0:10]
-    
-    top_elems = 20
-    if len(sorted_list) < top_elems:
-      top_elems = len(sorted_list)
-
-      
-    for i in range(top_elems):
-      createFlagsFile("opentuner_flags", sorted_list[i][2])
-      run_cmd = binary_name
-      run_result_call_program = self.call_program(run_cmd)
-      accuracy = getAccuracy("final_accuracy")
-      shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
-    #os.mkdir(result_dir + "full_results")
-  
-    
-
-
-if __name__ == '__main__':
-
-  argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-  argparser.add_argument('--binary', help='name of binary to run')
-  argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
-  argparser.add_argument('--error-range', type=int, help='num of flags to tune for') 
-  argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
-  argparser.add_argument('--result-dir', help='accuracy threshold')
-
-  
-  args = argparser.parse_args()
-  binary_name = str(args.binary)
-  print("binary_name = ", binary_name)
-  num_flags = int(args.num_flags)
-  error_range = int(args.error_range)
-  accuracy_threshold = float(args.accuracy)
-  print("accuracy = ", accuracy_threshold)
-  result_dir = args.result_dir
-  orig_result_dir = result_dir
-  if result_dir == "":
-    print("Provide --result-dir ")
-
-
-  output_dir = result_dir + "/full_results"
-  print output_dir
-  if not os.path.exists(result_dir):
-    os.mkdir(result_dir)
-    
-  if not os.path.exists(output_dir):
-    print("Creating output directory = ", output_dir)
-    os.mkdir(output_dir)
-
-  for j in range(error_range):
-    flag_ranges.append(j)
-
-  print("flag_ranges = ", flag_ranges)
-  
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-  
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/adddeps.py
deleted file mode 100644
index 72de04cf55e138a5ee5d0fdaf11da4b692045706..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/adddeps.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/gettingstarted.md b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/gettingstarted.md
deleted file mode 100644
index 8a442c5f44d6c501f686125d4468ca642f745920..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/gettingstarted.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-layout: default
-title: OpenTuner - Using OpenTuner
-permalink: /tutorial/gettingstarted/index.html
----
-
-Tutorial: Optimizing Block Matrix Multiplication
-================================================
-
-This tutorial assumes that you have checked out a copy of opentuner. For
-guidelines on how to get opentuner set up, refer [here][setup].
-
-[setup]: http://opentuner.org/tutorial/setup/
-
-Identifying a Program to Autotune
----------------------------------
-
-In order to do autotuning, you first need something to autotune. This will
-normally be your own program that you want to make either fast or better in
-some way.  For this tutorial we will use a blocked version of matrix multiply
-as an example. We will use opentuner to find the optimal value of the block
-size parameter.
-
-We will autotune the sample code below(based off of modification of code
-found [here][matrix-multiply-code]), making sure to take the block size as
-a compile time constant to the program.
-
-[matrix-multiply-code]: http://csapp.cs.cmu.edu/public/waside/waside-blocking.pdf
-
-Save the sample code below to examples/tutorials/mmm_block.cpp
-
-    #include <stdio.h>
-    #include <cstdlib>
-
-    #define N 100
-    
-    int main(int argc, const char** argv)
-    {
-    
-      int n = BLOCK_SIZE * (N/BLOCK_SIZE);
-      int a[N][N];
-      int b[N][N];
-      int c[N][N];
-      int sum=0;
-      for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-      {
-          for(int j1=0;j1<n;j1+=BLOCK_SIZE)
-          {
-              for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-              {
-                  for(int i=0;i<n;i++)
-                  {
-                      for(int j=j1;j<j1+BLOCK_SIZE;j++)
-                      {
-                          sum = c[i][j];
-                          for(int k=k1;k<k1+BLOCK_SIZE;k++)
-                          {
-                              sum += a[i][k] * b[k][j];
-                          }
-                          c[i][j] = sum;
-                      }
-                  }
-              }
-          }
-             }
-      return 0;
-    }
-
-Creating a New Autotuner with Opentuner
-------------------------------------
-Now we need to create a program that uses OpenTuner to optimize the program we just saved.
-
-Save the following code to examples/tutorials/mmm_tuner.py
-
-    #!/usr/bin/env python
-    #
-    # Optimize blocksize of apps/mmm_block.cpp
-    #
-    # This is an extremely simplified version meant only for tutorials
-    #
-    import adddeps  # fix sys.path
-
-    import opentuner
-    from opentuner import ConfigurationManipulator
-    from opentuner import IntegerParameter
-    from opentuner import MeasurementInterface
-    from opentuner import Result
-
-
-    class GccFlagsTuner(MeasurementInterface):
-
-      def manipulator(self):
-        """
-        Define the search space by creating a
-        ConfigurationManipulator
-        """
-        manipulator = ConfigurationManipulator()
-        manipulator.add_parameter(
-          IntegerParameter('blockSize', 1, 10))
-        return manipulator
-
-      def run(self, desired_result, input, limit):
-        """
-        Compile and run a given configuration then
-        return performance
-        """
-        cfg = desired_result.configuration.data
-
-        gcc_cmd = 'g++ mmm_block.cpp '
-        gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
-        gcc_cmd += ' -o ./tmp.bin'
-
-        compile_result = self.call_program(gcc_cmd)
-        assert compile_result['returncode'] == 0
-
-        run_cmd = './tmp.bin'
-
-        run_result = self.call_program(run_cmd)
-        assert run_result['returncode'] == 0
-
-        return Result(time=run_result['time'])
-
-      def save_final_config(self, configuration):
-        """called at the end of tuning"""
-        print "Optimal block size written to mmm_final_config.json:", configuration.data
-        self.manipulator().save_to_file(configuration.data,
-                                        'mmm_final_config.json')
-
-
-    if __name__ == '__main__':
-      argparser = opentuner.default_argparser()
-      GccFlagsTuner.main(argparser.parse_args())
-
-
-This file consists of several components, each of which will be discussed in further detail below.
-
-Tuning Programs have a general structure as follows:
-
-    from opentuner import MeasurementInterface
-    from opentuner import Result
-
-Create an instance of class GccFlagsTuner, which tunes specified parameters using opentuner.
-    class GccFlagsTuner(MeasurementInterface):
-
-The manipulator method defines the variable search space by specifying parameters that should be tuned by this instance of GccFlagsTuner
-
-    def manipulator(self):
-      """
-      Define the search space by creating a
-      ConfigurationManipulator
-      """
-      manipulator = ConfigurationManipulator()
-      manipulator.add_parameter(
-        IntegerParameter('blockSize', 1, 10))
-      return manipulator
-
-The run method actually runs opentuner under the given configuration and returns the calculated performance under this configuration. In this example, the blockSize parameter to be tuned is input as a compile-time constant that takes on a value within the specified range each time it is run. However, opentuner also supports other methods of specifying these parameters that may be preferred in different use cases.
-
-    def run(self, desired_result, input, limit):
-      """
-      Compile and run a given configuration then
-      return performance
-      """
-      cfg = desired_result.configuration.data
-
-      gcc_cmd = 'g++ mmm_block.cpp '
-      gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
-      gcc_cmd += ' -o ./tmp.bin'
-
-      compile_result = self.call_program(gcc_cmd)
-      assert compile_result['returncode'] == 0
-
-      run_cmd = './tmp.bin'
-
-      run_result = self.call_program(run_cmd)
-      assert run_result['returncode'] == 0
-
-      return Result(time=run_result['time'])
-
-We can actually display the result of running opentuner(the optimal block size for our multiplication problem) by creating a method, save_final_config() in our class. This saves a json dictionary of the optimal blockSize parameter found to the file mmm_final_config.json
-
-    def save_final_config(self, configuration):
-      """called at the end of tuning"""
-      print "Optimal block size written to mmm_final_config.json:", configuration.data
-      self.manipulator().save_to_file(configuration.data,
-                                      'mmm_final_config.json')
-
-    if __name__ == '__main__':
-      argparser = opentuner.default_argparser()
-      GccFlagsTuner.main(argparser.parse_args())
-
-Generating and Viewing Results
-------------------------------
-
-Run the following command to autotune our program(The --no-dups flag hides warnings about duplicate results and the --stop-after parameter specifies that we are running opentuner for a maximum of 30 seconds):
-
-    python mmm_tuner.py --no-dups --stop-after=30
-
-The results of each run configuration will be displayed as follows(output lines are truncated for readability here):
-
-    [    10s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    19s]    INFO opentuner.search.metatechniques: AUCBanditMetaTechniqueA: [('DifferentialEvolutionAlt', 477), ('UniformGreedyMutation', 18), ('NormalGreedyMutation', 5), ('RandomNelderMead', 1)]
-    [    20s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    30s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    [    30s]    INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
-    Optimal block size written to mmm_final_config.json: {'BLOCK_SIZE': 4}
-
-
-Look up the optimal BlockSize value by inspecting the following created file:
-
-    mmm_final_config.json
-
-In this example, the output file content was as follows:
-
-    {'BLOCK_SIZE': 4}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/measure_confidence.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/measure_confidence.py
deleted file mode 100644
index 655bdb024f72f0fd47807b5aa2696f9fb89b40e6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/measure_confidence.py
+++ /dev/null
@@ -1,164 +0,0 @@
-
-import argparse
-import os
-import sys
-
-
-def getAccuracy(file_name):
-
-  if not os.path.exists(file_name):
-    print("final_accuracy file not found ")
-    sys.exit(0)
-    
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-
-
-total_runs = 100.0
-skip_lines = 0
-
-
-def test_func():
-  print "test_func"
-  sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
-  #total_runs = 100.0
-  successful_runs = 0.0
-  total_acc = 0
-
-  for i in range(int(total_runs)):
-
-    fifo = open("/tmp/myfifo", "w")
-    fifo.write("start_run")
-    fifo.close()
-
-    print "Waiting for process to signal back - when done processing one run"
-
-    fifo2 = open("/tmp/myfifo", "r")
-    fifo2.read()
-    fifo2.close()
-
-    print "Process Signalled back"
-
-    accuracy = getAccuracy("final_accuracy")
-    total_acc += accuracy
-
-    if accuracy > accuracy_threshold:
-      successful_runs += 1
-
-  confidence = (successful_runs / total_runs) * 100.0    
-  print("confidence = ", confidence)    
-  avg_acc = total_acc / total_runs
-  print("average accuracy = ", avg_acc)
-
-  return confidence, avg_acc
-  
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("opentuner_flags", "w+")
-
-    index = 0
-    results_str = ""
-    for x in f:
-      if index >= skip_lines:
-        error_knob = int(float(x.split()[1]))
-        print error_knob
-        tuner_file.write(str(error_knob) + "\n")
-
-      results_str += x
-      index += 1
-      
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
-    if run_confidence > 90:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(results_str)
-      f2.close()
-
-    conf_result = (run_confidence, avg_accuracy, file_name)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
-  #result_dir = args.result_dir
-  output_dir = result_dir + "/high_confidence"
-  result_dir = result_dir + "/full_results"
-
-  if not os.path.exists(output_dir):
-    os.mkdir(output_dir)
-
-    
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  print  "Dumped Confidence Summary"
-  
-
-  
-
-
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-  argparser.add_argument('--output-dir', help='Directory for storing output directory')
-  argparser.add_argument('--binary', help='Binary name to run')
-  argparser.add_argument('--accuracy', type=float,  help='Accuracy constraint')
-  argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-  
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-  output_dir = args.output_dir
-  binary = args.binary
-  accuracy = args.accuracy
-  confidence = args.confidence
-
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  #print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_block.cpp b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_block.cpp
deleted file mode 100755
index 0bb76845f8d6653d1c90a0a5b387e75c46e18233..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_block.cpp
+++ /dev/null
@@ -1,36 +0,0 @@
-#include <stdio.h>
-#include <cstdlib>
-
-#define N 100
-
-int main(int argc, const char** argv)
-{
-
-  int n = BLOCK_SIZE * (N/BLOCK_SIZE);
-  int a[N][N];
-  int b[N][N];
-  int c[N][N];
-  int sum=0;
-  for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-  {
-      for(int j1=0;j1<n;j1+=BLOCK_SIZE)
-      {
-          for(int k1=0;k1<n;k1+=BLOCK_SIZE)
-          {
-              for(int i=0;i<n;i++)
-              {
-                  for(int j=j1;j<j1+BLOCK_SIZE;j++)
-                  {
-                      sum = c[i][j];
-                      for(int k=k1;k<k1+BLOCK_SIZE;k++)
-                      {               
-                          sum += a[i][k] * b[k][j];
-                      }
-                      c[i][j] = sum;
-                  }
-              }
-          }
-      }
-         }
-  return 0;
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_tuner.py
deleted file mode 100644
index f92c4c3bfc9640514e4879b1e46480613015c207..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/mmm_tuner.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps  # fix sys.path
-
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-import os
-import sys
-
-
-tuning_flags = []
-
-binary_name = ""
-
-
-class ClangFlagsTuner(MeasurementInterface):
-  def manipulator(self):
-    """
-    Define the search space by creating a
-    ConfigurationManipulator
-    """
-    manipulator = ConfigurationManipulator()
-    for flag in tuning_flags:
-      manipulator.add_parameter(
-        EnumParameter(flag,
-                      [0, 1, 2, 3, 4, 5, 6])) #default is needed, optimizations don't work without it(tried and tested)
-    return manipulator
-
-  def compile(self, cfg, id):
-    """
-    Compile a given configuration in parallel
-    """
-    cmd_config = ""
-    for flag in tuning_flags:
-      flag_value = cfg[flag]
-      cmd_config += " " + flag_value 
-
-    run_cmd = binary_name + cmd_config    
-    return self.call_program(run_cmd)
-
-  def run_precompiled(self, desired_result, input, limit, compile_result, id):
-    """
-    Run a compile_result from compile() sequentially and return performance
-    """
-    run_result_call_program = self.call_program(binary_filename.format(id))
-    run_result_getFileSize = self.getFileSize(output_filename)
-    self.store_size_list(run_result_getFileSize)
-    return Result(size=run_result_getFileSize['binary_size'],time=run_result_call_program['time'])
-
-  def run(self, desired_result, input, limit):
-    """
-    Compile and run a given configuration then
-    return performance
-    """
-    cfg = desired_result.configuration.data
-    self.store_config_list(cfg)
-    compile_result = self.compile(cfg, 0)
-    return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
-  list_size = [] # list of file sizes
-  list_config = [] #list of configurations
-  list_size_config = [] #list of file size with corresponding optimization
-  list_N_size_config=[]
-
-  def store_size_list(self, binary_size):
-    """stores file size in a list"""
-    self.list_size.append(binary_size)
-
-  def store_config_list(self,cfg):
-    """stores configurations in a list"""
-    self.list_config.append(cfg)
-
-  counter = 0
-  def save_final_config(self,configuration):
-    """saves list of file size with corresponding optimization in a file"""
-    for list in self.list_size:
-      dict_size_config = {self.list_size[self.counter]['binary_size']: self.list_config[self.counter]}
-      self.list_size_config.append(dict_size_config)
-      self.list_size_config.sort()
-      self.counter += 1
-    self.extract_topN_resuls(10)
-    print "ALL file sizes along with corresponding configurations writtent to size_config.json"
-    self.manipulator().save_to_file(self.list_size_config,
-                                      'size_config.json')
-
-  def extract_topN_resuls(self,N):
-    """extracts top N results w.r.t size,N currently set to 10"""
-    counter=0
-    for list in self.list_size_config:
-      if counter < N:
-        self.list_N_size_config.append(list)
-    print "Top "+str(N)+" file sizes along with corresponding configurations writtent to TopN_size_config.json"
-    self.manipulator().save_to_file(self.list_size_config,
-                                    'TopN_size_config.json')
-
-
-
-if __name__ == '__main__':
-
-  binary_name = sys.argv[1]
-  num_flags = int(sys.argv[2])
-
-  for i in range(num_flags):
-    tuning_flags.append("flag" + str(i))
-
-  print tuning_flags  
-    
-  argparser = opentuner.default_argparser()
-  ClangFlagsTuner.main(argparser.parse_args())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/select_top_results.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/select_top_results.py
deleted file mode 100644
index 7ee878e5f8f84f3f56ea982c1f933b2c1a5b914b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/tutorials/select_top_results.py
+++ /dev/null
@@ -1,101 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 9
-linear_index = 10
-quad_index = 11
-
-top_k = 10
-skip_lines = 1
-
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
-  ref_dir = result_dir + "/" + sub_dir
-  if not os.path.exists(ref_dir):
-    os.mkdir(ref_dir)
-  
-  for i in range(min(k, len(sorted_list)) ):
-    file_name = sorted_list[i][1]
-    file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
-    f = open(file_name, "w+")
-    f.write(str(sorted_list[i][2]) + "\t")
-    f.write(str(sorted_list[i][3]) + "\t")
-    f.write(str(sorted_list[i][4]) + "\n")
-    f.write(sorted_list[i][0])
-    f.close()
-
-    
-    
-
-def select_top_results(result_dir):
-
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  results_arr = []
-  
-  for file_name in file_names:
-
-    if file_name == "confidence_summary.txt":
-      continue
-    
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-
-    log_result = 0.0
-    linear_result = 0.0
-    quad_result = 0.0
-    file_str = ""
-    
-    index = 0
-    f = open(result_dir + "/" + file_name)
-    for x in f:
-      if index >= skip_lines:
-        words = x.split()
-        log_result += float(words[log_index])
-        linear_result += float(words[linear_index])
-        quad_result += float(words[quad_index])
-        file_str += x 
-
-      index += 1
-
-
-    file_result = (file_str, file_name, log_result, linear_result, quad_result)          
-    results_arr.append(file_result)    
-
-    
-  sorted_list = sorted(results_arr, key = lambda tup: tup[2])
-  dump_results(sorted_list, top_k, result_dir, "log")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[3])
-  dump_results(sorted_list, top_k, result_dir, "linear")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[4])
-  dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-#def select_top_configuration(result_dir):
-  
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-
-  select_top_results(result_dir)
-  
-
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
-                      '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/cla_func.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/cla_func.py
deleted file mode 100644
index f4787a2f23f175457ee527f8569dca39bf450605..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/cla_func.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import numpy as np
-import math
-
-
-class Op:
-  def __init__(self):
-    self.M = []
-    self.name = [];
-    self.mutation_partners = [];
-    self.anti_operator = [];
-
-    self.mutation_partners_no = []
-    self.anti_operator_no = []
-
-    # create all operators
-    self.create_operators()
-
-    # check unitarity of all operators
-    self.check_unitarity()
-
-    # determine the indices of the mutation partners
-    self.determine_index_of_mutation_partners()
-
-    # determine the indices of the anti-operators
-    self.determine_index_of_anti_operators()
-
-
-  def create_operators(self):
-
-    # example with +Z
-    #self.M.append(np.matrix([[1.0, 2.0], [2.0+2.0j, 3.0]]))
-    # watch out: python needs 1.0 instead of just 1 to assume float variables
-    #self.name.append('asd');
-    #self.mutation_partners.append(['+z','+w']);
-    #self.anti_operator.append('+w');
-
-    # Operators
-    alpha = math.pi / 3.0;
-    da = math.pi / 10.0;
-
-    # operator 1 +z
-    self.M.append(np.matrix(
-      [[math.cos(da / 2.0) - 1j * math.sin(da / 2.0), 0.0],
-       [0.0, math.cos(da / 2.0) + 1j * math.sin(da / 2.0)]]))
-    self.name.append('+z');
-    self.mutation_partners.append(['-z', '+w', '-w']);
-    self.anti_operator.append('-z');
-
-    # operator 2 -z
-    self.M.append(np.matrix(
-      [[math.cos(-da / 2.0) - 1j * math.sin(-da / 2.0), 0.0],
-       [0.0, math.cos(-da / 2.0) + 1j * math.sin(-da / 2.0)]]))
-    self.name.append('-z');
-    self.mutation_partners.append(['+z', '+w', '-w']);
-    self.anti_operator.append('+z');
-
-    # operator 3 +w
-    self.M.append(np.matrix([
-      [math.cos(da / 2.0) - 1j * math.cos(alpha) * math.sin(da / 2.0),
-       -math.sin(alpha) * math.sin(da / 2.0)],
-      [math.sin(alpha) * math.sin(da / 2.0),
-       math.cos(da / 2.0) + 1j * math.cos(alpha) * math.sin(da / 2.0)]]))
-    self.name.append('+w');
-    self.mutation_partners.append(['+z', '-z', '-w']);
-    self.anti_operator.append('-w');
-
-    # operator 4 -w
-    self.M.append(np.matrix([
-      [math.cos(-da / 2.0) - 1j * math.cos(alpha) * math.sin(-da / 2.0),
-       -math.sin(alpha) * math.sin(-da / 2.0)],
-      [math.sin(alpha) * math.sin(-da / 2.0),
-       math.cos(-da / 2.0) + 1j * math.cos(alpha) * math.sin(-da / 2.0)]]))
-    self.name.append('-w');
-    self.mutation_partners.append(['+z', '-z', '+w']);
-    self.anti_operator.append('+w');
-
-
-  def check_unitarity(self):
-    # this function checks if all defined operators are unitary
-    # in case one isn't unitary the program stops
-    for k in range(len(self.M)):
-      if (np.trace(self.M[k] * self.M[k].getH()) - 2 != 0):
-        print "Operator " + self.name[k] + " (no. " + str(
-          k) + ") isn't unitary!"
-        exit()
-
-  def determine_index_of_mutation_partners(self):
-    # create a field for each operator with an array of possible other gates for the mutation step
-    for k in range(len(self.M)):
-      hlp = []
-      for m in range(len(self.mutation_partners[k])):
-        # go through all possible partners and find them among the operators
-        for n in range(len(self.M)):
-          if self.mutation_partners[k][m] is self.name[n]:
-            hlp.append(n)
-      self.mutation_partners_no.append(hlp)
-
-  def determine_index_of_anti_operators(self):
-    # determine the Anti operator index
-    for k in range(len(self.M)):
-      found_operator = False
-      for n in range(len(self.M)):
-        # go through all possible partners and find them among the operators
-        if self.anti_operator[k] is self.name[n]:
-          self.anti_operator_no.append(n);
-          found_operator = True
-
-      if found_operator == False:
-        print "Couldn't find the anti-operator for operator " + self.name[
-          k] + " (no " + str(k) + ")"
-
-  def __str__(self):
-    # just a test to play around
-    hlpstr = ''
-    for k in range(len(self.M)):
-      hlpstr = hlpstr + self.name[k] + " " + str(
-        self.anti_operator_no[k]) + "\n"
-
-    return "Operator Class:\n" + hlpstr
-
-
-def calc_fidelity(sequence, Op, Ugoal):
-  # Op will be function that return operator matrix
-  # Ugoal 2x2 unitary matrix
-  # sequence = [1 2 3 4];
-  # return = fidelity
-
-  # example:
-  # sequence = [1 4 2 4 5];
-  # Uapprox = Op(1) * Op(4) * Op(2) * Op(4) * Op(5);
-
-  # create identity matrix
-  Uapprox = np.eye(len(Ugoal))
-
-  for k in range(len(sequence)):
-    Uapprox = Op.M[sequence[k]] * Uapprox
-
-  # M.getH() returns the complex conjugate of self
-  result = (1.0 / len(Ugoal)) * abs(np.trace(Ugoal * Uapprox.getH()))
-
-  return result
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/input_generator.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/input_generator.py
deleted file mode 100644
index 009af836f435d013050ff877c4cd66d86019edfc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/input_generator.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import numpy as np
-import math
-import random
-
-
-def generate_random_Ugoal_HARD(N, **kwargs):
-  # N is the length of random matrix multiplication yielding Ugoal
-  # N ~ 100 should be enough
-  # This method is hard because it creates Ugoal over the whole space
-  # Ugoal 2x2 unitary matrix
-
-  # create identity matrix
-  Ugoal = np.eye(2)
-
-  # create all N random angles in 2*pi*[0,1)
-  seq_angle = 2.0 * math.pi * np.random.rand(1, N)
-
-  # determine random operator
-  help2 = np.random.randint(3, size=(1, N))
-
-  for k in range(N):
-    hlp = seq_angle[0][k];
-    if help2[0][k] == 0:
-      Ugoal = X_Mat(hlp) * Ugoal
-    elif help2[0][k] == 1:
-      Ugoal = Y_Mat(hlp) * Ugoal
-    else:
-      Ugoal = Z_Mat(hlp) * Ugoal
-
-  return Ugoal
-
-
-def generate_random_Ugoal_EASY(N, alpha):
-  # N is the length of random matrix multiplication yielding Ugoal
-  # N ~ 100 should be enough
-  # alpha is the used angle between rotation axes
-  # This method is easy because it creates Ugoal over the whole space
-  # Ugoal 2x2 unitary matrix
-
-  # create identity matrix
-  Ugoal = np.eye(2)
-
-  # create all N random angles in 2*pi*[0,1)
-  seq_angle = 2.0 * math.pi * np.random.rand(1, N)
-
-  # determine random operator
-  help2 = np.random.randint(2, size=(1, N))
-
-  for k in range(N):
-    hlp = seq_angle[0][k];
-    if help2[0][k] == 0:
-      Ugoal = Z_Mat(hlp) * Ugoal
-    else:
-      Ugoal = W_Mat(hlp, alpha) * Ugoal
-
-  return Ugoal
-
-
-def generate_random_Ugoal_RANDOM(**kwargs):
-  # Random guess with the following parametrization for U
-  # U = @(q1, q2, q3) [
-  #				[ cos(q1)*exp( i*q2 ), sin(q1)*exp( i*q3 )];
-  #                [-sin(q1)*exp(-i*q3 ), cos(q1)*exp(-i*q2 )]
-  #                    ];
-
-  # create random angles
-  q1 = random.uniform(0.0, 0.5 * math.pi)
-  q2 = random.uniform(0.0, 2.0 * math.pi)
-  q3 = random.uniform(0.0, 2.0 * math.pi)
-
-  return np.matrix([
-    [math.cos(q1) * my_cexp(q2), math.sin(q1) * my_cexp(q3)],
-    [-math.sin(q1) * my_cexp(-q3), math.cos(q1) * my_cexp(-q2)]])
-
-
-def my_cexp(x):
-  return math.cos(x) + 1j * math.sin(x)
-
-
-def X_Mat(a):
-  return np.matrix([[math.cos(a / 2.0), -1j * math.sin(a / 2.0)],
-                    [-1j * math.sin(a / 2.0), math.cos(a / 2.0)]])
-
-
-def Y_Mat(a):
-  return np.matrix([[math.cos(a / 2.0), -math.sin(a / 2.0)],
-                    [math.sin(a / 2.0), math.cos(a / 2.0)]])
-
-
-def Z_Mat(a):
-  return np.matrix([[math.cos(-a / 2.0) + 1j * math.sin(-a / 2.0), 0],
-                    [0, math.cos(a / 2.0) + 1j * math.sin(a / 2.0)]])
-
-
-def W_Mat(a, alpha):
-  return np.matrix([[math.cos(a / 2) - 1j * math.cos(alpha) * math.sin(a / 2.0),
-                     -math.sin(a / 2.0) * math.sin(alpha)],
-                    [math.sin(a / 2.0) * math.sin(alpha),
-                     math.cos(a / 2.0) + 1j * math.cos(alpha) * math.sin(
-                       a / 2.0)]])
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/problem_description.pdf b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/problem_description.pdf
deleted file mode 100644
index e8d09de95a8a6416bf88f10a4d6e4a0fca92670d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/problem_description.pdf and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/testwrapper.sh b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/testwrapper.sh
deleted file mode 100755
index e08593a8764b81a8e68380f9d46753c7a73859c0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/testwrapper.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-COUNT=50
-for Z in `seq $COUNT`
-do
-  for T in `./unitary.py --list-techniques $@`;
-  do
-    echo $Z/$COUNT $T
-    ./unitary.py --technique=$T $@
-  done
-done
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/unitary.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/unitary.py
deleted file mode 100755
index cfa5fe114155f9a7efbd25d191d520846e3d4017..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/examples/unitary/unitary.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a quantum control example motivated by the experimental need
-# to synthesize unitary matrices in SU(2) in optimal time, given an
-# explicit and finite control set generating the whole space, and an
-# admissible error.
-#
-# See problem_description.pdf for additional details.
-#
-# Contributed by Clarice D. Aiello <clarice@mit.edu>
-#
-
-import adddeps  # fix sys.path
-
-import argparse
-import logging
-import math
-import random
-import sys
-
-try:
-  import numpy as np
-except:
-  print >> sys.stderr, '''
-
-ERROR: import numpy failed, please install numpy
-
-Possible things to try:
-  ../../venv/bin/pip install numpy
-  ../../venv/bin/easy_install numpy
-  sudo apt-get install python-numpy
-
-'''
-  raise
-
-import opentuner
-
-from math import sqrt
-import cla_func
-from input_generator import (generate_random_Ugoal_HARD,
-                             generate_random_Ugoal_EASY,
-                             generate_random_Ugoal_RANDOM)
-
-from opentuner.search.manipulator import (ConfigurationManipulator,
-                                          SwitchParameter,
-                                          IntegerParameter,
-                                          FloatParameter)
-
-
-def generate_random_Ugoal_FIXED(**kwargs):
-  Ag = -1 / sqrt(10);
-  Bg = sqrt(2) / sqrt(10);
-  Cg = -sqrt(3) / sqrt(10);
-  Dg = -sqrt(4) / sqrt(10);
-  return cla_func.np.matrix(
-    [[Ag + Cg * 1j, Bg + Dg * 1j], [-Bg + Dg * 1j, Ag - Cg * 1j]])
-
-
-log = logging.getLogger(__name__)
-
-generators = {
-  'hard': generate_random_Ugoal_HARD,
-  'easy': generate_random_Ugoal_EASY,
-  'random': generate_random_Ugoal_RANDOM,
-  'fixed': generate_random_Ugoal_FIXED,
-}
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('--seq-len', type=int, default=10,
-                    help='maximum length for generated sequence')
-parser.add_argument('--goal-type', choices=generators.keys(), default='hard',
-                    help='method used to generate goal')
-parser.add_argument('--goal-n', type=int, default=100,
-                    help='argument to ugoal generator')
-parser.add_argument('--goal-alpha', type=float,
-                    default=random.random() * math.pi,
-                    help='argument to ugoal generator')
-
-
-class Unitary(opentuner.measurement.MeasurementInterface):
-  def __init__(self, *pargs, **kwargs):
-    super(Unitary, self).__init__(*pargs, **kwargs)
-
-    self.op = cla_func.Op()
-    self.num_operators = len(self.op.M)
-    self.Ugoal = generators[args.goal_type](N=args.goal_n,
-                                            alpha=args.goal_alpha)
-
-
-  def run(self, desired_result, input, limit):
-    cfg = desired_result.configuration.data
-
-    sequence = [cfg[i] for i in xrange(self.args.seq_len)
-                if cfg[i] < self.num_operators]
-    # sequence can be shorter than self.args.seq_len with null operator
-
-    if len(sequence) > 0:
-      accuracy = cla_func.calc_fidelity(sequence, self.op, self.Ugoal)
-      # ~.99 is acceptable
-    else:
-      accuracy = 0.0
-
-    return opentuner.resultsdb.models.Result(time=0.0,
-                                             accuracy=accuracy,
-                                             size=len(sequence))
-
-  def manipulator(self):
-    manipulator = ConfigurationManipulator()
-    for d in xrange(self.args.seq_len):
-      # we add 1 to num_operators allow a ignored 'null' operator
-      manipulator.add_parameter(SwitchParameter(d, self.num_operators + 1))
-    return manipulator
-
-  def save_final_config(self, configuration):
-    '''
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    '''
-    cfg = configuration.data
-    sequence = [cfg[i] for i in xrange(self.args.seq_len)
-                if cfg[i] < self.num_operators]
-    print "Final sequence", sequence
-
-  def objective(self):
-    # we could have also chosen to store 1.0 - accuracy in the time field
-    # and use the default MinimizeTime() objective
-    return opentuner.search.objective.MaximizeAccuracyMinimizeSize()
-
-
-if __name__ == '__main__':
-  args = parser.parse_args()
-  Unitary.main(args)
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/gen-venv-bootstrap.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/gen-venv-bootstrap.py
deleted file mode 100755
index ff159bb1080e7f3f0979e4b60f4d41eea5c9d1e9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/gen-venv-bootstrap.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!./venv/bin/python
-
-extra = '''
-
-default_target_dir = 'venv'
-
-pip_install_packages = filter(len, open('requirements.txt').readlines())
-
-import os
-import subprocess
-import sys
-
-def adjust_options(options, args):
-  if len(args)==0:
-    os.chdir(os.path.dirname(__file__))
-    args.append(default_target_dir)
-
-def after_install(options, home_dir):
-  from os.path import join
-  pip = join(home_dir, 'bin/pip')
-  if not os.path.exists(pip):
-    # on windows
-    pip = join(home_dir, 'Scripts/pip.exe')
-  if not os.path.exists(pip):
-    print "error", pip, "is missing"
-  if sys.version_info < (2, 7):
-    subprocess.call([pip, 'install', 'importlib'])
-  for prog in pip_install_packages:
-    subprocess.call([pip, 'install', prog])
-
-'''
-
-import os
-import virtualenv
-
-os.chdir(os.path.dirname(__file__))
-output = virtualenv.create_bootstrap_script(extra)
-f = open('venv-bootstrap.py', 'w').write(output)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/install_reqs.sh b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/install_reqs.sh
deleted file mode 100644
index e671a5f2a1619f7960fa7471774aa94cab3e0bd6..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/install_reqs.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-pip2 install sqlalchemy
-pip2 install psutil
-pip2 install opentuner
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.gnuplot b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.gnuplot
deleted file mode 100644
index 1d4f13021303b0df3c2821eac3935524f494e18f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.gnuplot
+++ /dev/null
@@ -1,10 +0,0 @@
-
-set terminal x11
-set xlabel "Autotuning Seconds"
-set ylabel "Runtime Seconds"
-set xrange [0:600]
-
-plot "/tmp/livedisplay.dat" u 1:2 w lp lw 3 title "Best Execution Time", \
-     "/tmp/livedisplaydetails.dat" w p lw 2 title "Tests (excluding timeouts)"
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.py
deleted file mode 100755
index 5aa3d552d8e5506236d9e004c1f66370b7f19a23..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/misc/livedisplay.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-import os
-import argparse
-import subprocess
-import time
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--gnuplot-filename', default='livedisplay.gnuplot')
-parser.add_argument('--data', default='/tmp/livedisplay.dat')
-parser.add_argument('--details', default='/tmp/livedisplaydetails.dat')
-parser.add_argument('--xrange', type=float, default=300)
-parser.add_argument('--yrange', type=float, default=.05)
-parser.add_argument('--yrange2', type=float, default=1.0)
-parser.add_argument('--remote')
-args = parser.parse_args()
-
-if args.remote:
-  if os.path.exists(args.data):
-    os.unlink(args.data)
-  if os.path.exists(args.details):
-    os.unlink(args.details)
-  syncproc = subprocess.Popen(
-      ["ssh", args.remote, "tail -f -n10000 " + args.data],
-      stdout=open(args.data, "w"))
-  syncproc2 = subprocess.Popen(
-      ["ssh", args.remote, "tail -f -n10000 " + args.details],
-      stdout=open(args.details, "w"))
-
-while '\n' not in open(args.data).read():
-  time.sleep(1)
-while '\n' not in open(args.details).read():
-  time.sleep(1)
-
-p1 = subprocess.Popen(["gnuplot"], stdin=subprocess.PIPE)
-p1.stdin.write(open(args.gnuplot_filename).read())
-print >> p1.stdin, 'set title "Zoomed out"'
-print >> p1.stdin, "set xrange [0:%f]" % args.xrange
-print >> p1.stdin, "set yrange [0:%f]" % args.yrange2
-p1.stdin.flush()
-
-time.sleep(1)
-
-p2 = subprocess.Popen(["gnuplot"], stdin=subprocess.PIPE)
-p2.stdin.write(open(args.gnuplot_filename).read())
-print >> p2.stdin, 'set title "Zoomed in"'
-print >> p2.stdin, "set xrange [0:%f]" % args.xrange
-print >> p2.stdin, "set yrange [0:%f]" % args.yrange
-p2.stdin.flush()
-
-procs = [p1, p2]
-
-while True:
-  time.sleep(1)
-  for p in procs:
-    print >> p.stdin, "replot"
-    p.stdin.flush()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/__init__.py
deleted file mode 100644
index 09a5dead02d214f4dce641069d7be66c124f278a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-import measurement
-import resultsdb
-import search
-import tuningrunmain
-from opentuner.measurement import MeasurementInterface
-from opentuner.resultsdb.models import Configuration
-from opentuner.resultsdb.models import DesiredResult
-from opentuner.resultsdb.models import Result
-from opentuner.resultsdb.models import TuningRun
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import EnumParameter
-from opentuner.search.manipulator import FloatParameter
-from opentuner.search.manipulator import IntegerParameter
-from opentuner.search.manipulator import LogFloatParameter
-from opentuner.search.manipulator import LogIntegerParameter
-from opentuner.search.manipulator import PermutationParameter
-from opentuner.search.manipulator import ScheduleParameter
-from opentuner.search.manipulator import SwitchParameter
-from opentuner.tuningrunmain import init_logging
-
-
-def argparsers():
-  """
-  return a list of ArguementParser to be used as parents to the user's
-  """
-  return [
-      measurement.driver.argparser,
-      measurement.interface.argparser,
-      search.driver.argparser,
-      search.plugin.argparser,
-      search.technique.argparser,
-      #stats.argparser,
-      tuningrunmain.argparser,
-    ]
-
-
-def default_argparser():
-  import argparse
-  return argparse.ArgumentParser(parents=argparsers())
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/api.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/api.py
deleted file mode 100644
index 19a2f60935d7a700771778f0a1304f5ff5cbea6d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/api.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from datetime import datetime
-from opentuner import tuningrunmain
-
-
-class TuningRunManager(tuningrunmain.TuningRunMain):
-  """
-  This class manages a tuning run in a "slave" configuration, where main()
-  is controlled by an another program.
-  """
-  def __init__(self, measurement_interface, args, **kwargs):
-    super(TuningRunManager, self).__init__(measurement_interface, args, **kwargs)
-    self.init()
-    self.tuning_run.state = 'RUNNING'
-    self.commit(force=True)
-    self.search_driver.external_main_begin()
-
-  def get_next_desired_result(self):
-    """
-    Returns a opentuner.resultsdb.DesiredResult that should be tested next.
-    """
-    dr = self.measurement_driver.query_pending_desired_results().first()
-    if dr is None:
-      self.search_driver.external_main_generation()
-      dr = self.measurement_driver.query_pending_desired_results().first()
-      if dr is None:
-        return None
-    self.measurement_driver.claim_desired_result(dr)
-    dr.limit = self.measurement_driver.run_time_limit(dr)
-    return dr
-
-  def get_desired_results(self):
-    """
-    Returns a list of all opentuner.resultsdb.DesiredResult that should be tested next.
-    """
-    drs = self.measurement_driver.query_pending_desired_results().all()
-    if len(drs) == 0:
-      self.search_driver.external_main_generation()
-      drs = self.measurement_driver.query_pending_desired_results().all()
-      if len(drs) == 0:
-        return []
-    for dr in drs:
-      self.measurement_driver.claim_desired_result(dr)
-      dr.limit = self.measurement_driver.run_time_limit(dr)
-
-    return drs
-
-  def report_result(self, desired_result, result, result_input=None):
-    """
-    Report a measured result.  desired_result should have been returned by
-    get_next_desired_result().
-    """
-    self.measurement_driver.report_result(desired_result, result, result_input)
-
-  def get_best_configuration(self):
-    """
-    The best configuration found so far.  From the current tuning run only.
-    """
-    try:
-      return self.search_driver.best_result.configuration.data
-    except AttributeError:
-      return None
-
-  def get_best_result(self):
-    """
-    The best result found so far.  From the current tuning run only.
-    """
-    try:
-      return self.search_driver.best_result
-    except AttributeError:
-      return None
-
-  def finish(self):
-    """
-    Called at the end of the tuning process to call hooks and close database
-    connections.
-    """
-    self.search_driver.external_main_end()
-    self.measurement_interface.save_final_config(
-        self.search_driver.best_result.configuration)
-    self.tuning_run.final_config = self.search_driver.best_result.configuration
-    self.tuning_run.state = 'COMPLETE'
-    self.tuning_run.end_date = datetime.now()
-    self.commit(force=True)
-    self.session.close()
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/driverbase.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/driverbase.py
deleted file mode 100644
index 5486889c0dcedd4342a9cb463aa0d5047f3c0932..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/driverbase.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from opentuner.resultsdb.models import *
-
-
-class DriverBase(object):
-  """
-  shared base class between MeasurementDriver and SearchDriver
-  """
-
-  def __init__(self,
-               session,
-               tuning_run,
-               objective,
-               tuning_run_main,
-               args,
-               **kwargs):
-    self.args = args
-    self.objective = objective
-    self.session = session
-    self.tuning_run_main = tuning_run_main
-    self.tuning_run = tuning_run
-    self.program = tuning_run.program
-
-  def results_query(self,
-                    generation=None,
-                    objective_ordered=False,
-                    config=None):
-    q = self.session.query(Result)
-    q = q.filter_by(tuning_run=self.tuning_run)
-
-    if config:
-      q = q.filter_by(configuration=config)
-
-    if generation is not None:
-      subq = (self.session.query(DesiredResult.result_id)
-              .filter_by(tuning_run=self.tuning_run,
-                         generation=generation))
-      q = q.filter(Result.id.in_(subq.subquery()))
-
-    if objective_ordered:
-      q = self.objective.result_order_by(q)
-
-    return q
-
-  def requests_query(self):
-    q = self.session.query(DesiredResult).filter_by(tuning_run=self.tuning_run)
-    return q
-    
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/#interface.py# b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/#interface.py#
deleted file mode 100644
index 4fe23da5d904183fa4d3c340a74e89918052823e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/#interface.py#
+++ /dev/null
@@ -1,359 +0,0 @@
-
-import abc
-import argparse
-import errno
-import hashlib
-import logging
-import os
-import re
-import signal
-import subprocess
-import threading
-import time
-from multiprocessing.pool import ThreadPool
-
-try:
-  import resource
-except ImportError:
-  resource = None
-
-try:
-  import fcntl
-except ImportError:
-  fcntl = None
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--parallel-compile', action='store_true',
-                       default=False,
-                       help="present if compiling can be done in parallel")
-
-the_io_thread_pool = None
-
-
-class MeasurementInterface(object):
-  """
-  abstract base class for compile and measurement
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self,
-               args=None,
-               project_name=None,
-               program_name='unknown',
-               program_version='unknown',
-               manipulator=None,
-               objective=None,
-               input_manager=None):
-    self.args = args
-    self._project = project_name
-    self._program = program_name
-    self._version = program_version
-    self._objective = objective
-    self._manipulator = manipulator
-    self._input_manager = input_manager
-
-    self.pids = []
-    self.pid_lock = threading.Lock()
-    self.parallel_compile = args.parallel_compile
-    # If parallel_compile is False then compile_and_run() will be invoked
-    # sequentially otherwise the driver first invokes compile() in parallel
-    # followed by run_precompiled() sequentially
-
-  def compile(self, config_data, id):
-    """
-    Compile in PARALLEL according to the configuration in config_data 
-    (obtained from desired_result.configuration) Should use id parameter 
-    to determine output location of executable Return value will be passed 
-    to run_precompiled as compile_result, useful for storing error/timeout 
-    information
-    """
-    if self.parallel_compile:
-        raise RuntimeError('MeasurementInterface.compile() not implemented for',
-                'parallel compilation')
-    pass
-
-  def run_precompiled(self, desired_result, input, limit, compile_result, id):
-    """
-    Run the given desired_result SEQUENTIALLY on input and produce a Result() 
-    Abort early if limit (in seconds) is reached Assume that the executable
-    to be measured has already been compiled to an executable corresponding to
-    identifier id by compile() The compile_result is the return result of compile(), 
-    and it will be None if compile() was not called
-    """
-    if self.parallel_compile:
-        raise RuntimeError('MeasurementInterface.run_precompiled() not implemented', 
-                'for parallel compilation')
-    pass
-
-  def cleanup(self, id):
-    """
-    Clean up any temporary files associated with the executable
-    """
-    pass
-
-  def pre_process(self):
-    """
-    The process before each iteration This method will be called
-    once per iteration before all threads are launched
-    """
-    pass
-
-  def post_process(self):
-    """
-    The process after each iteration This method will be called 
-    once per iteration after all threads are committed
-    """
-    pass
-
-  def extra_convergence_criteria(self, result):
-    """
-    The extra convergence criteria which returns True if the
-    current result is acceptable by the user
-    """
-    return False
-
-  #@abc.abstractmethod
-  def compile_and_run(self, desired_result, input, limit):
-    """
-    Compile and run the given desired_result on input and produce a 
-    Result(), abort early if limit (in seconds) is reached This function 
-    is only used for sequential execution flow
-
-    FIXME: Shoud uncomment @abc.abstractmethod Now comment out for
-    compatiability
-    """
-    return self.run(desired_result, input, limit)
-
-  def run(self, desired_result, input, limit):
-    """
-    run the given desired_result on input and produce a Result(),
-    abort early if limit (in seconds) is reached
-    """
-    return opentuner.resultdb.models.Result()
-
-  def save_final_config(self, config):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    try:
-      config_str = repr(config.data)
-      if len(config_str) > 256:
-        config_str = config_str[:256] + '...'
-      log.info('final configuration: %s', config_str)
-      log.info('you may want to implement save_final_config(), to store this')
-    except:
-      log.error('error printing configuration', exc_info=True)
-
-  def db_program_version(self, session):
-    """return a version identifier for the program being tuned"""
-    return resultsdb.models.ProgramVersion.get(
-        session=session,
-        project=self.project_name(),
-        name=self.program_name(),
-        version=self.program_version(),
-        parameter_info=self.manipulator().parameters_to_json(),
-    )
-
-  def set_driver(self, measurement_driver):
-    self.driver = measurement_driver
-
-  def project_name(self):
-    if self._project is not None:
-      return self._project
-    autoname = re.sub('(Measurement?)Interface$', '', self.__class__.__name__)
-    if autoname:
-      return autoname
-    else:
-      return 'unknown'
-
-  def program_name(self):
-    return self._program
-
-  def program_version(self):
-    return self._version
-
-  def file_hash(self, filename):
-    """helper used to generate program versions"""
-    return hashlib.sha256(open(filename).read()).hexdigest()
-
-  def manipulator(self):
-    """
-    called once to create the search.manipulator.ConfigurationManipulator
-    """
-    if self._manipulator is None:
-      msg = ('MeasurementInterface.manipulator() must be implemented or a '
-             '"manipulator=..." must be provided to the constructor')
-      log.error(msg)
-      raise Exception(msg)
-    return self._manipulator
-
-  def objective(self):
-    """
-    called once to create the search.objective.SearchObjective
-    """
-    if self._objective is None:
-      from ..search.objective import MinimizeTime
-
-      return MinimizeTime()
-    return self._objective
-
-  def input_manager(self):
-    """
-    called once to create the measurement.inputmanager.InputManager
-    """
-    if self._objective is None:
-      from .inputmanager import FixedInputManager
-
-      return FixedInputManager()
-    return self._input_manager
-
-  def seed_configurations(self):
-    """
-    Extra seed configuration objects to add to those given on the command line.
-    Configuration objects (typically dictionaries) not database objects.
-    """
-    return []
-
-  def kill_all(self):
-    self.pid_lock.acquire()
-    for pid in self.pids:
-      goodkillpg(pid)
-    self.pids = []
-    self.pid_lock.release()
-
-  def call_program(self, cmd, limit=None, memory_limit=None, **kwargs):
-    """
-    call cmd and kill it if it runs for longer than limit
-
-    returns dictionary like
-      {'returncode': 0,
-       'stdout': '', 'stderr': '',
-       'timeout': False, 'time': 1.89}
-    """
-    the_io_thread_pool_init(self.args.parallelism)
-    if limit is float('inf'):
-      limit = None
-    if type(cmd) in (str, unicode):
-      kwargs['shell'] = True
-    killed = False
-    t0 = time.time()
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         preexec_fn=preexec_setpgid_setrlimit(memory_limit),
-                         **kwargs)
-    # Add p.pid to list of processes to kill in case of keyboardinterrupt
-    self.pid_lock.acquire()
-    self.pids.append(p.pid)
-    self.pid_lock.release()
-
-    try:
-      stdout_result = the_io_thread_pool.apply_async(p.stdout.read)
-      stderr_result = the_io_thread_pool.apply_async(p.stderr.read)
-      while p.returncode is None:
-        if limit is None:
-          goodwait(p)
-        elif limit and time.time() > t0 + limit:
-          killed = True
-          goodkillpg(p.pid)
-          goodwait(p)
-        else:
-          # still waiting...
-          sleep_for = limit - (time.time() - t0)
-          if not stdout_result.ready():
-            stdout_result.wait(sleep_for)
-          elif not stderr_result.ready():
-            stderr_result.wait(sleep_for)
-          else:
-            #TODO(jansel): replace this with a portable waitpid
-            time.sleep(0.001)
-        p.poll()
-    except:
-      if p.returncode is None:
-        goodkillpg(p.pid)
-      raise
-    finally:
-      # No longer need to kill p
-      self.pid_lock.acquire()
-      if p.pid in self.pids:
-        self.pids.remove(p.pid)
-      self.pid_lock.release()
-
-    t1 = time.time()
-    return {'time': float('inf') if killed else (t1 - t0),
-            'timeout': killed,
-            'returncode': p.returncode,
-            'stdout': stdout_result.get(),
-            'stderr': stderr_result.get()}
-
-  def prefix_hook(self, session):
-    pass
-
-  @classmethod
-  def main(cls, args, *pargs, **kwargs):
-    from opentuner.tuningrunmain import TuningRunMain
-
-    return TuningRunMain(cls(args, *pargs, **kwargs), args).main()
-
-
-class DefaultMeasurementInterface(MeasurementInterface):
-  def run(self, desired_result, input, limit):
-    raise RuntimeError('MeasurementInterface.run() not implemented')
-
-
-def preexec_setpgid_setrlimit(memory_limit):
-  if resource is not None:
-    def _preexec():
-      os.setpgid(0, 0)
-      try:
-        resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
-      except ValueError:
-        pass  # No permission
-      if memory_limit:
-        try:
-          (soft, hard) = resource.getrlimit(resource.RLIMIT_AS)
-          resource.setrlimit(resource.RLIMIT_AS, (min(soft, memory_limit),
-                                                  min(hard, memory_limit)))
-        except ValueError:
-          pass  # No permission
-    return _preexec
-
-
-def the_io_thread_pool_init(parallelism=1):
-  global the_io_thread_pool
-  if the_io_thread_pool is None:
-    the_io_thread_pool = ThreadPool(2 * parallelism)
-    # make sure the threads are started up
-    the_io_thread_pool.map(int, range(2 * parallelism))
-
-
-def goodkillpg(pid):
-  """
-  wrapper around kill to catch errors
-  """
-  log.debug("killing pid %d", pid)
-  try:
-    if hasattr(os, 'killpg'):
-      os.killpg(pid, signal.SIGKILL)
-    else:
-      os.kill(pid, signal.SIGKILL)
-  except:
-    log.error('error killing process %s', pid, exc_info=True)
-
-
-def goodwait(p):
-  """
-  python doesn't check if its system calls return EINTR, retry if it does
-  """
-  while True:
-    try:
-      rv = p.wait()
-      return rv
-    except OSError, e:
-      if e.errno != errno.EINTR:
-        raise
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/.#interface.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/.#interface.py
deleted file mode 120000
index 68c682013089268d9e8f3e50ca41da1228c544c5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/.#interface.py
+++ /dev/null
@@ -1 +0,0 @@
-hashim@hashim-VirtualBox.2708:1511328915
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/__init__.py
deleted file mode 100644
index c289e8d6f5081d846ef431f36649b6e976df1a82..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-import driver
-import interface
-from interface import MeasurementInterface
-from driver import MeasurementDriver
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/driver.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/driver.py
deleted file mode 100644
index d00886920a95e2b7c61ca41b6aea0a89247ab8c9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/driver.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import argparse
-import logging
-import time
-import socket
-import os
-from multiprocessing.pool import ThreadPool
-from datetime import datetime
-
-from sqlalchemy.exc import SQLAlchemyError
-from sqlalchemy.orm.exc import NoResultFound
-
-from opentuner.driverbase import DriverBase
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--machine-class',
-                       help="name of the machine class being run on")
-
-
-class MeasurementDriver(DriverBase):
-  """
-  manages the measurement process, reading DesiredResults and creating Results
-  """
-
-  def __init__(self,
-               measurement_interface,
-               input_manager,
-               **kwargs):
-    super(MeasurementDriver, self).__init__(**kwargs)
-
-    if not self.args.machine_class:
-      self.args.machine_class = 'default'
-
-    self.interface = measurement_interface
-    self.input_manager = input_manager
-    self.commit = self.tuning_run_main.commit
-    self.upper_limit_multiplier = 10.0
-    self.default_limit_multiplier = 2.0
-
-    self.laptime = time.time()
-    self.machine = self.get_machine()
-
-  def get_machine(self):
-    """
-    get (or create) the machine we are currently running on
-    """
-    hostname = socket.gethostname()
-    try:
-      self.session.flush()
-      return self.session.query(Machine).filter_by(name=hostname).one()
-    except sqlalchemy.orm.exc.NoResultFound:
-      m = Machine(name=hostname,
-                  cpu=_cputype(),
-                  cores=_cpucount(),
-                  memory_gb=_memorysize() / (
-                  1024.0 ** 3) if _memorysize() else 0,
-                  machine_class=self.get_machine_class())
-      self.session.add(m)
-      return m
-
-  def get_machine_class(self):
-    """
-    get (or create) the machine class we are currently running on
-    """
-    return MachineClass.get(self.session, name=self.args.machine_class)
-
-  def run_time_limit(self, desired_result, default=3600.0 * 24 * 365 * 10):
-    """return a time limit to apply to a test run (in seconds)"""
-    best = self.results_query(objective_ordered=True).first()
-    if best is None:
-      if desired_result.limit:
-        return desired_result.limit
-      else:
-        return default
-
-    if desired_result.limit:
-      return min(desired_result.limit, self.upper_limit_multiplier * best.time)
-    else:
-      return self.default_limit_multiplier * best.time
-
-  def report_result(self, desired_result, result, input=None):
-    result.configuration = desired_result.configuration
-    result.input = input
-    result.machine = self.machine
-    result.tuning_run = self.tuning_run
-    result.collection_date = datetime.now()
-    self.session.add(result)
-    desired_result.result = result
-    desired_result.state = 'COMPLETE'
-    self.input_manager.after_run(desired_result, input)
-    result.collection_cost = self.lap_timer()
-    self.session.flush()  # populate result.id
-    log.debug(
-        'Result(id=%d, cfg=%d, time=%.4f, accuracy=%.2f, collection_cost=%.2f)',
-        result.id,
-        result.configuration.id,
-        result.time,
-        result.accuracy if result.accuracy is not None else float('NaN'),
-        result.collection_cost)
-    self.commit()
-
-  def run_desired_result(self, desired_result, compile_result=None,
-                         exec_id=None):
-    """
-    create a new Result using input manager and measurment interface
-    Optional compile_result paramater can be passed to run_precompiled as
-    the return value of compile()
-    Optional exec_id paramater can be passed to run_precompiled in case of
-    locating a specific executable
-    """
-    desired_result.limit = self.run_time_limit(desired_result)
-
-    input = self.input_manager.select_input(desired_result)
-    self.session.add(input)
-    self.session.flush()
-
-    log.debug('running desired result %s on input %s', desired_result.id,
-              input.id)
-
-    self.input_manager.before_run(desired_result, input)
-
-    if self.interface.parallel_compile:
-        result = self.interface.run_precompiled(desired_result, input,
-                                                desired_result.limit,
-                                                compile_result, exec_id)
-    else:
-        result = self.interface.compile_and_run(desired_result, input,
-                                                desired_result.limit)
-
-    self.report_result(desired_result, result, input)
-
-  def lap_timer(self):
-    """return the time elapsed since the last call to lap_timer"""
-    t = time.time()
-    r = t - self.laptime
-    self.laptime = t
-    return r
-
-  def claim_desired_result(self, desired_result):
-    """
-    claim a desired result by changing its state to running
-    return True if the result was claimed for this process
-    """
-    self.commit()
-    try:
-      self.session.refresh(desired_result)
-      if desired_result.state == 'REQUESTED':
-        desired_result.state = 'RUNNING'
-        desired_result.start_date = datetime.now()
-        self.commit()
-        return True
-    except SQLAlchemyError:
-      self.session.rollback()
-    return False
-
-  def query_pending_desired_results(self):
-    q = (self.session.query(DesiredResult)
-         .filter_by(tuning_run=self.tuning_run,
-                    state='REQUESTED')
-         .order_by(DesiredResult.generation,
-                   DesiredResult.priority.desc()))
-    return q
-
-  def process_all(self):
-    """
-    process all desired_results in the database
-    """
-    self.lap_timer()  # reset timer
-    q = self.query_pending_desired_results()
-
-    if self.interface.parallel_compile:
-      desired_results = []
-      thread_args = []
-
-      def compile_result(args):
-        interface, data, result_id = args
-        return interface.compile(data, result_id)
-
-      for dr in q.all():
-        if self.claim_desired_result(dr):
-          desired_results.append(dr)
-          thread_args.append((self.interface, dr.configuration.data, dr.id))
-      if len(desired_results) == 0:
-        return
-      thread_pool = ThreadPool(len(desired_results))
-      # print 'Compiling %d results' % len(thread_args)
-      try:
-        # Use map_async instead of map because of bug where keyboardinterrupts are ignored
-        # See http://stackoverflow.com/questions/1408356/keyboard-interrupts-with-pythons-multiprocessing-pool
-        compile_results = thread_pool.map_async(compile_result,
-                                                thread_args).get(9999999)
-      except Exception:
-        # Need to kill other processes because only one thread receives
-        # exception
-        self.interface.kill_all()
-        raise
-      # print 'Running %d results' % len(thread_args)
-      for dr, compile_result in zip(desired_results, compile_results):
-        # Make sure compile was successful
-        self.run_desired_result(dr, compile_result, dr.id)
-        try:
-          self.interface.cleanup(dr.id)
-        except RuntimeError, e:
-          print e
-          # print 'Done!'
-      thread_pool.close()
-    else:
-      for dr in q.all():
-        if self.claim_desired_result(dr):
-          self.run_desired_result(dr)
-
-
-def _cputype():
-  try:
-    return re.search(r"model name\s*:\s*([^\n]*)",
-                     open("/proc/cpuinfo").read()).group(1)
-  except:
-    pass
-  try:
-    # for OS X
-    import subprocess
-
-    return subprocess.Popen(["sysctl", "-n", "machdep.cpu.brand_string"],
-                            stdout=subprocess.PIPE).communicate()[0].strip()
-  except:
-    log.warning("failed to get cpu type")
-  return "unknown"
-
-
-def _cpucount():
-  try:
-    return int(os.sysconf("SC_NPROCESSORS_ONLN"))
-  except:
-    pass
-  try:
-    return int(os.sysconf("_SC_NPROCESSORS_ONLN"))
-  except:
-    pass
-  try:
-    return int(os.environ["NUMBER_OF_PROCESSORS"])
-  except:
-    pass
-  try:
-    return int(os.environ["NUM_PROCESSORS"])
-  except:
-    log.warning("failed to get the number of processors")
-  return 1
-
-
-def _memorysize():
-  try:
-    return int(os.sysconf("SC_PHYS_PAGES") * os.sysconf("SC_PAGE_SIZE"))
-  except:
-    pass
-  try:
-    return int(os.sysconf("_SC_PHYS_PAGES") * os.sysconf("_SC_PAGE_SIZE"))
-  except:
-    pass
-  try:
-    # for OS X
-    import subprocess
-
-    return int(subprocess.Popen(["sysctl", "-n", "hw.memsize"],
-                                stdout=subprocess.PIPE)
-               .communicate()[0].strip())
-  except:
-    log.warning("failed to get total memory")
-  return 1024 ** 3
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/inputmanager.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/inputmanager.py
deleted file mode 100644
index 7acaeaa0cfa178c7e62716a29cca2e9497f255d1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/inputmanager.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import abc
-import opentuner
-from opentuner.resultsdb.models import *
-
-
-class InputManager(object):
-  """
-  abstract base class for compile and measurement
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def set_driver(self, measurement_driver):
-    self.driver = measurement_driver
-    self.session = measurement_driver.session
-    self.program = measurement_driver.tuning_run.program
-
-  @abc.abstractmethod
-  def select_input(self, desired_result):
-    """
-    select the input to be used to test desired_result
-    """
-    return opentuner.resultsdb.models.Input()
-
-
-  def before_run(self, desired_result, input):
-    """hook called before an input is used"""
-    pass
-
-  def after_run(self, desired_result, input):
-    """hook called after an input is used"""
-    pass
-
-  def get_input_class(self):
-    return None
-
-
-class FixedInputManager(InputManager):
-  """
-  an input manage that produces a single input for all tests
-  """
-
-  def __init__(self,
-               input_class_name='fixed',
-               size=-1,
-               path=None,
-               extra=None):
-    self.input_class_name = input_class_name
-    self.size = size
-    self.path = path
-    self.extra = extra
-    self.the_input = None
-    super(FixedInputManager, self).__init__()
-
-
-  def get_input_class(self):
-    return InputClass.get(self.session,
-                          program=self.program,
-                          name=self.input_class_name,
-                          size=self.size)
-
-  def create_input(self, desired_result):
-    """create the fixed input database object, result will be cached"""
-    return Input(input_class=self.get_input_class(),
-                 path=self.path,
-                 extra=self.extra)
-
-  def select_input(self, desired_result):
-    if self.the_input is None:
-      self.the_input = self.create_input(desired_result)
-    return self.the_input
-
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/interface.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/interface.py
deleted file mode 100644
index 174902488289fe4ef038a9dd3553ea13acc68f2b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/measurement/interface.py
+++ /dev/null
@@ -1,366 +0,0 @@
-import abc
-import argparse
-import errno
-import hashlib
-import logging
-import os
-import re
-import signal
-import subprocess
-import threading
-import time
-from multiprocessing.pool import ThreadPool
-
-try:
-  import resource
-except ImportError:
-  resource = None
-
-try:
-  import fcntl
-except ImportError:
-  fcntl = None
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--parallel-compile', action='store_true',
-                       default=False,
-                       help="present if compiling can be done in parallel")
-
-the_io_thread_pool = None
-
-
-class MeasurementInterface(object):
-  """
-  abstract base class for compile and measurement
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self,
-               args=None,
-               project_name=None,
-               program_name='unknown',
-               program_version='unknown',
-               manipulator=None,
-               objective=None,
-               input_manager=None):
-    self.args = args
-    self._project = project_name
-    self._program = program_name
-    self._version = program_version
-    self._objective = objective
-    self._manipulator = manipulator
-    self._input_manager = input_manager
-
-    self.pids = []
-    self.pid_lock = threading.Lock()
-    self.parallel_compile = args.parallel_compile
-    # If parallel_compile is False then compile_and_run() will be invoked
-    # sequentially otherwise the driver first invokes compile() in parallel
-    # followed by run_precompiled() sequentially
-
-  def compile(self, config_data, id):
-    """
-    Compile in PARALLEL according to the configuration in config_data 
-    (obtained from desired_result.configuration) Should use id parameter 
-    to determine output location of executable Return value will be passed 
-    to run_precompiled as compile_result, useful for storing error/timeout 
-    information
-    """
-    if self.parallel_compile:
-        raise RuntimeError('MeasurementInterface.compile() not implemented for',
-                'parallel compilation')
-    pass
-
-  def run_precompiled(self, desired_result, input, limit, compile_result, id):
-    """
-    Run the given desired_result SEQUENTIALLY on input and produce a Result() 
-    Abort early if limit (in seconds) is reached Assume that the executable
-    to be measured has already been compiled to an executable corresponding to
-    identifier id by compile() The compile_result is the return result of compile(), 
-    and it will be None if compile() was not called
-    """
-    if self.parallel_compile:
-        raise RuntimeError('MeasurementInterface.run_precompiled() not implemented', 
-                'for parallel compilation')
-    pass
-
-  def cleanup(self, id):
-    """
-    Clean up any temporary files associated with the executable
-    """
-    pass
-
-  def pre_process(self):
-    """
-    The process before each iteration This method will be called
-    once per iteration before all threads are launched
-    """
-    pass
-
-  def post_process(self):
-    """
-    The process after each iteration This method will be called 
-    once per iteration after all threads are committed
-    """
-    pass
-
-  def extra_convergence_criteria(self, result):
-    """
-    The extra convergence criteria which returns True if the
-    current result is acceptable by the user
-    """
-    return False
-
-  #@abc.abstractmethod
-  def compile_and_run(self, desired_result, input, limit):
-    """
-    Compile and run the given desired_result on input and produce a 
-    Result(), abort early if limit (in seconds) is reached This function 
-    is only used for sequential execution flow
-
-    FIXME: Shoud uncomment @abc.abstractmethod Now comment out for
-    compatiability
-    """
-    return self.run(desired_result, input, limit)
-
-  def run(self, desired_result, input, limit):
-    """
-    run the given desired_result on input and produce a Result(),
-    abort early if limit (in seconds) is reached
-    """
-    return opentuner.resultdb.models.Result()
-
-  def save_final_config(self, config):
-    """
-    called at the end of autotuning with the best resultsdb.models.Configuration
-    """
-    try:
-      config_str = repr(config.data)
-      if len(config_str) > 256:
-        config_str = config_str[:256] + '...'
-      log.info('final configuration: %s', config_str)
-      log.info('you may want to implement save_final_config(), to store this')
-    except:
-      log.error('error printing configuration', exc_info=True)
-
-  def db_program_version(self, session):
-    """return a version identifier for the program being tuned"""
-    return resultsdb.models.ProgramVersion.get(
-        session=session,
-        project=self.project_name(),
-        name=self.program_name(),
-        version=self.program_version(),
-        parameter_info=self.manipulator().parameters_to_json(),
-    )
-
-  def set_driver(self, measurement_driver):
-    self.driver = measurement_driver
-
-  def project_name(self):
-    if self._project is not None:
-      return self._project
-    autoname = re.sub('(Measurement?)Interface$', '', self.__class__.__name__)
-    if autoname:
-      return autoname
-    else:
-      return 'unknown'
-
-  def program_name(self):
-    return self._program
-
-  def program_version(self):
-    return self._version
-
-  def file_hash(self, filename):
-    """helper used to generate program versions"""
-    return hashlib.sha256(open(filename).read()).hexdigest()
-
-  def manipulator(self):
-    """
-    called once to create the search.manipulator.ConfigurationManipulator
-    """
-    if self._manipulator is None:
-      msg = ('MeasurementInterface.manipulator() must be implemented or a '
-             '"manipulator=..." must be provided to the constructor')
-      log.error(msg)
-      raise Exception(msg)
-    return self._manipulator
-
-  def objective(self):
-    """
-    called once to create the search.objective.SearchObjective
-    """
-    if self._objective is None:
-      from ..search.objective import MinimizeSize
-
-      return MinimizeSize()
-    return self._objective
-
-  def input_manager(self):
-    """
-    called once to create the measurement.inputmanager.InputManager
-    """
-    if self._objective is None:
-      from .inputmanager import FixedInputManager
-
-      return FixedInputManager()
-    return self._input_manager
-
-  def seed_configurations(self):
-    """
-    Extra seed configuration objects to add to those given on the command line.
-    Configuration objects (typically dictionaries) not database objects.
-    """
-    return []
-
-  def kill_all(self):
-    self.pid_lock.acquire()
-    for pid in self.pids:
-      goodkillpg(pid)
-    self.pids = []
-    self.pid_lock.release()
-
-  def call_program(self, cmd, limit=None, memory_limit=None, **kwargs):
-    """
-    call cmd and kill it if it runs for longer than limit
-
-    returns dictionary like
-      {'returncode': 0,
-       'stdout': '', 'stderr': '',
-       'timeout': False, 'time': 1.89}
-    """
-    the_io_thread_pool_init(self.args.parallelism)
-    if limit is float('inf'):
-      limit = None
-    if type(cmd) in (str, unicode):
-      kwargs['shell'] = True
-    killed = False
-    t0 = time.time()
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         preexec_fn=preexec_setpgid_setrlimit(memory_limit),
-                         **kwargs)
-    # Add p.pid to list of processes to kill in case of keyboardinterrupt
-    self.pid_lock.acquire()
-    self.pids.append(p.pid)
-    self.pid_lock.release()
-
-    try:
-      stdout_result = the_io_thread_pool.apply_async(p.stdout.read)
-      stderr_result = the_io_thread_pool.apply_async(p.stderr.read)
-      while p.returncode is None:
-        if limit is None:
-          goodwait(p)
-        elif limit and time.time() > t0 + limit:
-          killed = True
-          goodkillpg(p.pid)
-          goodwait(p)
-        else:
-          # still waiting...
-          sleep_for = limit - (time.time() - t0)
-          if not stdout_result.ready():
-            stdout_result.wait(sleep_for)
-          elif not stderr_result.ready():
-            stderr_result.wait(sleep_for)
-          else:
-            #TODO(jansel): replace this with a portable waitpid
-            time.sleep(0.001)
-        p.poll()
-    except:
-      if p.returncode is None:
-        goodkillpg(p.pid)
-      raise
-    finally:
-      # No longer need to kill p
-      self.pid_lock.acquire()
-      if p.pid in self.pids:
-        self.pids.remove(p.pid)
-      self.pid_lock.release()
-
-    # TODO-autotune: Extract the file size and use it
-    # FIXIT: Appropriately update the file size
-    t1 = time.time()
-    return {'time': float('inf') if killed else (t1 - t0),
-            'timeout': killed,
-            'returncode': p.returncode,
-            'stdout': stdout_result.get(),
-            'stderr': stderr_result.get(),
-            }
-
-  def getFileSize(self,filename):
-    fileinfo=os.stat(filename)
-    file_size=fileinfo.st_size
-    return  {'binary_size': file_size}
-
-  def prefix_hook(self, session):
-    pass
-
-  @classmethod
-  def main(cls, args, *pargs, **kwargs):
-    from opentuner.tuningrunmain import TuningRunMain
-
-    return TuningRunMain(cls(args, *pargs, **kwargs), args).main()
-
-
-class DefaultMeasurementInterface(MeasurementInterface):
-  def run(self, desired_result, input, limit):
-    raise RuntimeError('MeasurementInterface.run() not implemented')
-
-
-def preexec_setpgid_setrlimit(memory_limit):
-  if resource is not None:
-    def _preexec():
-      os.setpgid(0, 0)
-      try:
-        resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
-      except ValueError:
-        pass  # No permission
-      if memory_limit:
-        try:
-          (soft, hard) = resource.getrlimit(resource.RLIMIT_AS)
-          resource.setrlimit(resource.RLIMIT_AS, (min(soft, memory_limit),
-                                                  min(hard, memory_limit)))
-        except ValueError:
-          pass  # No permission
-    return _preexec
-
-
-def the_io_thread_pool_init(parallelism=1):
-  global the_io_thread_pool
-  if the_io_thread_pool is None:
-    the_io_thread_pool = ThreadPool(2 * parallelism)
-    # make sure the threads are started up
-    the_io_thread_pool.map(int, range(2 * parallelism))
-
-
-def goodkillpg(pid):
-  """
-  wrapper around kill to catch errors
-  """
-  log.debug("killing pid %d", pid)
-  try:
-    if hasattr(os, 'killpg'):
-      os.killpg(pid, signal.SIGKILL)
-    else:
-      os.kill(pid, signal.SIGKILL)
-  except:
-    log.error('error killing process %s', pid, exc_info=True)
-
-
-def goodwait(p):
-  """
-  python doesn't check if its system calls return EINTR, retry if it does
-  """
-  while True:
-    try:
-      rv = p.wait()
-      return rv
-    except OSError, e:
-      if e.errno != errno.EINTR:
-        raise
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/__init__.py
deleted file mode 100644
index a0150a1577e22cdfd50e490bb4a0c6b735bfcac8..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from connect import connect
-
-import models
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/connect.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/connect.py
deleted file mode 100644
index 1a04d05447a3b62d241a4f2402c22cac15b98b3b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/connect.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from sqlalchemy import create_engine
-from sqlalchemy.orm import scoped_session, sessionmaker
-from models import Base, _Meta
-import logging
-import time
-from pprint import pprint
-
-log = logging.getLogger(__name__)
-
-DB_VERSION = "0.0"
-
-if False:  # profiling of queries
-  import atexit
-  from sqlalchemy import event
-  from collections import Counter
-  from sqlalchemy.engine import Engine
-  the_query_totals = Counter()
-
-  @event.listens_for(Engine, "before_cursor_execute")
-  def before_cursor_execute(conn, cursor, statement,
-                            parameters, context, executemany):
-      context._query_start_time = time.time()
-
-  @event.listens_for(Engine, "after_cursor_execute")
-  def after_cursor_execute(conn, cursor, statement,
-                           parameters, context, executemany):
-      total = time.time() - context._query_start_time
-      the_query_totals[statement] += total
-
-  @atexit.register
-  def report():
-    pprint(the_query_totals.most_common(10))
-
-
-def connect(dbstr):
-  engine = create_engine(dbstr, echo = False)
-  connection = engine.connect()
-
-  #handle case that the db was initialized before a version table existed yet
-  if engine.dialect.has_table(connection, "program"):
-    # if there are existing tables
-    if not engine.dialect.has_table(connection, "_meta"):
-      # if no version table, assume outdated db version and error
-      connection.close()
-      raise Exception("Your opentuner database is currently out of date. Save a back up and reinitialize")
-
-  # else if we have the table already, make sure version matches
-  if engine.dialect.has_table(connection, "_meta"):
-    Session = scoped_session(sessionmaker(autocommit=False,
-                                          autoflush=False,
-                                          bind=engine))
-    version = _Meta.get_version(Session)
-    if not DB_VERSION == version:
-      raise Exception('Your opentuner database version {} is out of date with the current version {}'.format(version, DB_VERSION))
-
-  Base.metadata.create_all(engine)
-
-  Session = scoped_session(sessionmaker(autocommit=False,
-                                        autoflush=False,
-                                        bind=engine))
-  # mark database with current version
-  _Meta.add_version(Session, DB_VERSION)
-  Session.commit()
-
-  return engine, Session
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/models.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/models.py
deleted file mode 100644
index dd88ae8e51c0d94db2364cbc444b9a11d2667116..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/resultsdb/models.py
+++ /dev/null
@@ -1,319 +0,0 @@
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.ext.declarative import declared_attr
-from sqlalchemy import create_engine
-from sqlalchemy.orm import relationship, backref
-from sqlalchemy import (
-  Column, Integer, String, DateTime, Boolean, Enum,
-  Float, PickleType, ForeignKey, Text, func, Index)
-import sqlalchemy
-import re
-
-from cPickle import dumps, loads
-from gzip import zlib
-class CompressedPickler(object):
-  @classmethod
-  def dumps(cls, obj, protocol=2):
-    s = dumps(obj, protocol)
-    sz = zlib.compress(s, 9)
-    if len(sz) < len(s):
-      return sz
-    else:
-      return s
-
-  @classmethod
-  def loads(cls, string):
-    try:
-      s = zlib.decompress(string)
-    except:
-      s = string
-    return loads(s)
-
-class Base(object):
-  @declared_attr
-  def __tablename__(cls):
-    """convert camel case to underscores"""
-    return re.sub(r'([a-z])([A-Z])', r'\1_\2', cls.__name__).lower()
-
-  id = Column(Integer, primary_key=True, index=True)
-
-
-Base = declarative_base(cls=Base)
-
-class _Meta(Base):
-  """ meta table to track current version """
-  db_version = Column(String(128))
-
-  @classmethod
-  def has_version(cls, session, version):
-    try:
-      session.flush()
-      session.query(_Meta).filter_by(db_version=version).one()
-      return True
-    except sqlalchemy.orm.exc.NoResultFound:
-      return False
-
-  @classmethod
-  def get_version(cls, session):
-    try:
-      session.flush()
-      x = session.query(_Meta).one()
-      return x.db_version
-    except sqlalchemy.orm.exc.NoResultFound:
-      return None
-
-  @classmethod
-  def add_version(cls, session, version):
-    if not cls.has_version(session, version):
-      session.add(_Meta(db_version=version))
-
-
-class Program(Base):
-  project = Column(String(128))
-  name = Column(String(128))
-
-  @classmethod
-  def get(cls, session, project, name):
-    try:
-      session.flush()
-      return session.query(Program).filter_by(project=project, name=name).one()
-    except sqlalchemy.orm.exc.NoResultFound:
-      t = Program(project=project, name=name)
-      session.add(t)
-      return t
-
-
-class ProgramVersion(Base):
-  program_id = Column(ForeignKey(Program.id))
-  program = relationship(Program, backref='versions')
-  version = Column(String(128))
-  parameter_info = Column(Text)
-
-  @property
-  def name(self):
-    return self.program.name
-
-  @property
-  def project(self):
-    return self.program.project
-
-  @classmethod
-  def get(cls, session, project, name, version, parameter_info=None):
-    program = Program.get(session, project, name)
-    try:
-      session.flush()
-      if parameter_info is None:
-        return session.query(ProgramVersion).filter_by(program=program,
-                                                     version=version).one()
-      else:
-        return session.query(ProgramVersion).filter_by(program=program,
-                                                      version=version,
-                                                      parameter_info=parameter_info).one()
-    except sqlalchemy.orm.exc.NoResultFound:
-      t = ProgramVersion(program=program, version=version, parameter_info=parameter_info)
-      session.add(t)
-      return t
-
-
-class Configuration(Base):
-  program_id = Column(ForeignKey(Program.id))
-  program = relationship(Program)
-  hash = Column(String(64))
-  data = Column(PickleType(pickler=CompressedPickler))
-
-  @classmethod
-  def get(cls, session, program, hashv, datav):
-    try:
-      session.flush()
-      return (session.query(Configuration)
-              .filter_by(program=program, hash=hashv).one())
-    except sqlalchemy.orm.exc.NoResultFound:
-      t = Configuration(program=program, hash=hashv, data=datav)
-      session.add(t)
-      return t
-
-
-Index('ix_configuration_custom1', Configuration.program_id, Configuration.hash)
-
-
-class MachineClass(Base):
-  name = Column(String(128))
-
-  @classmethod
-  def get(cls, session, name):
-    try:
-      session.flush()
-      return session.query(MachineClass).filter_by(name=name).one()
-    except sqlalchemy.orm.exc.NoResultFound:
-      t = MachineClass(name=name)
-      session.add(t)
-      return t
-
-
-class Machine(Base):
-  name = Column(String(128))
-
-  cpu = Column(String(128))
-  cores = Column(Integer)
-  memory_gb = Column(Float)
-
-  machine_class_id = Column(ForeignKey(MachineClass.id))
-  machine_class = relationship(MachineClass, backref='machines')
-
-
-class InputClass(Base):
-  program_id = Column(ForeignKey(Program.id))
-  program = relationship(Program, backref='inputs')
-
-  name = Column(String(128))
-  size = Column(Integer)
-
-  @classmethod
-  def get(cls, session, program, name='default', size=-1):
-    try:
-      session.flush()
-      return session.query(InputClass).filter_by(program=program,
-                                                 name=name,
-                                                 size=size).one()
-    except sqlalchemy.orm.exc.NoResultFound:
-      t = InputClass(program=program, name=name, size=size)
-      session.add(t)
-      return t
-
-
-class Input(Base):
-  #state          = Column(Enum('ANY_MACHINE', 'SINGLE_MACHINE', 'DELETED'),
-  #                        default='ANY_MACHINE', name='t_input_state')
-
-  input_class_id = Column(ForeignKey(InputClass.id))
-  input_class = relationship(InputClass, backref='inputs')
-
-  #optional, set only for state='SINGLE_MACHINE'
-  #machine_id     = Column(ForeignKey(MachineClass.id))
-  #machine        = relationship(MachineClass, backref='inputs')
-
-  #optional, for use by InputManager
-  path = Column(Text)
-  extra = Column(PickleType(pickler=CompressedPickler))
-
-
-class TuningRun(Base):
-  uuid = Column(String(32), index=True, unique=True)
-
-  program_version_id = Column(ForeignKey(ProgramVersion.id))
-  program_version = relationship(ProgramVersion, backref='tuning_runs')
-
-  machine_class_id = Column(ForeignKey(MachineClass.id))
-  machine_class = relationship(MachineClass, backref='tuning_runs')
-
-  input_class_id = Column(ForeignKey(InputClass.id))
-  input_class = relationship(InputClass, backref='tuning_runs')
-
-  name = Column(String(128), default='unnamed')
-  args = Column(PickleType(pickler=CompressedPickler))
-  objective = Column(PickleType(pickler=CompressedPickler))
-
-  state = Column(Enum('QUEUED', 'RUNNING', 'COMPLETE', 'ABORTED',
-                      name='t_tr_state'),
-                 default='QUEUED')
-  start_date = Column(DateTime, default=func.now())
-  end_date = Column(DateTime)
-
-  final_config_id = Column(ForeignKey(Configuration.id))
-  final_config = relationship(Configuration)
-
-  #__mapper_args__ = {'primary_key': uuid}
-
-  @property
-  def program(self):
-    return self.program_version.program
-
-
-class Result(Base):
-  #set by MeasurementDriver:
-  configuration_id = Column(ForeignKey(Configuration.id))
-  configuration = relationship(Configuration)
-
-  machine_id = Column(ForeignKey(Machine.id))
-  machine = relationship(Machine, backref='results')
-
-  input_id = Column(ForeignKey(Input.id))
-  input = relationship(Input, backref='results')
-
-  tuning_run_id = Column(ForeignKey(TuningRun.id), index=True)
-  tuning_run = relationship(TuningRun, backref='results')
-
-  collection_date = Column(DateTime, default=func.now())
-  collection_cost = Column(Float)
-
-  #set by MeasurementInterface:
-  state = Column(Enum('OK', 'TIMEOUT', 'ERROR',
-                      name='t_result_state'),
-                 default='OK')
-  time = Column(Float)
-  accuracy = Column(Float)
-  energy = Column(Float)
-  size = Column(Float)
-  confidence = Column(Float)
-  #extra = Column(PickleType)
-
-  #set by SearchDriver
-  was_new_best = Column(Boolean)
-
-
-Index('ix_result_custom1', Result.tuning_run_id, Result.was_new_best)
-
-
-class DesiredResult(Base):
-  #set by the technique:
-  configuration_id = Column(ForeignKey(Configuration.id))
-  configuration = relationship(Configuration)
-  limit = Column(Float)
-
-  #set by the search driver
-  priority = Column(Float)
-  tuning_run_id = Column(ForeignKey(TuningRun.id))
-  tuning_run = relationship(TuningRun, backref='desired_results')
-  generation = Column(Integer)
-  requestor = Column(String(128))
-  request_date = Column(DateTime, default=func.now())
-
-  #set by the measurement driver
-  state = Column(Enum('UNKNOWN', 'REQUESTED', 'RUNNING',
-                      'COMPLETE', 'ABORTED',
-                      name="t_dr_state"),
-                 default='UNKNOWN')
-  result_id = Column(ForeignKey(Result.id), index=True)
-  result = relationship(Result, backref='desired_results')
-  start_date = Column(DateTime)
-
-  #input_id        = Column(ForeignKey(Input.id))
-  #input           = relationship(Input, backref='desired_results')
-
-
-Index('ix_desired_result_custom1', DesiredResult.tuning_run_id,
-      DesiredResult.generation)
-
-Index('ix_desired_result_custom2', DesiredResult.tuning_run_id,
-      DesiredResult.configuration_id)
-
-
-# track bandit meta-technique information if a bandit meta-technique is used for a tuning run.
-class BanditInfo(Base):
-  tuning_run_id = Column(ForeignKey(TuningRun.id))
-  tuning_run = relationship(TuningRun, backref='bandit_info')
-  # the bandit exploration/exploitation tradeoff
-  c = Column(Float)
-  # the bandit window
-  window = Column(Integer)
-
-class BanditSubTechnique(Base):
-  bandit_info_id = Column(ForeignKey(BanditInfo.id))
-  bandit_info = relationship(BanditInfo, backref='subtechniques')
-  name = Column(String(128))
-
-
-if __name__ == '__main__':
-  #test:
-  engine = create_engine('sqlite:///:memory:', echo=True)
-  Base.metadata.create_all(engine)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/__init__.py
deleted file mode 100644
index bb4ce57bb2d1760bd9fb6ebe196f39072a43ab4a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-import driver
-import objective
-import plugin
-import technique
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/bandittechniques.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/bandittechniques.py
deleted file mode 100644
index 29816c03de1c52b4b6318991faafb488952e4019..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/bandittechniques.py
+++ /dev/null
@@ -1,316 +0,0 @@
-import abc
-import copy
-import logging
-import math
-import random
-from collections import deque
-
-from .metatechniques import MetaSearchTechnique
-from .technique import register, SearchTechnique, all_techniques, get_random_generator_technique
-
-log = logging.getLogger(__name__)
-
-
-class BanditQueue(object):
-  def __init__(self, keys, C=0.05, window=500, **kwargs):
-    """
-    C is exploration/exploitation tradeoff
-    window is how long to remember past results
-    """
-    super(BanditQueue, self).__init__(**kwargs)
-    self.C = C
-    self.history = deque()
-    self.keys = keys
-    self.use_counts = dict(((k, 0) for k in keys))
-    self.window = window
-    self.request_count = 0
-
-  @abc.abstractmethod
-  def exploitation_term(self, key):
-    """
-    value 0 to 1.0 to represent quality of technique
-    """
-    return 0.0
-
-  def exploration_term(self, key):
-    """
-    value represent how unsure we are (optimal bandit solution)
-    """
-    if self.use_counts[key] > 0:
-      return math.sqrt((2.0 * math.log(len(self.history), 2.0))
-                       / self.use_counts[key])
-    else:
-      return float('inf')
-
-  def bandit_score(self, key):
-    return (self.exploitation_term(key) +
-            self.C * self.exploration_term(key))
-
-  def ordered_keys(self):
-    """select the next technique to use"""
-
-    keys = list(self.keys)
-    random.shuffle(keys)  # break ties randomly
-    keys.sort(key=self.bandit_score)
-
-    self.request_count += 1
-    if log.isEnabledFor(logging.DEBUG) and (self.request_count % 1000) == 0:
-      log.debug(str([
-          (t, self.exploitation_term(t), self.C * self.exploration_term(t))
-          for t in keys]))
-
-    return reversed(keys)
-
-  def on_result(self, key, value):
-    self.history.append((key, value))
-    self.on_push_history(key, value)
-    if len(self.history) > self.window:
-      self.on_pop_history(*self.history.popleft())
-
-  def on_push_history(self, key, value):
-    self.use_counts[key] += 1
-
-  def on_pop_history(self, key, value):
-    self.use_counts[key] -= 1
-
-
-class AUCBanditQueue(BanditQueue):
-  """
-  Area Under the Receiving Operator Curve (AUC) credit assignment
-
-  See:
-  Comparison-based adaptive strategy selection with bandits in differential
-  evolution. Fialho et al.
-  """
-
-  def __init__(self, *args, **kwargs):
-    super(AUCBanditQueue, self).__init__(*args, **kwargs)
-    self.debug = kwargs.get('debug', False)
-    self.auc_sum = dict(((t, 0) for t in self.keys))
-    self.auc_decay = dict(((t, 0) for t in self.keys))
-
-  def exploitation_term_slow(self, key):
-    """
-    value 0 to 1.0 to represent quality of key
-
-    computes the area under the curve where finding a new
-    global best results in adding 1 to a cumulative total
-    """
-    score = 0.0
-    pos = 0
-    for t, value in self.history:
-      if t is key:
-        pos += 1
-        if value:
-          score += pos
-    if pos:
-      return score * 2.0 / (pos * (pos + 1.0))
-    else:
-      return 0.0
-
-  def exploitation_term_fast(self, key):
-    """
-    value 0 to 1.0 to represent quality of key
-
-    optimized O(1) implementation exploitation_term_slow()
-    """
-    score = self.auc_sum[key]
-    pos = self.use_counts[key]
-    if pos:
-      return score * 2.0 / (pos * (pos + 1.0))
-    else:
-      return 0.0
-
-  def exploitation_term(self, key):
-    v1 = self.exploitation_term_fast(key)
-    if self.debug:
-      v2 = self.exploitation_term_slow(key)
-      assert v1 == v2
-    return v1
-
-  def on_push_history(self, key, value):
-    super(AUCBanditQueue, self).on_push_history(key, value)
-    if value:
-      self.auc_sum[key] += self.use_counts[key]
-      self.auc_decay[key] += 1
-
-  def on_pop_history(self, key, value):
-    super(AUCBanditQueue, self).on_pop_history(key, value)
-    self.auc_sum[key] -= self.auc_decay[key]
-    if value:
-      self.auc_decay[key] -= 1
-
-
-
-class AUCBanditMetaTechnique(MetaSearchTechnique):
-  def __init__(self, techniques, bandit_kwargs=dict(), **kwargs):
-    super(AUCBanditMetaTechnique, self).__init__(techniques, **kwargs)
-    self.bandit = AUCBanditQueue([t.name for t in techniques], **bandit_kwargs)
-    self.name_to_technique = dict(((t.name, t) for t in self.techniques))
-
-  def select_technique_order(self):
-    """select the next technique to use"""
-    return (self.name_to_technique[k] for k in self.bandit.ordered_keys())
-
-  def on_technique_result(self, technique, result):
-    self.bandit.on_result(technique.name, result.was_new_best)
-
-  def on_technique_no_desired_result(self, technique):
-    """treat not providing a configuration as not a best"""
-    self.bandit.on_result(technique.name, 0)
-
-  @classmethod
-  def generate_technique(cls, manipulator=None, num_techniques=5, retry_count=3, generator_weight=10, *args, **kwargs):
-    """
-    Generate a bandit by randomly selecting existing techniques or composable techniques.
-    If a composable technique is selected, the operators are then chosen
-
-    :param manipulator: a ConfigurationManipulator used to enumerate parameters
-    :param num_techniques: max number of subtechniques in the bandit
-    :param retry_count: number of times to try getting a new technique before giving up
-    :param generator_weight: weight to increase probability of choosing to generate a technique
-    """
-    techniques, generators = all_techniques()
-
-    # get set of parameters to consider
-    paramset = set()
-    for p in manipulator.params:
-      paramset.add(type(p))
-
-    # filter techniques to get rid of metatechniques
-    basetechniques = [t for t in techniques if not isinstance(t, MetaSearchTechnique)]
-    bandit_techniques = []
-    for i in range(num_techniques):
-      for j in range(retry_count):
-        # pick a technique or generate a composable
-        if random.random() < float(len(basetechniques)) / (len(basetechniques) + generator_weight*len(generators)):
-          candidate = copy.deepcopy(random.choice(basetechniques))
-        else:
-          # pick a random generator
-          candidate = get_random_generator_technique(generators, manipulator=manipulator)
-        if not (candidate.name in [t.name for t in bandit_techniques]):
-          bandit_techniques.append(candidate)
-          break
-
-    # make a bandit of the output list
-    return cls(bandit_techniques, name="GeneratedBandit", *args, **kwargs)
-
-
-class AUCBanditMutationTechnique(SearchTechnique):
-  def __init__(self, bandit_kwargs=dict(), **kwargs):
-    super(AUCBanditMutationTechnique, self).__init__(**kwargs)
-    self.bandit = None
-    self.bandit_kwargs = bandit_kwargs
-    self.pending_results = []
-
-  def handle_requested_result(self, result):
-    for i in xrange(len(self.pending_results)):
-      cfg, name, index = self.pending_results[i]
-      if result.configuration == cfg:
-        self.bandit.on_result((name, index), result.was_new_best)
-        del self.pending_results[i]
-        return
-    log.warning("unexpected result")
-
-  def desired_configuration(self):
-    """
-    use bandit to pick a single manipulator and apply it
-    """
-    seed = self.get_seed()
-    if self.bandit is None:
-      self.init_bandit(seed)
-
-    cfg = self.manipulator.copy(seed)
-    hash1 = self.manipulator.hash_config(cfg)
-    params = self.manipulator.parameters_dict(cfg)
-    for name, index in self.bandit.ordered_keys():
-      if name in params:
-        param = params[name]
-        fns = param.manipulators(cfg)
-        fn = fns[index % len(fns)]
-        fn(cfg)
-        hash2 = self.manipulator.hash_config(cfg)
-        if hash1 != hash2:
-          cfg = self.driver.get_configuration(cfg)
-          self.pending_results.append((cfg, name, index))
-          log.debug("applied %s[%s] manipulator function", name, index)
-          return cfg
-
-    return None
-
-
-  def init_bandit(self, cfg):
-    options = []
-    for param in self.manipulator.parameters(cfg):
-      for i in xrange(len(param.manipulators(cfg))):
-        options.append((param.name, i))
-    # TODO(jansel): remove assumption that set of parameters are fixed
-    self.bandit = AUCBanditQueue(options, **self.bandit_kwargs)
-
-  def get_seed(self):
-    """seed mutation with global best"""
-    if (self.driver.best_result is not None and
-        self.driver.best_result.state == 'OK'):
-      return self.driver.best_result.configuration.data
-    else:
-      return self.manipulator.random()
-
-
-import evolutionarytechniques
-import differentialevolution
-import simplextechniques
-import patternsearch
-import simulatedannealing
-from pso import PSO, HybridParticle
-import globalGA
-register(AUCBanditMutationTechnique())
-
-register(AUCBanditMetaTechnique([
-        differentialevolution.DifferentialEvolutionAlt(),
-        evolutionarytechniques.UniformGreedyMutation(),
-        evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
-        simplextechniques.RandomNelderMead(),
-      ], name = "AUCBanditMetaTechniqueA"))
-register(AUCBanditMetaTechnique([
-        differentialevolution.DifferentialEvolutionAlt(),
-        evolutionarytechniques.UniformGreedyMutation(),
-      ], name = "AUCBanditMetaTechniqueB"))
-register(AUCBanditMetaTechnique([
-        differentialevolution.DifferentialEvolutionAlt(),
-        patternsearch.PatternSearch(),
-      ], name = "AUCBanditMetaTechniqueC"))
-register(AUCBanditMetaTechnique([
-        PSO(crossover = 'op3_cross_OX3'),
-        PSO(crossover = 'op3_cross_OX1'),
-        PSO(crossover = 'op3_cross_CX'),
-        PSO(crossover = 'op3_cross_PMX'),
-        PSO(crossover = 'op3_cross_PX'),
-        evolutionarytechniques.GA(crossover = 'op3_cross_OX3', mutation_rate=0.01, crossover_rate=0.8),
-        evolutionarytechniques.GA(crossover = 'op3_cross_OX1', mutation_rate=0.01, crossover_rate=0.8),
-        evolutionarytechniques.GA(crossover = 'op3_cross_CX', mutation_rate=0.01, crossover_rate=0.8),
-        evolutionarytechniques.GA(crossover = 'op3_cross_PX', mutation_rate=0.01, crossover_rate=0.8),
-        evolutionarytechniques.GA(crossover = 'op3_cross_PMX', mutation_rate=0.01, crossover_rate=0.8),
-        evolutionarytechniques.UniformGreedyMutation(name='ga-base', mutation_rate=0.01)
-      ], name = "PSO_GA_Bandit"))
-register(AUCBanditMetaTechnique([
-	differentialevolution.DifferentialEvolutionAlt(),
-	simulatedannealing.PseudoAnnealingSearch()
-      ], name = "test"))
-register(AUCBanditMetaTechnique([
-        differentialevolution.DifferentialEvolutionAlt(),
-        evolutionarytechniques.UniformGreedyMutation(),
-        evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
-        simplextechniques.RandomNelderMead(),
-	simulatedannealing.PseudoAnnealingSearch()
-      ], name = "test2"))
-register(AUCBanditMetaTechnique([
-	PSO(crossover='op3_cross_OX1'),
-	PSO(crossover='op3_cross_PMX'),
-	PSO(crossover='op3_cross_PX'),
-	evolutionarytechniques.GA(crossover='op3_cross_OX1', crossover_rate=0.5),
-	evolutionarytechniques.GA(crossover='op3_cross_PMX', crossover_rate=0.5),
-	evolutionarytechniques.GA(crossover='op3_cross_PX', crossover_rate=0.5),
-	differentialevolution.DifferentialEvolutionAlt(),
-        globalGA.NormalGreedyMutation( crossover_rate=0.5, crossover_strength=0.2, name='GGA')
-	], name='PSO_GA_DE'))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/composableevolutionarytechniques.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/composableevolutionarytechniques.py
deleted file mode 100644
index e511744f30b8a7d271539e4ed26e247b5574c2b5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/composableevolutionarytechniques.py
+++ /dev/null
@@ -1,512 +0,0 @@
-import random
-import time
-import sys
-import json
-from fn import _
-from technique import all_techniques
-from technique import register
-from technique import register_generator
-from technique import SequentialSearchTechnique
-from manipulator import *
-from opentuner.search.manipulator import Parameter
-
-
-class PopulationMember(object):
-  """
-  An extendable object representing a population member for ComposableEvolutionaryTechniques.
-  Must have the field "config" which is a configuration
-  """
-  def __init__(self, config):
-    self.config = config
-    self.timestamp = time.time()
-
-  def touch(self):
-    """
-    Update the timestamp on a PopulationMember
-    """
-    self.timestamp = time.time()
-
-
-class ComposableEvolutionaryTechnique(SequentialSearchTechnique):
-  """
-  An abstract base class for a technique that is composable with operators
-  """
-  __metaclass__ = abc.ABCMeta
-
-  # operator_map - from param-type to dict with operator name + list of arguments TODO
-  # min_parent - minimum number of parents returned. Limits which operators can be used
-  def __init__(self,
-               operator_map = {},
-               population_size = 50,
-               initial_configs = None,
-               *pargs,
-               **kwargs):
-    """
-
-    :param operator_map:
-    :param population_size:
-    :param initial_configs:
-    :param pargs:
-    :param kwargs:
-    :return:
-    """
-    super(ComposableEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
-    # generate a name based on operators if no name
-
-    self.initial_configurations = initial_configs
-    self.population_size = population_size
-    self.operator_map = operator_map # map from parameter type to an operator function
-
-  def set_operator_map(self, operator_map):
-    self.operator_map = operator_map
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['population_size']
-
-  def default_generated_name(self):
-    """
-    Gets the default name for this technique based on its operator map
-
-    Name is in the format
-    classname paramname;opname;[arg1,arg2,[[kwarg1,v1][kwarg2,v2]]] paramname2;opname2;...
-    """
-    # TODO - include technique hyperparameters
-    parts = [self.base_name()]
-    for param in sorted(self.operator_map, cmp=lambda x,y: cmp(x.__name__, y.__name__)):
-      subparts = [param.__name__]
-      operator_info = self.operator_map[param]
-      subparts.append(operator_info['op_name'])
-      args = list(operator_info['args'])
-      kwargs = operator_info['kwargs']
-      args.append([(k,kwargs[k]) for k in sorted(kwargs)])
-      subparts.append(json.dumps(args, separators=(',', ':')))
-      parts.append(';'.join(subparts))
-    return ' '.join(parts)
-
-
-  def make_population_member(self, config):
-    """
-    Given a configuration, returns an object representing a single member of the
-    population with the given configuration. Meta-data about the configuration,
-    such as last selection time as a parent, can be attached to the object.
-
-    This can be overriden to return a custom population member for use in
-    :py:meth:`get_parents` and :py:meth:`update_population`
-
-    :param config: the configuration that this population member will represent
-    :return: a population member reresenting the input configuration.
-    """
-    return PopulationMember(config)
-
-  def select_parameters(self, params):
-    """
-    Given all the available parameters, return a subset of parameters to operate
-    on when generating a new configuration.
-
-    Can override this to operate on only a subset of parameters.
-
-    :param params: a list of all the available parameters
-    :return: a subset of params
-    """
-    return params
-
-  @abc.abstractmethod
-  def minimum_number_of_parents(self):
-    """
-    Return the minimum number of parents ever returned by :py:meth:`get_parents`.
-    This limits which operators can be composed with the technique. Operators
-    requiring more input configurations than the minimum number of parents will
-    result in an error.
-
-    :return: the minimum number of parents ever generated.
-    """
-    return 1
-
-  @abc.abstractmethod
-  def get_parents(self, population):
-    """
-    Given the current population, return a list of configurations that will be
-    used to generate a new configuration via operators. Returning less parents
-    than guaranteed by :py:meth:`minimum_number_of_parents` results in an error.
-
-    The parents will be passed to operators in order. If there are more parents
-    than required by an operator, extra parents will be discarded.
-
-    Note that operators mutate the first configuration passed in.
-
-    :param population: the current population in the technique
-    :return: a list of parent configurations to generate a new configuration from
-    """
-    pass
-
-  @abc.abstractmethod
-  def update_population(self, config, population):
-    """
-    Update the population given the newest configuration and current population
-    in the technique. should return the new population
-
-    :param config: the newest generated configuration
-    :param population: the current population in this iteration of the technique
-    :return: the updated population
-    """
-    pass
-
-  def get_initial_population(self):
-    """
-    Returns an initial population by passing initial configurations into
-    :py:meth:`make_population_member`
-
-    :return: an initial list of objects returned by :py:meth:`make_population_member`.
-    """
-    init_configs = self.initial_configurations
-    if not init_configs:
-      init_configs = [self.manipulator.random() for i in range(self.population_size)]
-    return [PopulationMember(config) for config in init_configs]
-
-  def lt(self, cfg_a, cfg_b):
-    """
-    Return whether cfg_a has a better objective function score than cfg_b
-
-    :param cfg_a: first configuration
-    :param cfg_b: second configuration
-    :return: True if cfg_a is better than cfg_b
-    """
-    def config(cfg):
-      return self.driver.get_configuration(cfg)
-    return self.objective.lt(config(cfg_a), config(cfg_b))
-
-  def lte(self, cfg_a, cfg_b):
-    """
-    Return whether cfg_a's objective function score is at least as good as cfg_b's
-    score
-
-    :param cfg_a: first configuration
-    :param cfg_b: second configuration
-    :return: True if cfg_a is at least as good as cfg_b
-    """
-    def config(cfg):
-      return self.driver.get_configuration(cfg)
-    return self.objective.lte(config(cfg_a), config(cfg_b))
-
-  def get_global_best_configuration(self):
-    """
-    Return the current global best configuration in the search
-
-    :return: the current global best configuration
-    """
-    if (self.driver.best_result is not None and
-        self.driver.best_result.state == 'OK'):
-      return self.manipulator.copy(self.driver.best_result.configuration.data)
-    else:
-      return self.manipulator.random()
-
-  def get_default_operator(self, param_type):
-    """
-    Given a parameter type, return a dictionary with information about the
-    operator to be used for the parameter. The returned dictionary must contain
-    the following 3 key, value pairs
-
-      1. 'op_name' - the string name of the operator
-      2. 'args' - an iterable of the non-configuration arguments in order
-      3. 'kwargs' - a dictionary from any optional arguments to their values
-
-    :return: a dictionary containing information about the operator to apply for the input parameter type
-    """
-    return {'op_name': 'op1_nop', 'args': [], 'kwargs': {}}
-
-  # HELPER METHODS FOR BUILDING OPERATOR MAP
-  @classmethod
-  def add_to_map(cls, operator_map, param_type, operator_name, *args, **kwargs):
-    """
-    A helper method for adding parameter to operator mappings into the operator
-    map.
-
-    :param operator_map: the operator map to add to
-    :param param_type: the parameter type to use the this operator on
-    :param operator_name: the string name of the operator method
-    :param *args: any non-configuration arguments to the operator
-    :param **kwargs: any keyword arguemnts for the operator
-    """
-    if(isinstance(param_type, Parameter)):
-      ptype = type(param_type)
-    elif (type(param_type) == str):
-      ptype = reduce(getattr, param_type.split("."), sys.modules[__name__])
-    else:
-      ptype = param_type;
-
-    operator_map[ptype] = {'op_name': operator_name, 'args':args, 'kwargs':kwargs}
-
-
-  def main_generator(self):
-    """
-    The primary body of the search technique.
-    Initializes an initial population and then yields configurations by applying
-    operators to get_parents.
-    """
-    min_parents = self.minimum_number_of_parents();
-    # convert a manipulator configuration to a db.models.Configuration
-    def get_driver_configuration(cfg):
-      return self.driver.get_configuration(cfg)
-
-    # initialize the population
-    population = self.get_initial_population()
-
-    # measure initial population
-    for p in population:
-      yield get_driver_configuration(p.config)
-
-    while True:
-      # get parents
-      parents = self.get_parents(population)
-      if len(parents) < min_parents:
-         log.error("%s: Number of parents returned %d is less than the guaranteed"
-                     + " minimum returned by minimum_number_of_parents() %d. ",
-                     self.name, len(parents), min_parents)
-         # fail and let other techniques work forever
-         while True:
-          yield None
-
-      params = self.select_parameters(self.manipulator.params)
-      config = self.get_new_config(parents, params)
-      yield get_driver_configuration(config)
-
-      population = self.update_population(config, population)
-
-      # safety check that population has all been tested
-      for p in population:
-        if not self.driver.has_results(get_driver_configuration(p.config)):
-          yield get_driver_configuration(p.config)
-
-  def get_new_config(self, parents, params):
-    """
-    Return a new configuration to test, given a list of parent configurations
-    This mutates the first parent
-
-    :param parents: A list of parent configurations
-    :params: A list of parameters to operate on
-    :return: The mutated configuration (first parent)
-    """
-    for param in params:
-      self.apply_operator(param, parents) #TODO
-    return parents[0]
-
-  def apply_operator(self, param, parents):
-    """
-    Apply the appropriate operator for param to parents.
-    If an operator takes less input configurations than the number of parents,
-    only the first parents are passed in. If operator takes more input configs
-    than minimum_number_of_parents, logs an error and doesn't do anything
-    """
-    x = self.get_operator(type(param))
-
-    operator_name = x['op_name']
-    if not self.is_valid_operator(type(param), operator_name):
-      # do nothing
-      return
-
-    # operator is already in valid form and starts with op1, op2, op3, op4, or opn
-    num_parents_required = operator_name[2]
-    if num_parents_required == 'n':
-      args = parents[0] + [parents[1:]]
-    else:
-      num_parents_required = int(num_parents_required)
-      args = parents[:num_parents_required]
-    args.extend(x['args'])
-
-    kwargs = x['kwargs']
-
-    getattr(param, operator_name)(*args, **kwargs)
-
-  def get_operator(self, param_type):
-    if param_type in self.operator_map:
-      return self.operator_map[param_type]
-    return self.get_default_operator(param_type)
-
-  def is_valid_operator(self, param_type, operator_name):
-    if not hasattr(param_type, operator_name):
-      log.error("%s: %s is not a valid operator for Parameter type %s",
-                self.name, operator_name, param_type.__name__)
-      return False
-
-    if operator_name[:3] not in ['op1','op2','op3','op4','opn']:
-      log.error("%s: %s is not a valid operator for Parameter type %s",
-                self.name, operator_name, param_type.__name__)
-      return False
-
-    num_parents_required = operator_name[2]
-    if num_parents_required == 'n':
-      return True
-
-    num_parents_required = int(num_parents_required)
-    minimum_number_of_parents = self.minimum_number_of_parents()
-
-    if num_parents_required > minimum_number_of_parents:
-      log.error("%s: %s for Parameter type %s requires more input configs "
-                + "than minimum number of parents, %d, produced by this technique",
-                self.name, operator_name, param_type.__name__, minimum_number_of_parents)
-      return False
-
-    return True
-
-  @classmethod
-  def generate_technique(cls, manipulator=None, *args, **kwargs):
-    """
-    generate a composable technique with random operators
-    """
-    from manipulator import composable_operators
-    # randomly select a composable technique to generate
-    t = cls(*args, **kwargs)
-    if manipulator is None:
-      return t
-
-    paramset = set()
-    for p in manipulator.params:
-      paramset.add(type(p))
-
-    # add some random operator for each param
-    operator_map = {}
-    for param in paramset:
-      operators = composable_operators(param, t.minimum_number_of_parents())
-      # TODO - sometimes use "default" operator (don't choose an operator?
-      # TODO - lower chance of picking op1_nop?
-      ComposableEvolutionaryTechnique.add_to_map(operator_map, param, random.choice(operators))
-
-    t.set_operator_map(operator_map)
-    t.use_default_generated_name()
-    return t
-
-
-class RandomThreeParentsComposableTechnique(ComposableEvolutionaryTechnique):
-  """
-  based on DifferentialEvolution
-  """
-
-  def __init__(self, cr = 0.9, must_mutate_count=1, information_sharing=1, *pargs, **kwargs):
-    super(RandomThreeParentsComposableTechnique, self).__init__(*pargs, **kwargs)
-    self.cr = cr
-    self.must_mutate_count = must_mutate_count
-    self.information_sharing = information_sharing
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['population_size', 'cr', 'must_mutate_count', 'information_sharing']
-
-  def minimum_number_of_parents(self):
-    return 4
-
-  def get_parents(self, population):
-    self.use_f = random.random()
-    population.sort(key=_.timestamp) # sort population by timestamp
-
-    # copy oldest
-    cfg = self.manipulator.copy(population[0].config)
-
-    shuffled_population = map(_.config, population[1:])
-    # mix in the global best configuration
-    shuffled_population += ([self.get_global_best_configuration()]
-                            * self.information_sharing)
-    random.shuffle(shuffled_population)
-
-    # return oldest configuration +_3 other configurations
-    return [cfg] + shuffled_population[0:3]
-
-  def update_population(self, config, population):
-    # replace the oldest configuration if the new one is better.
-    population.sort(key=_.timestamp)
-    if self.lt(config, population[0].config):
-      population[0].config = config
-
-    # mark that oldest configuration is updated
-    population[0].touch()
-
-    return population
-
-  def select_parameters(self, params):
-    """
-    randomly select a subset of parameters to operate on
-    """
-    random.shuffle(params)
-    ret_list = params[:self.must_mutate_count]
-    for param in params[self.must_mutate_count:]:
-      if random.random() < self.cr:
-        ret_list.append(param)
-    return ret_list
-
-  def get_default_operator(self, param_type):
-    return {'op_name': 'op4_set_linear', 'args': [1.0, self.use_f, -self.use_f], 'kwargs': {}}
-
-class GreedyComposableTechnique(ComposableEvolutionaryTechnique):
-  """
-  Always mixes in global best as parents
-  """
-  def __init__(self,
-               mutation_rate = 0.1,
-               must_mutate_count = 1,
-               population_size = 10,
-               *pargs, **kwargs):
-    super(GreedyComposableTechnique, self).__init__(*pargs, **kwargs)
-    self.mutation_rate = mutation_rate
-    self.must_mutate_count = must_mutate_count
-    self.population_size = population_size
-
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['mutation_rate', 'must_mutate_count']
-
-  def minimum_number_of_parents(self):
-    # specify that we will return at least 4 cfgs from get_parents
-    # this maximizes # of operators we can use
-    return 4
-
-  def get_parents(self, population):
-    population.sort(key=_.timestamp) # sort population by timestamp
-
-    # get a 50-50 mix of base and best cfgs as many operators do nothing given identical input cfgs
-    cfg = self.manipulator.copy(population[0].config)
-    # duplicate to get a total of 4 configurations to fulfill the promise in minimum_number_of_parents
-    cfgs = [self.get_global_best_configuration(), cfg]*2
-    # return a random 50-50 mix of the current configuration and global best to pass into operators
-    random.shuffle(cfgs)
-    return cfgs
-
-  def update_population(self, config, population):
-    # replace the oldest configuration if the new one is better.
-    population.sort(key=_.timestamp)
-    if self.lt(config, population[0].config):
-      population[0].config = config
-
-    # mark that oldest configuration is updated
-    population[0].touch()
-
-    return population
-
-  def select_parameters(self, params):
-    random.shuffle(params)
-    ret_list = params[:self.must_mutate_count]
-    for param in params[self.must_mutate_count:]:
-      if random.random() < self.mutation_rate:
-        ret_list.append(param)
-    return ret_list
-
-  def get_default_operator(self, param_type):
-    return {'op_name': 'op1_randomize', 'args': [], 'kwargs':{}}
-
-
-register(RandomThreeParentsComposableTechnique(name='ComposableDiffEvolution',
-                                                 population_size=30))
-register_generator(RandomThreeParentsComposableTechnique)
-register_generator(GreedyComposableTechnique)
-
-
-op_map = {}
-ComposableEvolutionaryTechnique.add_to_map(op_map,
-                                      PermutationParameter,
-                                      "op3_cross", xchoice='op3_cross_CX')
-ComposableEvolutionaryTechnique.add_to_map(op_map,
-                                      "FloatArray",
-                                      "op3_cross", strength=0.4)
-register(RandomThreeParentsComposableTechnique(name='ComposableDiffEvolutionCX',
-                                                 operator_map=op_map,
-                                                 population_size=30))
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/differentialevolution.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/differentialevolution.py
deleted file mode 100644
index cecffc460c5cdbef184fc244a70f9a6af251bddd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/differentialevolution.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import random
-import time
-import logging
-from fn import _
-from technique import register
-from technique import SearchTechnique
-
-log = logging.getLogger(__name__)
-log.setLevel(logging.WARNING)
-
-
-class PopulationMember(object):
-  def __init__(self, config, submitted=True):
-    self.config = config
-    self.submitted = submitted
-    self.timestamp = time.time()
-    self.candidate_replacement = None
-
-  def touch(self):
-    self.timestamp = time.time()
-
-
-class DifferentialEvolution(SearchTechnique):
-  """
-  based on http://cci.lbl.gov/cctbx_sources/scitbx/differential_evolution.py
-  """
-
-  def __init__(self,
-               population_size=30,
-               cr=0.9,  # crossover rate
-               n_cross=1,  # force at least 1 to crossover
-               information_sharing=1,  # number token sharing pop members
-               duplicate_retries=5,  # how many times to retry on duplicate
-               *pargs, **kwargs):
-
-    self.population_size = population_size
-    self.cr = cr
-    self.n_cross = n_cross
-    self.information_sharing = information_sharing
-    self.population = None
-    self.duplicate_retries = duplicate_retries
-    self.limit = None
-    super(DifferentialEvolution, self).__init__(*pargs, **kwargs)
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['population_size', 'cr', 'n_cross', 'information_sharing']
-
-  def initial_population(self):
-    self.population = [PopulationMember(
-        self.driver.get_configuration(
-            self.manipulator.random()), submitted=False)
-        for z in xrange(self.population_size)]
-
-  def oldest_pop_member(self):
-    # since tests are run in parallel, exclude things with a replacement pending
-    pop_without_replacements = filter(lambda x: x.candidate_replacement is None,
-                                      self.population)
-    if not pop_without_replacements:
-      # everything has a pending replacement
-      return None
-    pop_without_replacements.sort(key=_.timestamp)
-    return pop_without_replacements[0]
-
-  def desired_configuration(self):
-    """
-    return a cfg that we should test,
-    """
-    if not self.population:
-      # first time called
-      self.initial_population()
-
-    # make sure initial population is completely submitted
-    for p in self.population:
-      if not p.submitted:
-        p.submitted = True
-        if p is self.population[-1]:
-          log.info('initial population testing done')
-        return p.config
-
-    # pp is member of population to be replaced
-    oldest_pop_member = self.oldest_pop_member()
-    if not oldest_pop_member:
-      return None
-
-    config = None
-    for retry in xrange(self.duplicate_retries):
-      config = self.driver.get_configuration(
-          self.create_new_configuration(oldest_pop_member))
-      if not self.driver.has_results(config):
-        break
-      # new configuration would have been a duplicate, try again
-
-    oldest_pop_member.touch()  # move to back of the line for next replacement
-    oldest_pop_member.candidate_replacement = config
-    self.limit = self.driver.objective.limit_from_config(
-        oldest_pop_member.config)
-    return oldest_pop_member.candidate_replacement
-
-  def create_new_configuration(self, parent_pop_member):
-    cfg = self.manipulator.copy(parent_pop_member.config.data)
-    cfg_params = self.manipulator.proxy(cfg)
-
-    # pick 3 random parents, not pp
-    shuffled_pop = list(set(self.population) - set([parent_pop_member]))
-
-    # share information with other techniques
-    if self.driver.best_result:
-      shuffled_pop += ([PopulationMember(self.driver.best_result.configuration)]
-                       * self.information_sharing)
-
-    random.shuffle(shuffled_pop)
-    x1, x2, x3 = map(_.config.data, shuffled_pop[0:3])
-
-    use_f = random.random() / 2.0 + 0.5
-
-    params = self.manipulator.param_names(cfg, x1, x2, x3)
-    random.shuffle(params)
-    for i, k in enumerate(params):
-      if i < self.n_cross or random.random() < self.cr:
-        # cfg = x1 + use_f*(x2 - x3)
-        cfg_params[k].op4_set_linear(x1, x2, x3, 1.0, use_f, -use_f)
-
-    return cfg
-
-  def handle_requested_result(self, result):
-    """called when new results are added"""
-    for p in self.population:
-      if p.candidate_replacement == result.configuration:
-        if self.objective.lt(p.candidate_replacement, p.config):
-          # candidate replacement was better, replace it!
-          p.config = p.candidate_replacement
-          log.info('better point')
-        p.candidate_replacement = None
-
-
-class DifferentialEvolutionAlt(DifferentialEvolution):
-  def __init__(self, cr=0.2, **kwargs):
-    kwargs['cr'] = cr
-    super(DifferentialEvolutionAlt, self).__init__(**kwargs)
-
-
-register(DifferentialEvolution())
-register(DifferentialEvolutionAlt())
-register(DifferentialEvolution(population_size=100, cr=0.2,
-                               name='DifferentialEvolution_20_100'))
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/driver.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/driver.py
deleted file mode 100644
index 7924e36e6fbc772e375ce344e8de919f66e8c6b4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/driver.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import argparse
-import copy
-import logging
-import os
-import sys
-
-from datetime import datetime
-from fn import _
-from opentuner.driverbase import DriverBase
-from opentuner.resultsdb.models import Configuration
-from opentuner.resultsdb.models import DesiredResult
-from opentuner.resultsdb.models import Result
-from opentuner.resultsdb.models import BanditInfo
-from opentuner.resultsdb.models import BanditSubTechnique
-from opentuner.search import plugin
-from opentuner.search import technique
-from opentuner.search.bandittechniques import AUCBanditMetaTechnique
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--test-limit', type=int, default=5000,
-                       help='stop tuning after given tests count')
-argparser.add_argument('--stop-after', type=float,
-                       help='stop tuning after given seconds')
-argparser.add_argument('--parallelism', type=int, default=8,
-                       help='how many tests to support at once')
-argparser.add_argument('--pipelining', type=int, default=0,
-                       help='how long a delay (in generations) before results are available')
-argparser.add_argument('--bail-threshold', type=int, default=5000,
-                       help='abort if no requests have been made in X generations')
-argparser.add_argument('--no-dups', action='store_true',
-                       help='don\'t print out warnings for duplicate requests')
-argparser.add_argument('--seed-configuration', action='append', default=[],
-                       metavar='FILENAME', help="""
-                           Start search at a given configuration.  Can be
-                           specified multiple times.  Configurations are loaded
-                           with ConfigurationManipulator.load_from_file()
-                           and file format is detected from extension.""")
-
-
-class SearchDriver(DriverBase):
-  """
-  controls the search process managing root_technique and creating
-  DesiredResults
-  """
-
-  def __init__(self, manipulator, extra_seeds=None, extra_criteria=None, **kwargs):
-    super(SearchDriver, self).__init__(**kwargs)
-    if extra_seeds is None:
-      extra_seeds = []
-    self.manipulator = manipulator
-    self.wait_for_results = self.tuning_run_main.results_wait
-    self.commit = self.tuning_run_main.commit
-    self.extra_criteria = extra_criteria
-
-    self.generation = 0
-    self.test_count = 0
-    self.plugins = plugin.get_enabled(self.args)
-    self.pending_result_callbacks = list()  # (DesiredResult, function) tuples
-    # deepcopy is required to have multiple tuning runs in a single process
-    if self.args.list_techniques:
-      techniques, generators = technique.all_techniques()
-      for t in techniques:
-        print t.name
-      sys.exit(0)
-
-    if self.args.generate_bandit_technique:
-      # generate a bandit
-      self.root_technique = AUCBanditMetaTechnique.generate_technique(manipulator)
-    else:
-      self.root_technique = copy.deepcopy(technique.get_root(self.args))
-
-    if isinstance(self.root_technique, AUCBanditMetaTechnique):
-      self.session.flush()
-      info = BanditInfo(tuning_run=self.tuning_run,
-                        c=self.root_technique.bandit.C,
-                        window=self.root_technique.bandit.window,)
-      self.session.add(info)
-      for t in self.root_technique.techniques:
-        subtechnique = BanditSubTechnique(bandit_info=info,
-                                          name=t.name)
-        self.session.add(subtechnique)
-
-
-    self.objective.set_driver(self)
-    self.pending_config_ids = set()
-    self.best_result = None
-    self.new_results = []
-
-    for t in self.plugins:
-      t.set_driver(self)
-    self.root_technique.set_driver(self)
-    self.seed_cfgs = list(extra_seeds)
-    for cfg_filename in reversed(self.args.seed_configuration):
-      if os.path.exists(cfg_filename):
-        self.seed_cfgs.append(manipulator.load_from_file(cfg_filename))
-      else:
-        log.error('no such file for --seed-configuration %s', cfg_filename)
-
-    self.plugins.sort(key=_.priority)
-
-  def add_plugin(self, p):
-    if p in self.plugins:
-      return
-    self.plugins.append(p)
-    self.plugins.sort(key=_.priority)
-    p.set_driver(self)
-
-  def convergence_criteria(self):
-    """returns true if the tuning process should stop"""
-    if self.args.stop_after:
-      elapsed = (datetime.now() - self.tuning_run.start_date)
-      try:
-        elapsed = elapsed.total_seconds()
-      except:  # python 2.6
-        elapsed = elapsed.days * 86400 + elapsed.seconds
-      if elapsed > self.args.stop_after:
-          return True
-    if self.test_count > self.args.test_limit:
-        return True    
-    if self.extra_criteria:
-        if self.extra_criteria(self.new_results):
-            return True
-    return False
-
-  def register_result_callback(self, desired_result, callback):
-    if desired_result.result is not None:
-      callback(desired_result.result)
-    else:
-      self.pending_result_callbacks.append((desired_result, callback))
-
-  def result_callbacks(self):
-    pending = self.pending_result_callbacks
-    self.pending_result_callbacks = list()
-    for dr, callback in pending:
-      if dr.result is not None:
-        callback(dr.result)
-        continue
-      elif self.generation - dr.generation > self.args.pipelining:
-        # see if we can find a result
-        results = self.results_query(config=dr.configuration).all()
-        log.warning("Result callback %d (requestor=%s) pending for "
-                    "%d generations %d results available",
-                    dr.id, dr.requestor, self.generation - dr.generation,
-                    len(results))
-        if len(results):
-          dr.result = results[0]
-          callback(dr.result)
-          continue
-      # try again later
-      self.pending_result_callbacks.append((dr, callback))
-
-  def has_results(self, config):
-    return self.results_query(config=config).count() > 0
-
-  def run_generation_techniques(self):
-    tests_this_generation = 0
-    self.plugin_proxy.before_techniques()
-    for z in xrange(self.args.parallelism):
-      
-      if self.seed_cfgs:
-        config = self.get_configuration(self.seed_cfgs.pop())
-        dr = DesiredResult(configuration=config,
-                           requestor='seed',
-                           generation=self.generation,
-                           request_date=datetime.now(),
-                           tuning_run=self.tuning_run)
-      else:
-        dr = self.root_technique.desired_result()
-      if dr is None or dr is False:
-        log.debug("no desired result, skipping to testing phase")
-        break
-      self.session.flush()  # populate configuration_id
-      duplicates = (self.session.query(DesiredResult)
-                    .filter_by(tuning_run=self.tuning_run,
-                               configuration_id=dr.configuration_id)
-                    .filter(DesiredResult.id != dr.id)
-                    .order_by(DesiredResult.request_date)
-                    .limit(1).all())
-      self.session.add(dr)
-      if len(duplicates):
-        if not self.args.no_dups:
-          log.warning("duplicate configuration request #%d %s/%s %s",
-                      self.test_count,
-                      dr.requestor,
-                      duplicates[0].requestor,
-                      'OLD' if duplicates[0].result else 'PENDING')
-        self.session.flush()
-        desired_result_id = dr.id
-
-        def callback(result):
-          dr = self.session.query(DesiredResult).get(desired_result_id)
-          dr.result = result
-          dr.state = 'COMPLETE'
-          dr.start_date = datetime.now()
-
-        self.register_result_callback(duplicates[0], callback)
-      else:
-        log.debug("desired result id=%d, cfg=%d", dr.id, dr.configuration_id)
-        dr.state = 'REQUESTED'
-      self.test_count += 1
-      tests_this_generation += 1
-    self.plugin_proxy.after_techniques()
-    return tests_this_generation
-
-  def process_new_results(self):
-    self.new_results = []
-    for result in (self.results_query()
-                       .filter_by(was_new_best=None)
-                       .order_by(Result.collection_date)):
-      self.plugin_proxy.on_result(result)
-      self.new_results.append(result)
-      if self.best_result is None:
-        self.best_result = result
-        result.was_new_best = True
-      elif self.objective.lt(result, self.best_result):
-        self.best_result = result
-        result.was_new_best = True
-        self.plugin_proxy.on_new_best_result(result)
-      else:
-        result.was_new_best = False
-    self.result_callbacks()
-
-  def run_generation_results(self, offset=0):
-    self.commit()
-    self.plugin_proxy.before_results_wait()
-    self.wait_for_results(self.generation + offset)
-    self.plugin_proxy.after_results_wait()
-    self.process_new_results()
-
-  @property
-  def plugin_proxy(self):
-    """
-    forward any method calls on the returned object to all plugins
-    """
-    plugins = self.plugins
-
-    class PluginProxy(object):
-      def __getattr__(self, method_name):
-        def plugin_method_proxy(*args, **kwargs):
-          rv = []
-          for plugin in plugins:
-            rv.append(getattr(plugin, method_name)(*args, **kwargs))
-          return filter(lambda x: x is not None, rv)
-
-        return plugin_method_proxy
-
-    return PluginProxy()
-
-  def get_configuration(self, cfg):
-    """called by SearchTechniques to create Configuration objects"""
-    self.manipulator.normalize(cfg)
-    hashv = self.manipulator.hash_config(cfg)
-    config = Configuration.get(self.session,self.program, hashv, cfg)
-    return config
-
-  def main(self):
-    self.plugin_proxy.set_driver(self)
-    self.plugin_proxy.before_main()
-
-    no_tests_generations = 0
-
-    # prime pipeline with tests
-    for z in xrange(self.args.pipelining):
-      self.run_generation_techniques()
-      self.generation += 1
-
-    while not self.convergence_criteria():
-      if self.run_generation_techniques() > 0:
-        no_tests_generations = 0
-      elif no_tests_generations <= self.args.bail_threshold:
-        no_tests_generations += 1
-      else:
-        break
-      self.run_generation_results(offset=-self.args.pipelining)
-      self.generation += 1
-
-    self.plugin_proxy.after_main()
-
-  def external_main_begin(self):
-    self.plugin_proxy.set_driver(self)
-    self.plugin_proxy.before_main()
-
-  def external_main_generation(self):
-    if self.generation > 0:
-      self.plugin_proxy.after_results_wait()
-    self.process_new_results()
-    self.run_generation_techniques()
-    self.commit()
-    self.plugin_proxy.before_results_wait()
-
-  def external_main_end(self):
-    self.plugin_proxy.after_main()
-
-
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/evolutionarytechniques.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/evolutionarytechniques.py
deleted file mode 100644
index e663ac1345cfbd0823df2231fc3e8040298059f9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/evolutionarytechniques.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import abc
-import copy
-import random
-from technique import SearchTechnique
-from opentuner.search import technique
-
-class EvolutionaryTechnique(SearchTechnique):
-  def __init__(self,
-               mutation_rate = 0.1,
-               crossover_rate = 0.0,
-               must_mutate_count = 1,
-               *pargs, **kwargs):
-    super(EvolutionaryTechnique, self).__init__(*pargs, **kwargs)
-    self.mutation_rate = mutation_rate
-    self.crossover_rate = crossover_rate
-    self.must_mutate_count = must_mutate_count
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['mutation_rate', 'crossover_rate', 'must_mutate_count']
-
-
-  def desired_configuration(self):
-    """
-    return a (cfg, priority) that we should test,
-    through random mutation and crossover
-    """
-    #TODO: set limit value
-
-    parents = self.selection()
-    parents = map(copy.deepcopy, parents)
-    parent_hashes = map(self.manipulator.hash_config, parents)
-
-    if len(parents) > 1:
-      cfg = self.crossover(parents)
-    else:
-      cfg = parents[0]
-
-    for z in xrange(10): #retries
-      self.mutation(cfg)
-      if self.manipulator.hash_config(cfg) in parent_hashes:
-        continue # try again
-      return cfg
-
-  def mutation(self, cfg):
-    """
-    mutate cfg in place
-    """
-    params = self.manipulator.parameters(cfg)
-    random.shuffle(params)
-    for param in params[:self.must_mutate_count]:
-      self.mutate_param(cfg, param)
-    for param in params[self.must_mutate_count:]:
-      if random.random() < self.mutation_rate:
-        self.mutate_param(cfg, param)
-
-  def mutate_param(self, cfg, param):
-    """
-    mutate single parameter of cfg in place
-    """
-    param.op1_randomize(cfg)
-
-  def crossover(self):
-    raise Exception('Not implemented')
-
-  def selection(self):
-    """return a list of parent configurations to use"""
-    if random.random() < self.crossover_rate:
-      return [self.select(),
-              self.select()]
-    else:
-      return [self.select()]
-
-  @abc.abstractmethod
-  def select(self):
-    """return a single random parent configuration"""
-    return None
-
-class GreedySelectionMixin(object):
-  """
-  EvolutionaryTechnique mixin for greedily selecting the best known
-  configuration
-  """
-  def select(self):
-    """return a single random parent configuration"""
-    if (self.driver.best_result is not None and
-        self.driver.best_result.state == 'OK'):
-      return self.driver.best_result.configuration.data
-    else:
-      return self.manipulator.random()
-
-class NormalMutationMixin(object):
-  """
-  Mutate primitive parameters according to normal distribution
-  """
-
-  def __init__(self, sigma = 0.1, *pargs, **kwargs):
-    super(NormalMutationMixin, self).__init__(*pargs, **kwargs)
-    self.sigma = sigma
-
-  def mutate_param(self, cfg, param):
-    """
-    mutate single parameter of cfg in place
-    """
-    if param.is_primitive():
-      param.op1_normal_mutation(cfg, self.sigma)
-    else:
-      random.choice(param.manipulators(cfg))(cfg)
-
-
-class CrossoverMixin(object):
-  def __init__(self, crossover,   *pargs, **kwargs):
-    super(CrossoverMixin, self).__init__(*pargs, **kwargs)
-    self.crossover_op = crossover
-    self.name = 'ga-'+crossover.replace("op3_cross_","")
-
-  def crossover(self, cfgs):
-    """
-    Crossover the first permtation parameter, if found, of two parents and
-    return one offspring cfg
-    """
-    cfg1, cfg2, = cfgs
-    new = self.manipulator.copy(cfg1)
-    params = self.manipulator.parameters(cfg1)
-    for param in params:
-      if param.is_permutation() and param.size>6:
-        getattr(param, self.crossover_op)(new, cfg1, cfg2, d=param.size/3)
-    return new
-
-
-class UniformGreedyMutation(GreedySelectionMixin, EvolutionaryTechnique):
-  pass
-
-class NormalGreedyMutation(NormalMutationMixin, GreedySelectionMixin, EvolutionaryTechnique):
-  pass
-
-class GA(CrossoverMixin, UniformGreedyMutation):
-  pass
-
-technique.register(GA(crossover = 'op3_cross_OX3', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_OX1', mutation_rate=0.10,crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_PX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_CX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_PMX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(UniformGreedyMutation(name='ga-base', mutation_rate=0.10))
-
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation05', mutation_rate=0.05))
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation10', mutation_rate=0.10))
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation20', mutation_rate=0.20))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation05', mutation_rate=0.05))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation10', mutation_rate=0.10))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation20', mutation_rate=0.20))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/globalGA.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/globalGA.py
deleted file mode 100644
index e9b1f711746bbd42d0fb6e7ca3972d467c703e66..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/globalGA.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import abc
-import copy
-import random
-from technique import SearchTechnique
-from opentuner.search import technique
-
-class GlobalEvolutionaryTechnique(SearchTechnique):
-  def __init__(self,
-               mutation_rate = 0.1,
-               crossover_rate = 0.0,
-               must_mutate_count = 1,
-	             crossover_strength = 0.1,
-               *pargs, **kwargs):
-    super(GlobalEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
-    self.mutation_rate = mutation_rate
-    self.crossover_rate = crossover_rate
-    self.must_mutate_count = must_mutate_count
-    self.crossover_strength = crossover_strength
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['mutation_rate', 'crossover_rate', 'must_mutate_count', 'crossover_strength']
-
-  def desired_configuration(self):
-    """
-    return a (cfg, priority) that we should test,
-    through random mutation and crossover
-    """
-    #TODO: set limit value
-
-    parents = self.selection()
-    parents = map(copy.deepcopy, parents)
-    parent_hashes = map(self.manipulator.hash_config, parents)
-
-    if len(parents) > 1:
-      cfg = self.crossover(parents)
-    else:
-      cfg = parents[0]
-
-    for z in xrange(10): #retries
-      self.mutation(cfg)
-      if self.manipulator.hash_config(cfg) in parent_hashes:
-        continue # try again
-      return cfg
-
-  def mutation(self, cfg):
-    """
-    mutate cfg in place
-    """
-    params = self.manipulator.parameters(cfg)
-    random.shuffle(params)
-    for param in params[:self.must_mutate_count]:
-      self.mutate_param(cfg, param)
-    for param in params[self.must_mutate_count:]:
-      if random.random() < self.mutation_rate:
-        self.mutate_param(cfg, param)
-
-  def mutate_param(self, cfg, param):
-    """
-    mutate single parameter of cfg in place
-    """
-    param.op1_randomize(cfg)
-
-  def crossover(self, cfgs):
-    cfg1, cfg2, = cfgs
-    new = self.manipulator.copy(cfg1)
-    params = self.manipulator.parameters(cfg1)
-    random.shuffle(params)
-    d = int(self.crossover_strength*len(params))
-    for param in params[:d]:
-      param.set_value(new, param.get_value(cfg2))
-    return new
-
-  def selection(self):
-    """return a list of parent configurations to use"""
-    if random.random() < self.crossover_rate:
-      return [self.select(),
-              self.select()]
-    else:
-      return [self.select()]
-
-  @abc.abstractmethod
-  def select(self):
-    """return a single random parent configuration"""
-    return None
-
-class GreedySelectionMixin(object):
-  """
-  EvolutionaryTechnique mixin for greedily selecting the best known
-  configuration
-  """
-  def select(self):
-    """return a single random parent configuration"""
-    if (self.driver.best_result is not None and
-        self.driver.best_result.state == 'OK'):
-      return self.driver.best_result.configuration.data
-    else:
-      return self.manipulator.random()
-
-class NormalMutationMixin(object):
-  """
-  Mutate primitive parameters according to normal distribution
-  """
-
-  def __init__(self, sigma = 0.1, *pargs, **kwargs):
-    super(NormalMutationMixin, self).__init__(*pargs, **kwargs)
-    self.sigma = sigma
-
-  def mutate_param(self, cfg, param):
-    """
-    mutate single parameter of cfg in place
-    """
-    if param.is_primitive():
-      param.op1_normal_mutation(cfg, self.sigma)
-    else:
-      random.choice(param.manipulators(cfg))(cfg)
-
-
-class UniformGreedyMutation(GreedySelectionMixin, GlobalEvolutionaryTechnique):
-  pass
-
-class NormalGreedyMutation(NormalMutationMixin, GreedySelectionMixin, GlobalEvolutionaryTechnique):
-  pass
-
-technique.register(NormalGreedyMutation( crossover_rate=0.5, crossover_strength=0.2, name='GGA'))
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/manipulator.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/manipulator.py
deleted file mode 100755
index decd476bf37ec2c12d2578b9b8266e5f8c705b12..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/manipulator.py
+++ /dev/null
@@ -1,1853 +0,0 @@
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-import abc
-import collections
-import copy
-import hashlib
-import json
-import logging
-import math
-import os
-import pickle
-import random
-from fn import _
-import argparse
-from datetime import datetime
-import numpy
-import inspect
-import sys
-
-log = logging.getLogger(__name__)
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--list-params', '-lp',
-                       help='list available parameter classes')
-
-
-class ConfigurationManipulatorBase(object):
-  """
-  abstract interface for objects used by search techniques to mutate
-  configurations
-  """
-  __metaclass__ = abc.ABCMeta
-
-  # List of file formats, which can be extended by subclasses. Used in
-  # write_to_file() and load_from_file().  Objects in list must define
-  # load(fd) and dump(cfg, fd).
-  FILE_FORMATS = {'default': json, 'json': json,
-                  'pickle': pickle, 'pk': pickle}
-
-  def validate(self, config):
-    """is the given config valid???"""
-    return all(map(_.validate(config), self.parameters(config)))
-
-  def normalize(self, config):
-    """mutate config into canonical form"""
-    for param in self.parameters(config):
-      param.normalize(config)
-
-  def set_search_driver(self, search_driver):
-    """called exactly once during setup"""
-    pass
-
-  def copy(self, config):
-    """produce copy of config"""
-    return copy.deepcopy(config)
-
-  def parameters_dict(self, config):
-    """convert self.parameters() to a dictionary by name"""
-    return dict([(p.name, p) for p in self.parameters(config)])
-
-  def param_names(self, *args):
-    """return union of parameter names in args"""
-    return sorted(reduce(set.union,
-                         [set(map(_.name, self.parameters(cfg)))
-                          for cfg in args]))
-
-  def linear_config(self, a, cfg_a, b, cfg_b, c, cfg_c):
-    """return a configuration that is a linear combination of 3 other configs"""
-    dst = self.copy(cfg_a)
-    dst_params = self.proxy(dst)
-    for k in self.param_names(dst, cfg_a, cfg_b, cfg_c):
-      dst_params[k].op4_set_linear(cfg_a, cfg_b, cfg_c, a, b, c)
-    return dst
-
-  def _get_serializer(self, filename, format=None):
-    """
-    Extract the correct file format serializer from self.FILE_FORMATS.
-    Guess the format by extension if one is not given.
-    """
-    if format is None:
-      format = os.path.splitext(filename)[1].lower().replace('.', '')
-    if format not in self.FILE_FORMATS:
-      serializer = self.FILE_FORMATS['default']
-      if len(self.FILE_FORMATS) > 1:
-        log.warning('Unknown file format "%s", using "%s" instead', format,
-                    serializer.__name__)
-    else:
-      serializer = self.FILE_FORMATS[format]
-    return serializer
-
-  def save_to_file(self, cfg, filename, format=None):
-    """
-    Write cfg to filename.  Guess the format by extension if one is not given.
-    """
-    with open(filename, 'a+') as fd:
-      self._get_serializer(filename, format).dump(cfg, fd)
-
-  def load_from_file(self, filename, format=None):
-    """
-    Read cfg from filename.  Guess the format by extension if one is not given.
-    """
-    with open(filename, 'rb') as fd:
-      return self._get_serializer(filename, format).load(fd)
-
-  def proxy(self, cfg):
-    return ManipulatorProxy(self, cfg)
-
-  @abc.abstractmethod
-  def random(self):
-    """produce a random initial configuration"""
-    return
-
-  @abc.abstractmethod
-  def parameters(self, config):
-    """return a list of of Parameter objects"""
-    return list()
-
-  @abc.abstractmethod
-  def hash_config(self, config):
-    """produce unique hash value for the given config"""
-    return
-
-
-class ConfigurationManipulator(ConfigurationManipulatorBase):
-  """
-  a configuration manipulator using a fixed set of parameters and storing
-  configs in a dict-like object
-  """
-
-  def __init__(self, params=None, config_type=dict, seed_config=None, **kwargs):
-    if params is None:
-      params = []
-    self.params = list(params)
-    self.config_type = config_type
-    self.search_driver = None
-    self._seed_config = seed_config
-    super(ConfigurationManipulator, self).__init__(**kwargs)
-    for p in self.params:
-      p.parent = self
-
-  def add_parameter(self, p):
-    p.set_parent(self)
-    self.params.append(p)
-
-    #TODO sub parameters should be recursed on
-    # not currently an issue since no doubly-nested sub-parameters
-    sub_params = p.sub_parameters()
-    for sp in sub_params:
-      sp.set_parent(p)
-    self.params.extend(sub_params)
-
-  def set_search_driver(self, search_driver):
-    self.search_driver = search_driver
-
-  def seed_config(self):
-    """produce a fixed seed configuration"""
-    if self._seed_config:
-      cfg = copy.deepcopy(self._seed_config)
-    else:
-      cfg = self.config_type()
-      for p in self.params:
-        if not isinstance(p.name, str) or '/' not in p.name:
-          cfg[p.name] = p.seed_value()
-    return cfg
-
-  def random(self):
-    """produce a random configuration"""
-    cfg = self.seed_config()
-    for p in self.parameters(cfg):
-      p.op1_randomize(cfg)
-    return cfg
-
-  def parameters(self, config):
-    """return a list of Parameter objects"""
-    if type(config) is not self.config_type:
-      log.error("wrong type, expected %s got %s",
-                str(self.config_type),
-                str(type(config)))
-      raise TypeError()
-    return self.params
-
-  def parameters_to_json(self):
-    """
-    output information about the parameters in this manipulator in json format:
-    [ConfigurationManipulator,{pinfo:count,pinfo:count ...}]
-    where pinfo has a similar form to describe the parameter's sub-parameters:
-    [param_name,{pinfo:count,pinfo:count ...}]
-    """
-    def param_info_to_json(param, sub_parameters):
-      """
-      recursively output information about a parameter and its subparameters in a json format:
-
-      [parameter_name, {subparam_info:count,subparam_info:count,...}]
-      or if no subparams
-      [parameter_name,{}]
-
-      where subparam_info are sorted alphabetically. Note we can't directly use json since
-      sets/dictionaries aren't always ordered by key
-      """
-      sub_parameter_counts = {}
-      # build the string
-      if isinstance(param, str):
-        param_name = param
-      else:
-        param_name = param.__class__.__name__
-      out = ['[', param_name, ',{']
-
-      if len(sub_parameters) > 0:
-        # count sub params
-        for sp in sub_parameters:
-          spout = param_info_to_json(sp, sp.sub_parameters())
-          sub_parameter_counts[spout] = sub_parameter_counts.get(spout, 0) + 1
-        # add the count map in sorted order
-        for sp in sorted(sub_parameter_counts):
-          out.append(sp)
-          out.append(':')
-          out.append(str(sub_parameter_counts[sp]))
-          out.append(',')
-        out.pop() # remove trailing comma
-
-      out.append('}]')
-      return ''.join(out)
-
-    # filter out subparameters to avoid double counting
-    params = [p for p in self.params if p.parent is self]
-    return param_info_to_json(self, params)
-
-  def hash_config(self, config):
-    """produce unique hash value for the given config"""
-    m = hashlib.sha256()
-    params = list(self.parameters(config))
-    params.sort(key=_.name)
-    for i, p in enumerate(params):
-      m.update(str(p.name))
-      m.update(p.hash_value(config))
-      m.update(str(i))
-      m.update("|")
-    return m.hexdigest()
-
-  def search_space_size(self):
-    """estimate the size of the search space, not precise"""
-    return reduce(_ * _, [x.search_space_size() for x in self.params])
-
-  def difference(self, cfg1, cfg2):
-    cfg = self.copy(cfg1)
-    for param in self.parameters(cfg1):
-      if param.is_primitive(cfg1):
-        # TODO: check range
-        param.set_value(cfg, param.get_value(cfg1) - param.get_value(cfg2))
-      else:
-        pass
-    return cfg
-
-  def applySVs(self, cfg, sv_map, args, kwargs):
-    """
-    Apply operators to each parameter according to given map. Updates cfg.
-    Parameters with no operators specified are not updated.
-    cfg: configuration data
-    sv_map: python dict that maps string parameter name to class method name
-    arg_map: python dict that maps string parameter name to class method
-    arguments
-    """
-    # TODO: check consistency between sv_map and cfg
-    param_dict = self.parameters_dict(cfg)
-    for pname in self.param_names(cfg):
-      param = param_dict[pname]
-      getattr(param, sv_map[pname])(cfg, *args[pname], **kwargs[pname])
-
-
-class Parameter(object):
-  """
-  abstract base class for parameters in a ConfigurationManipulator
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self, name):
-    self.name = name
-    self.parent = None
-    super(Parameter, self).__init__()
-
-  def _to_storage_type(self, val):
-    """hook to support transformation applied while stored"""
-    return val
-
-  def _from_storage_type(self, sval):
-    """hook to support transformation applied while stored"""
-    return sval
-
-  def _read_node(self, config):
-    """hook to support different storage structures"""
-    node = config
-    if not isinstance(self.name, str):
-      return node, self.name
-    name_parts = self.name.split('/')
-    for part in name_parts[:-1]:
-      if isinstance(node, list):
-        part = int(part)
-      node = node[part]
-    part = name_parts[-1]
-    if isinstance(node, list):
-      part = int(part)
-    return node, part
-
-  def _get(self, config):
-    """hook to support different storage structures"""
-    node, part = self._read_node(config)
-    return self._from_storage_type(node[part])
-
-  def _set(self, config, v):
-    """hook to support different storage structures"""
-    node, part = self._read_node(config)
-    node[part] = self._to_storage_type(v)
-
-  def set_parent(self, manipulator):
-    self.parent = manipulator
-
-  def validate(self, config):
-    """is the given config valid???"""
-    return True
-
-  def is_primitive(self, ignored=None):
-    return isinstance(self, PrimitiveParameter)
-
-  def is_permutation(self, ignored=None):
-    return isinstance(self, PermutationParameter)
-
-  def manipulators(self, config):
-    """
-    a list of manipulator functions to change this value in the config
-    manipulators must be functions that take a config and change it in place
-
-    default implementation just has op1_randomize as only operation
-    """
-    return [self.op1_randomize]
-
-  def normalize(self, config):
-    """
-    mutate this parameter into a canonical form
-    """
-    pass
-
-  def sub_parameters(self):
-    """
-    additional parameters added with this parameter
-    """
-    return []
-
-  @abc.abstractmethod
-  def op1_randomize(self, cfg):
-    """
-    Set this parameter's value in a configuration to a random value
-
-    :param config: the configuration to be changed
-    """
-    pass
-
-  @abc.abstractmethod
-  def seed_value(self):
-    """some legal value of this parameter (for creating initial configs)"""
-    return
-
-  @abc.abstractmethod
-  def copy_value(self, src, dst):
-    """copy the value of this parameter from src to dst config"""
-    pass
-
-  @abc.abstractmethod
-  def same_value(self, cfg1, cfg2):
-    """test if cfg1 and cfg2 have the same value of this parameter"""
-    return
-
-  @abc.abstractmethod
-  def hash_value(self, config):
-    """produce unique hash for this value in the config"""
-    return
-
-  @abc.abstractmethod
-  def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
-    """
-    Sets the parameter value in a configuration to a linear combination of 3
-    other configurations: :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
-    :param cfg: the configuration to be changed
-    :param cfg_a: a parent configuration
-    :param cfg_b: a parent configuration
-    :param cfg_c: a parent configuration
-    :param a: weight for cfg_a
-    :param b: weight for cfg_b
-    :param c: weight for cfg_c
-    """
-    pass
-
-  def search_space_size(self):
-    return 1
-
-  def op1_nop(self, cfg):
-    """
-    The 'null' operator. Does nothing.
-
-    :param cfg: the configuration to be changed
-    """
-    pass
-
-  # Stochastic variators
-  def op3_swarm(self, cfg, cfg1, cfg2, c, c1, c2, *args, **kwargs):
-    """
-    Stochastically 'move' the parameter value in a configuration towards those
-    in two parent configurations. This is done by calling :py:meth:`opn_stochastic_mix`
-
-    :param cfg: the configuration to be changed
-    :param cfg1: a parent configuration
-    :param cfg2: a parent configuration
-    :param c: weight of original configuration
-    :param c1: weight for cfg1
-    :param c2: weight for cfg2
-    """
-    # default to probabilistic treatment
-    self.opn_stochastic_mix(cfg, [cfg, cfg1, cfg2], [c, c1, c2])
-
-  def opn_stochastic_mix(self, cfg, cfgs, ratio, *args, **kwargs):
-    """
-    Stochastically recombine a list of parent values into a single result.
-
-    This randomly copies a value from a list of parents configurations according
-    to a list of weights.
-
-    :param cfg: the configuration to be changed
-    :param cfgs: a list of parent configurations
-    :param ratio: a list of floats representing the weight of each configuration
-     in cfgs
-
-    """
-    assert len(cfgs) == len(ratio)
-    r = random.random()
-    c = numpy.array(ratio, dtype=float) / sum(ratio)
-    for i in range(len(c)):
-      if r < sum(c[:i + 1]):
-        self.copy_value(cfg, cfgs[i])
-        break
-
-
-class PrimitiveParameter(Parameter):
-  """
-  An abstract interface implemented by parameters that represent a single
-  dimension in a cartesian space in a legal range
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self, name, value_type=float, **kwargs):
-    self.value_type = value_type
-    super(PrimitiveParameter, self).__init__(name, **kwargs)
-
-  def hash_value(self, config):
-    """produce unique hash for this value in the config"""
-    self.normalize(config)
-    return hashlib.sha256(repr(self.get_value(config))).hexdigest()
-
-  def copy_value(self, src, dst):
-    """copy the value of this parameter from src to dst config"""
-    self.set_value(dst, self.get_value(src))
-
-  def same_value(self, cfg1, cfg2):
-    """test if cfg1 and cfg2 have the same value of this parameter"""
-    return self.get_value(cfg1) == self.get_value(cfg2)
-
-  def is_integer_type(self):
-    """true if self.value_type can only represent integers"""
-    return self.value_type(0) == self.value_type(0.1)
-
-  def get_unit_value(self, config):
-    """get_value scaled such that range is between 0.0 and 1.0"""
-    low, high = self.legal_range(config)
-    if self.is_integer_type():
-      # account for rounding
-      low -= 0.4999
-      high += 0.4999
-    val = self.get_value(config)
-    if low < high:
-      return float(val - low) / float(high - low)
-    else:
-      if low > high:
-        log.warning('invalid range for parameter %s, %s to %s',
-                    self.name, low, high)
-      # only a single legal value!
-      return 0.0
-
-  def set_unit_value(self, config, unit_value):
-    """set_value scaled such that range is between 0.0 and 1.0"""
-    assert 0.0 <= unit_value <= 1.0
-    low, high = self.legal_range(config)
-    if self.is_integer_type():
-      # account for rounding
-      low -= 0.4999
-      high += 0.4999
-    if low < high:
-      val = unit_value * float(high - low) + low
-      if self.is_integer_type():
-        val = round(val)
-      val = max(low, min(val, high))
-      self.set_value(config, self.value_type(val))
-
-  def op1_normal_mutation(self, cfg, sigma=0.1, *args, **kwargs):
-    """
-    apply normally distributed noise to this parameter's value in a
-    configuration
-
-    :param cfg: The configuration to be changed
-    :param sigma: the std. deviation of the normally distributed noise on a unit
-     scale
-    """
-    v = self.get_unit_value(cfg)
-    v += random.normalvariate(0.0, sigma)
-    # handle boundary cases by reflecting off the edge
-    if v < 0.0:
-      v *= -1.0
-    if v > 1.0:
-      v = 1.0 - (v % 1)
-    self.set_unit_value(cfg, v)
-
-  def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
-    """
-    set the parameter value in a configuration to a linear combination of 3
-    other configurations: :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
-    :param cfg: The configuration to be changed
-    :param cfg_a: a parent configuration
-    :param cfg_b: a parent configuration
-    :param cfg_c: a parent configuration
-    :param a: weight for cfg_a
-    :param b: weight for cfg_b
-    :param c: weight for cfg_c
-    """
-    va = self.get_unit_value(cfg_a)
-    vb = self.get_unit_value(cfg_b)
-    vc = self.get_unit_value(cfg_c)
-    v = a * va + b * vb + c * vc
-    v = max(0.0, min(v, 1.0))
-
-    self.set_unit_value(cfg, v)
-
-  def manipulators(self, config):
-    """
-    a list of manipulator functions to change this value in the config
-    manipulators must be functions that take a config and change it in place
-
-    for primitive params default implementation is uniform random and normal
-    """
-    return [self.op1_randomize, self.op1_normal_mutation]
-
-  @abc.abstractmethod
-  def set_value(self, config, value):
-    """assign this value in the given configuration"""
-    pass
-
-  @abc.abstractmethod
-  def get_value(self, config):
-    """retrieve this value from the given configuration"""
-    return 0
-
-  @abc.abstractmethod
-  def legal_range(self, config):
-    """return the legal range for this parameter, inclusive"""
-    return 0, 1
-
-
-class NumericParameter(PrimitiveParameter):
-  """
-  A parameter representing a number with a minimum and maximum value
-  """
-  def __init__(self, name, min_value, max_value, **kwargs):
-    """min/max are inclusive"""
-    assert min_value <= max_value
-    super(NumericParameter, self).__init__(name, **kwargs)
-    # after super call so self.value_type is initialized
-    self.min_value = self.value_type(min_value)
-    self.max_value = self.value_type(max_value)
-
-  def seed_value(self):
-    """some legal value of this parameter (for creating initial configs)"""
-    return self.min_value
-
-  def set_value(self, config, value):
-    assert value >= self.min_value
-    assert value <= self.max_value
-    self._set(config, value)
-
-  def get_value(self, config):
-    return self._get(config)
-
-  def legal_range(self, config):
-    return self.min_value, self.max_value
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration to a random value in its legal
-     range
-
-    :param config: the configuration to be changed
-    """
-    if self.is_integer_type():
-      self.set_value(config, random.randint(*self.legal_range(config)))
-    else:
-      self.set_value(config, random.uniform(*self.legal_range(config)))
-
-  def op1_scale(self, cfg, k):
-    """
-    Scale this parameter's value in a configuration by a constant factor
-
-    :param cfg: the configuration to be changed
-    :param k: the constant factor to scale the parameter value by
-    """
-    v = self.get_value(cfg) * k
-    v = max(self.min_value, min(self.max_value, v))
-    self.set_value(cfg, v)
-
-  def op3_difference(self, cfg, cfg1, cfg2):
-    """
-    Set this parameter's value in a configuration to the difference between this
-    parameter's values in 2 other configs (cfg2 - cfg1)
-
-    :param cfg: the configuration to be changed
-    :param cfg1: The configuration whose parameter value is being subtracted
-    :param cfg2: The configuration whose parameter value is subtracted from
-    """
-    v = self.get_value(cfg2) - self.get_value(cfg1)
-    v = max(self.min_value, min(self.max_value, v))
-    self.set_value(cfg, v)
-
-  def opn_sum(self, cfg, *cfgs):
-    """
-    Set this parameter's value in a configuration to the sum of it's values in a
-     list of configurations
-
-    :param cfg: the configuration to be changed
-    :param cfgs: a list of configurations to sum
-    """
-    v = sum([self.get_value(c) for c in cfgs])
-    v = max(self.min_value, min(self.max_value, v))
-    self.set_value(cfg, v)
-
-  def search_space_size(self):
-    if self.value_type is float:
-      return 2 ** 32
-    else:
-      return self.max_value - self.min_value + 1  # inclusive range
-
-
-class IntegerParameter(NumericParameter):
-  """
-  A parameter representing an integer value in a legal range
-  """
-  def __init__(self, name, min_value, max_value, **kwargs):
-    """min/max are inclusive"""
-    kwargs['value_type'] = int
-    super(IntegerParameter, self).__init__(name, min_value, max_value, **kwargs)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
-                c2=0.5, velocity=0, sigma=0.2, *args, **kwargs):
-    """
-    Simulates a single update step in particle swarm optimization by updating
-    the current position and returning a new velocity.
-
-    The new velocity is given by
-
-    .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
-    where r1 and r2 are random values between 0 and 1.
-
-    The new current position is the new velocity with gaussian noise added.
-
-    :param cfg: the configuration to be changed. Represents the current position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param c: the weight of the current velocity
-    :param c1: weight of cfg1
-    :param c2: weight of cfg2
-    :param velocity: the old velocity
-    :param sigma: standard deviation of the gaussian noise, on a unit-scale
-    :return: the new velocity, a float
-
-    """
-    vmin, vmax = self.legal_range(cfg)
-    k = vmax - vmin
-    # calculate the new velocity
-    v = velocity * c + (self.get_value(cfg1) - self.get_value(
-        cfg)) * c1 * random.random() + (self.get_value(
-        cfg2) - self.get_value(cfg)) * c2 * random.random()
-    # Map velocity to continuous space with sigmoid
-    s = k / (1 + numpy.exp(-v)) + vmin
-    # Add Gaussian noise
-    p = random.gauss(s, sigma * k)
-    # Discretize and bound
-    p = int(min(vmax, max(round(p), vmin)))
-    self.set_value(cfg, p)
-    return v
-
-
-class FloatParameter(NumericParameter):
-  def __init__(self, name, min_value, max_value, **kwargs):
-    """min/max are inclusive"""
-    kwargs['value_type'] = float
-    super(FloatParameter, self).__init__(name, min_value, max_value, **kwargs)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
-                c2=0.5, velocity=0, *args, **kwargs):
-    """
-
-    Simulates a single update step in particle swarm optimization by updating
-    the current position and returning a new velocity.
-
-    The new velocity is given by
-
-    .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
-    where r1 and r2 are random values between 0 and 1
-
-    The new current position is the old current position offset by the new
-    velocity:
-
-    :param cfg: the configuration to be changed. Represents the current position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param c: the weight of the current velocity
-    :param c1: weight of cfg1
-    :param c2: weight of cfg2
-    :param velocity: the old velocity
-    :return: the new velocity, a float
-
-    """
-    vmin, vmax = self.legal_range(cfg)
-    v = velocity * c + (self.get_value(cfg1) - self.get_value(
-        cfg)) * c1 * random.random() + (self.get_value(
-        cfg2) - self.get_value(cfg)) * c2 * random.random()
-    p = self.get_value(cfg) + v
-    p = min(vmax, max(p, vmin))
-    self.set_value(cfg, p)
-    return v
-
-
-class ScaledNumericParameter(NumericParameter):
-  """
-  A Parameter that is stored in configurations normally, but has a scaled
-  value when accessed using 'get_value'.
-  Because search techniques interact with Parameters through get_value, these
-  parameters are searched on a different scale (e.g. log scale).
-  """
-
-  @abc.abstractmethod
-  def _scale(self, v):
-    """
-    called on a value when getting it from it's configuration. Transforms the
-    actual value to the scale it is searched on
-    """
-    return v
-
-  @abc.abstractmethod
-  def _unscale(self, v):
-    """
-    called on a value when storing it. Transforms a value from it's search scale
-    to it's actual value
-    """
-    return v
-
-  def set_value(self, config, value):
-    NumericParameter.set_value(self, config, self._unscale(value))
-
-  def get_value(self, config):
-    return self._scale(NumericParameter.get_value(self, config))
-
-  def legal_range(self, config):
-    return map(self._scale, NumericParameter.legal_range(self, config))
-
-
-class LogIntegerParameter(ScaledNumericParameter, FloatParameter):
-  """
-  an integer value that is searched on a log scale, but stored without scaling
-  """
-
-  def _scale(self, v):
-    return math.log(v + 1.0 - self.min_value, 2.0)
-
-  def _unscale(self, v):
-    v = 2.0 ** v - 1.0 + self.min_value
-    v = int(round(v))
-    return v
-
-  def legal_range(self, config):
-    low, high = NumericParameter.legal_range(self, config)
-    # increase the bounds account for rounding
-    return self._scale(low - 0.4999), self._scale(high + 0.4999)
-
-
-class LogFloatParameter(ScaledNumericParameter, FloatParameter):
-  """
-  a float parameter that is searched on a log scale, but stored without scaling
-  """
-
-  def _scale(self, v):
-    return math.log(v + 1.0 - self.min_value, 2.0)
-
-  def _unscale(self, v):
-    v = 2.0 ** v - 1.0 + self.min_value
-    return v
-
-
-class PowerOfTwoParameter(ScaledNumericParameter, IntegerParameter):
-  """
-  An integer power of two, with a min and max value. Searched by the exponent
-  """
-
-  def __init__(self, name, min_value, max_value, **kwargs):
-    kwargs['value_type'] = int
-    assert min_value >= 1
-    assert math.log(min_value, 2) % 1 == 0  # must be power of 2
-    assert math.log(max_value, 2) % 1 == 0  # must be power of 2
-    super(PowerOfTwoParameter, self).__init__(name, min_value, max_value,
-                                              **kwargs)
-
-  def _scale(self, v):
-    return int(math.log(v, 2))
-
-  def _unscale(self, v):
-    return 2 ** int(v)
-
-  def legal_range(self, config):
-    return int(math.log(self.min_value, 2)), int(math.log(self.max_value, 2))
-
-  def search_space_size(self):
-    return int(math.log(super(PowerOfTwoParameter, self).search_space_size(), 2))
-
-
-##################
-
-class ComplexParameter(Parameter):
-  """
-  A non-cartesian parameter that can't be manipulated directly, but has a set
-  of user defined manipulation functions
-  """
-
-  def copy_value(self, src, dst):
-    """copy the value of this parameter from src to dst config"""
-    self._set(dst, copy.deepcopy(self._get(src)))
-
-  def same_value(self, cfg1, cfg2):
-    """test if cfg1 and cfg2 have the same value of this parameter"""
-    return self._get(cfg1) == self._get(cfg2)
-
-  def hash_value(self, config):
-    """produce unique hash for this value in the config"""
-    self.normalize(config)
-    return hashlib.sha256(repr(self._get(config))).hexdigest()
-
-  def get_value(self, config):
-    return self._get(config)
-
-  def set_value(self, config, value):
-    self._set(config, value)
-
-  def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
-    """
-    set this value to :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
-    this operation is not possible in general with complex parameters but
-    we make an attempt to "fake" it for common use cases
-
-    basically a call to randomize unless after normalization,
-    a = 1.0, b == -c, and cfg_b == cfg_c, in which case nothing is done
-
-    :param cfg: the configuration to be changed
-    :param cfg_a: a parent configuration
-    :param cfg_b: a parent configuration
-    :param cfg_c: a parent configuration
-    :param a: weight for cfg_a
-    :param b: weight for cfg_b
-    :param c: weight for cfg_c
-    """
-    # attempt to normalize order, we prefer a==1.0
-    if a != 1.0 and b == 1.0:  # swap a and b
-      a, cfg_a, b, cfg_b = b, cfg_b, a, cfg_a
-    if a != 1.0 and c == 1.0:  # swap a and c
-      a, cfg_a, c, cfg_c = c, cfg_c, a, cfg_a
-
-    # attempt to normalize order, we prefer b==-c
-    if b < c:  # swap b and c
-      b, cfg_b, c, cfg_c = c, cfg_c, b, cfg_b
-    if b != -c and a == -c:  # swap a and c
-      a, cfg_a, c, cfg_c = c, cfg_c, a, cfg_a
-
-    if a == 1.0 and b == -c:
-      self.copy_value(cfg_a, cfg)
-      self.add_difference(cfg, b, cfg_b, cfg_c)  # TODO inline this logic?
-    else:
-      # TODO: should handle more cases
-      self.op1_randomize(cfg)
-
-  def add_difference(self, cfg_dst, scale, cfg_b, cfg_c):
-    """
-    add the difference cfg_b-cfg_c to cfg_dst
-
-    this is the key operation used in differential evolution
-    and some simplex techniques
-
-    this operation is not possible in general with complex parameters but
-    we make an attempt to "fake" it
-    """
-    if not self.same_value(cfg_b, cfg_c):
-      self.op1_randomize(cfg_dst)
-
-  @abc.abstractmethod
-  def op1_randomize(self, config):
-    """
-    randomize this value without taking into account the current position
-    :param config: the configuration to be changed
-    """
-    pass
-
-  @abc.abstractmethod
-  def seed_value(self):
-    """some legal value of this parameter (for creating initial configs)"""
-    return
-
-
-class BooleanParameter(ComplexParameter):
-  def manipulators(self, config):
-    return [self.op1_flip]
-
-  def get_value(self, config):
-    return self._get(config)
-
-  def set_value(self, config, value):
-    self._set(config, value)
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration randomly
-
-    :param config: the configuration to be changed
-    """
-    self._set(config, self.seed_value())
-
-  def seed_value(self):
-    return random.choice((True, False))
-
-  def op1_flip(self, config):
-    """
-    Flip this parameter's value in a configuration
-
-    :param config: the configuration to be changed
-    """
-    self._set(config, not self._get(config))
-
-  def search_space_size(self):
-    return 2
-
-  def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
-                c2=0.5, velocity=0, *args, **kwargs):
-    """
-    Simulates a single update step in particle swarm optimization by updating
-    the current position and returning a new velocity.
-
-    The new velocity is given by
-
-    .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
-    where r1 and r2 are random values between 0 and 1
-
-    The new current position is randomly chosen based on the new velocity
-
-    :param cfg: the configuration to be changed. Represents the current position
-    :param cfg1: a configuration to shift towards. Should be the local best position
-    :param cfg2: a configuration to shift towards. Should be the global best position
-    :param c: the weight of the current velocity
-    :param c1: weight of cfg1
-    :param c2: weight of cfg2
-    :param velocity: the old velocity
-    :param args:
-    :param kwargs:
-    :return: the new velocity, a float
-
-    """
-    v = velocity * c + (self.get_value(cfg1) - self.get_value(
-        cfg)) * c1 * random.random() + (self.get_value(
-        cfg2) - self.get_value(cfg)) * c2 * random.random()
-    # Map velocity to continuous space with sigmoid
-    s = 1 / (1 + numpy.exp(-v))
-    # Decide position randomly
-    p = (s - random.random()) > 0
-    self.set_value(cfg, p)
-    return v
-
-
-class SwitchParameter(ComplexParameter):
-  """
-  A parameter representing an unordered collection of options with no implied
-  correlation between the choices. The choices are range(option_count)
-  """
-
-  def __init__(self, name, option_count):
-    self.option_count = option_count
-    super(SwitchParameter, self).__init__(name)
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration to a random value
-
-    :param config: the configuration to be changed
-    """
-    self._set(config, random.randrange(self.option_count))
-
-  def seed_value(self):
-    return random.randrange(self.option_count)
-
-  def search_space_size(self):
-    return max(1, self.option_count)
-
-
-class EnumParameter(ComplexParameter):
-  """
-  same as a SwitchParameter but choices are taken from an arbitrarily typed list
-  """
-
-  def __init__(self, name, options):
-    super(EnumParameter, self).__init__(name)
-    self.options = list(options)
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration to a random value
-
-    :param config: the configuration to be changed
-    """
-    self._set(config, random.choice(self.options))
-
-  def seed_value(self):
-    return random.choice(self.options)
-
-  def search_space_size(self):
-    return max(1, len(self.options))
-
-
-class PermutationParameter(ComplexParameter):
-  """
-  A parameter representing a permutation (or ordering) as a list of items
-  """
-  def __init__(self, name, items):
-    super(PermutationParameter, self).__init__(name)
-    self._items = list(items)
-    self.size = len(items)
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration to a random value
-
-    :param config: the configuration to be changed
-    """
-    random.shuffle(self._get(config))
-    self.normalize(config)
-
-  def op1_small_random_change(self, config, p=0.25):
-    """
-    Iterates through the list and probabilistically swaps each element with the
-    next element
-
-    :param p: probability of swapping an element with the next element
-    :param config: the configuration to be changed
-    """
-    cfg_item = self._get(config)
-    for i in xrange(1, len(cfg_item)):
-      if random.random() < p:
-        # swap
-        cfg_item[i - 1], cfg_item[i] = cfg_item[i], cfg_item[i - 1]
-    self.normalize(config)
-
-  def seed_value(self):
-    return list(self._items)  # copy
-
-  def manipulators(self, config):
-    return [self.op1_randomize, self.op1_small_random_change]
-
-  def get_value(self, config):
-    return self._get(config)
-
-  def set_value(self, config, value):
-    self._set(config, value)
-
-  def search_space_size(self):
-    return math.factorial(max(1, len(self._items)))
-
-  def op3_cross(self, cfg, cfg1, cfg2, xchoice='op3_cross_OX1', strength=0.3,
-                *args, **kwargs):
-    """
-    Calls the crossover operator specified by xchoice
-    Passes argument d = strength*(size of the permutation)
-
-    :param cfg: the configuration to be changed
-    :param cfg1: a parent configuration
-    :param cfg2: a parent configuration
-    :param xchoice: string specifying which crossover operator to use (should start with op3_cross prefix)
-    :param strength: the strength of the crossover
-    """
-    dd = int(round(self.size * strength))
-    if dd < 1:
-      log.warning('Crossover length too small. Cannot create new solution.')
-    if dd >= self.size:
-      log.warning('Crossover length too big. Cannot create new solution.')
-    getattr(self, xchoice)(cfg, cfg1, cfg2, d=dd, *args, **kwargs)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, xchoice='op3_cross_OX1', c=0.5,
-                c1=0.5, c2=0.5, strength=0.3, velocity=0, *args, **kwargs):
-    """
-    Replacement for particle swarm optimization iterative step for permutations.
-    Given a target cfg and 2 parent cfgs, probabilistically performs an
-    op3_cross with one of the 2 parents.
-
-    :param cfg: the configuration to be changed. Represents the current position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param xchoice: which crossover operator should be used
-    :param c: the probability of not performing a crossover
-    :param c1: the probability of performing a crossover with cfg1 (if a
-     crossover is performed)
-    :param c2: unused
-    :param strength: the strength of the crossover
-    :param velocity: the old velocity - unused
-    """
-    if random.uniform(0, 1) > c:
-      if random.uniform(0, 1) < c1:
-        # Select crossover operator
-        self.op3_cross(cfg, cfg, cfg1, xchoice, strength)
-      else:
-        self.op3_cross(cfg, cfg, cfg2, xchoice, strength)
-
-  # swap-based operators
-  def op2_random_swap(self, cfg, cfg1, *args, **kwargs):
-    """
-    Swap a random pair of items in cfg1 and save the result into cfg
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the configuration whose PermutationParameter's elements are
-     swapped and copied into cfg
-    """
-    p = self.get_value(cfg1)[:]
-    r = random.randint(0, len(p) - 1)
-    s = random.randint(0, len(p) - 1)
-    v1 = p[r]
-    v2 = p[s]
-    p[r] = v2
-    p[s] = v1
-    self.set_value(cfg, p)
-
-  def op2_random_invert(self, cfg, cfg1, strength=0.3, *args, **kwargs):
-    """
-    Reverse the ordering of a random subsection of size d in cfg1 and save the
-    result in cfg where d = strength*total-size
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the configuration whose PermutationParameter is inverted
-    :param strength: the size of the reversed subsection as a fraction of the
-     total size
-    """
-    p = self.get_value(cfg1)[:]
-    d = int(round(len(p) * strength))
-    r = random.randint(0, len(p) - d)
-    subpath = p[r:r + d][:]
-    subpath.reverse()
-    p[r:r + d] = subpath
-    self.set_value(cfg, p)
-
-  # Crossover operators
-  def op3_cross_PX(self, cfg, cfg1, cfg2, d=0):
-    """
-    Partition crossover (Whitley 2009?)
-
-    Chooses a random cut point and reorders elements in cfg1 up to the cut point
-    according to their order in cfg2.
-
-    Saves the result in cfg
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the first parent configuration. The "base" configuration
-    :param cfg2: the second parent configuration. Is "crossed into" cfg1
-    :param d: unused
-    """
-    p1 = self.get_value(cfg1)
-    p2 = self.get_value(cfg2)
-    c1 = random.randint(2, len(p1))
-    self.set_value(cfg, sorted(p1[:c1], key=lambda x: p2.index(x)) + p1[c1:])
-
-  def op3_cross_PMX(self, cfg, cfg1, cfg2, d=0):
-    """
-    Partially-mapped crossover Goldberg & Lingle (1985)
-
-    Replaces a random section of cfg1 with the corresponding section in cfg2.
-    Displaced elements in cfg1 are moved to the old position of the elements
-    displacing them
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the first parent configuration. The "base" configuration
-    :param cfg2: the second parent configuration. Is "crossed into" cfg1
-    :param d: the size of the crossover
-    """
-    if d == 0:
-      d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
-    p1 = self.get_value(cfg1)[:]
-    p2 = self.get_value(cfg2)[:]
-
-    r = random.randint(0, len(p1) - d)
-
-    c1 = p1[r:r + d]
-    c2 = p2[r:r + d]
-
-    # get new permutation by crossing over a section of p2 onto p1
-    pnew = self.get_value(cfg1)[:]
-    pnew[r:r + d] = c2
-    # fix conflicts by taking displaced elements in crossed over section
-    # displaced = (elements x in c1 where x does not have corresponding value in c2)
-    # and putting them where the value that displaced them was
-
-    #candidates for displacement
-    candidate_indices = set(range(r) + range(r+d, len(p1)))
-    # Check through displaced elements to find values to swap conflicts to
-    while c1 != []:
-      n = c1[0]
-      #try to match up a value in c1 to the equivalent value in c2
-      while c2[0] in c1:
-        if n == c2[0]:
-          # already match up
-          break
-        # find position idx of c2[0] in c1
-        link_idx = c1.index(c2[0])
-        # get value of c2 at idx
-        link = c2[link_idx]
-        # remove c2[idx] and c1[idx] since they match up when we swap c2[0] with c2[idx] (this avoids an infinite loop)
-        del c2[link_idx]
-        del c1[link_idx]
-        # swap new value into c2[0]
-        c2[0] = link
-
-      if n != c2[0]:
-        # first check if we can swap in the crossed over section still
-        if n in c2:
-          c2[c2.index(n)] = c2[0]
-        else:
-          # assign first instance of c2[0] outside of the crossed over section in pnew to c1[0]
-          for idx in candidate_indices:
-            if pnew[idx] == c2[0]:
-              pnew[idx] = c1[0]
-              candidate_indices.remove(idx) # make sure we don't override this value now
-              break
-      # remove first elements
-      del c1[0]
-      del c2[0]
-    self.set_value(cfg, pnew)
-
-  def op3_cross_CX(self, cfg, cfg1, cfg2, d=0):
-    """
-    Implementation of a cyclic crossover.
-
-    Repeatedly replaces elements of cfg1 with the element at the same index in
-    cfg2. This is done until a cycle is reached and cfg1 is valid again. The
-    initial replacement is random.
-
-    Saves the result in cfg.
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the first parent configuration. The "base" configuration
-    :param cfg2: the second parent configuration. Is "crossed into" cfg1
-    :param d: unused
-    """
-    p1 = self.get_value(cfg1)
-    p2 = self.get_value(cfg2)
-    p = p1[:]
-
-    s = random.randint(0, len(p1) - 1)
-    i = s
-    indices = set()
-
-    while len(indices) < len(p1): # should never exceed this
-      indices.add(i)
-      val = p1[i]
-      i = p2.index(val)
-      # deal with duplicate values
-      while i in indices:
-        if i == s:
-          break
-        i = p2[i+1:].index(val) + i + 1
-      if i == s:
-        break
-
-    for j in indices:
-      p[j] = p2[j]
-
-    self.set_value(cfg, p)
-
-  def op3_cross_OX1(self, cfg, cfg1, cfg2, d=0):
-    """
-    Ordered Crossover (Davis 1985)
-
-    Exchanges a subpath from cfg2 into cfg1 while maintaining the order of the
-    remaining elements in cfg1.
-
-    Saves the result in cfg.
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the first parent configuration. The "base" configuration
-    :param cfg2: the second parent configuration. Is "crossed into" cfg1
-    :param d: size of the exchanged subpath
-    """
-    if d == 0:
-      d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
-    p1 = self.get_value(cfg1)
-    p2 = self.get_value(cfg2)
-    c1 = p1[:]
-    c2 = p2[:]
-    # Randomly find cut points
-    r = random.randint(0, len(
-        p1) - d)  # Todo: treat path as circle i.e. allow cross-boundary cuts
-    [c1.remove(i) for i in p2[r:int(r + d)]]
-    self.set_value(cfg, c1[:r] + p2[r:r + d] + c1[r:])
-
-  def op3_cross_OX3(self, cfg, cfg1, cfg2, d=0):
-    """
-    Ordered crossover variation 3 (Deep 2010)
-
-    Same as op3_cross_OX1, except the parents have different cut points for
-    their subpaths
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the first parent configuration. The "base" configuration
-    :param cfg2: the second parent configuration. Is "crossed into" cfg1
-    :param d: size of the exchanged subpath
-    """
-    if d == 0:
-      d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
-    p1 = self.get_value(cfg1)
-    p2 = self.get_value(cfg2)
-    c1 = p1[:]
-    c2 = p2[:]
-    # Randomly find cut points
-    # Todo: treat path as circle i.e. allow cross-boundary cuts
-    r1 = random.randint(0, len(p1) - d)
-    r2 = random.randint(0, len(p1) - d)
-    [c1.remove(i) for i in p2[r2:r2 + d]]
-    self.set_value(cfg, c1[:r1] + p2[r2:r2 + d] + c1[r1:])
-
-  def search_space_size(self):
-    return math.factorial(max(1, len(self._items)))
-
-
-class ScheduleParameter(PermutationParameter):
-  def __init__(self, name, items, deps):
-    super(ScheduleParameter, self).__init__(name, items)
-    self.deps = dict((k, set(v)) for k, v in deps.items())
-    log.debug("ScheduleParameter(%s, %s, %s)", repr(name), repr(items),
-              repr(deps))
-    self._expand_deps()
-
-  def _expand_deps(self):
-    """expand self.deps to include recursive dependencies"""
-    fixed_point = False
-    while not fixed_point:
-      fixed_point = True
-      for k in self.deps.keys():
-        oldlen = len(self.deps[k])
-        for dep in list(self.deps[k]):
-          if dep in self.deps:
-            self.deps[k].update(self.deps[dep])
-        if oldlen != len(self.deps[k]):
-          fixed_point = False
-
-    # verify schedule is valid
-    items = set(self._items)
-    for k, v in self.deps.items():
-      if k in v:
-        raise Exception("ScheduleParameter('%s') cycle: %s depends on itself" %
-                        (self.name, k))
-
-      if v - items:
-        raise Exception("ScheduleParameter('%s'): %s is unknown" %
-                        (self.name, v - items))
-
-    if set(self.deps.keys()) - items:
-      raise Exception("ScheduleParameter('%s'): %s is unknown" %
-                      (self.name, set(self.deps.keys()) - items))
-
-  def is_topologically_sorted(self, values):
-    used = set()
-    for v in values:
-      if v in self.deps and self.deps[v].union(used):
-        return False
-      used.add(v)
-    return True
-
-  def topologically_sorted_depth_first(self, values):
-    """faster but not stable enough"""
-    if self.is_topologically_sorted(values):
-      return values
-    sorted_values = []
-    used = set()
-    deps = dict((k, sorted(v, key=values.index, reverse=True))
-                for k, v in self.deps.items())
-
-    def visit(v):
-      if v in used:
-        return
-      if v in deps:
-        for dv in deps[v]:
-          visit(dv)
-      used.add(v)
-      sorted_values.append(v)
-
-    for v in reversed(values):
-      visit(v)
-    return list(reversed(sorted_values))
-
-  def topologically_sorted(self, values):
-    if self.is_topologically_sorted(values):
-      return values
-    deps = copy.deepcopy(self.deps)
-    queue = collections.deque(reversed(values))
-    sorted_values = []
-    while queue:
-      v = queue.popleft()
-      if v in deps and deps[v]:
-        queue.append(v)
-      else:
-        for k, d in deps.items():
-          d.discard(v)
-          if not d:
-            del deps[k]
-        sorted_values.append(v)
-
-    return list(reversed(sorted_values))
-
-  def normalize(self, cfg):
-    self._set(cfg, self.topologically_sorted(self._get(cfg)))
-
-
-class SelectorParameter(ComplexParameter):
-  def __init__(self, name, choices, max_cutoff,
-               order_class=PermutationParameter,
-               offset_class=LogIntegerParameter):
-    super(SelectorParameter, self).__init__(name)
-    self.choices = choices
-    self.max_cutoff = max_cutoff
-    self.order_param = order_class('{0}/order'.format(name), choices)
-    self.offset_params = [
-        offset_class('{0}/offsets/{1}'.format(name, i), 0, max_cutoff)
-        for i in xrange(len(choices) - 1)]
-
-  def sub_parameters(self):
-    return [self.order_param] + self.offset_params
-
-  def seed_value(self):
-    return {'order': self.order_param.seed_value(),
-            'offsets': [co.seed_value() for co in self.offset_params]}
-
-  def op1_randomize(self, config):
-    random.choice(self.sub_parameters()).op1_randomize(config)
-
-  def selector_iter(self, config):
-    """
-    yield (cutoff, choice) pairs
-    cutoff will be None on the first value
-    """
-    order = config[self.name]['order']
-    yield (None, order[0])
-    cutoff = 0
-    for n, offset in enumerate(config[self.name]['offsets']):
-      if offset > 0:
-        cutoff += offset
-        yield cutoff, order[n + 1]
-
-
-class ParameterArray(ComplexParameter):
-  """
-  Represents an array of Parameters
-  """
-  def __init__(self, name, count, element_type, *args, **kwargs):
-    super(ParameterArray, self).__init__(name)
-    self.count = count
-
-    self.sub_params = [
-        element_type('{0}/{1}'.format(name, i), *args[i], **kwargs[i])
-        for i in xrange(count)]
-
-  def sub_parameters(self):
-    return self.sub_params
-
-  def seed_value(self):
-    return [p.seed_value() for p in self.sub_params]
-
-  def op1_randomize(self, config):
-    """
-    randomly selects a sub-parameter and randomizes it
-
-    :param config: the configuration to be changed
-    """
-    random.choice(self.sub_parameters()).op1_randomize(config)
-
-
-class BooleanParameterArray(ParameterArray):
-  """
-  Represents an array of BooleanParameters - currently unimplimented
-  """
-  def __init__(self, name, count):
-    super(BooleanParameterArray, self).__init__(name, count, BooleanParameter)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, *args, **kwargs):
-    # TODO
-    pass
-
-  def op3_cross(self, cfg, cfg1, cfg2, *args, **kwargs):
-    # TODO
-    pass
-
-
-class IntegerParameterArray(ParameterArray):
-  """
-  Represents an array of IntegerParameters - currently unimplemented
-  """
-  def __init__(self, name, min_values, max_values):
-    assert len(min_values) == len(max_values)
-    super(IntegerParameterArray, self).__init__(name, len(min_values),
-                                                IntegerParameter,
-                                                min_value=min_values,
-                                                max_value=max_values)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, *args, **kwargs):
-    # TODO
-    pass
-
-  def op3_cross(self, cfg, cfg1, cfg2, *args, **kwargs):
-    # TODO
-    pass
-
-
-class Array(ComplexParameter):
-  """
-  An interface for parameters representing an array of values.
-  """
-  # TODO: constraints? (upper & lower bound etc)
-  def __init__(self, name, size):
-    super(Array, self).__init__(name)
-    self.size = size
-
-  def op3_cross(self, cfg, cfg1, cfg2, strength=0.3, *args, **kwargs):
-    """
-    Crosses two arrays by replacing a random subsection of cfg1 with the
-    corresponding subsection of cfg2.The size of the chunk is a fixed fraction
-    of the total length, given by the strength
-
-    Behaves like a specialized 2-point crossover, where the first cut point is
-    random and the second cut is a set distance after.
-
-    :param cfg: the configuration to be changed
-    :param cfg1: the configuration being inserted into
-    :param cfg2: the configuration being inserted
-    :param strength: the size of the crossover, as a fraction of total array
-     length
-    """
-    d = int(round(self.size * strength))
-    if d < 1:
-      log.debug('Crossover length too small. Cannot create new solution.')
-    if d >= self.size:
-      log.debug('Crossover length too big. Cannot create new solution.')
-    p1 = self.get_value(cfg1)
-    p2 = self.get_value(cfg2)
-    r = random.randint(0, len(
-        p1) - d)  # Todo: treat path as circle i.e. allow cross-boundary cuts
-    p = numpy.concatenate([p1[:r], p2[r:r + d], p1[r + d:]])
-    self.set_value(cfg, p)
-
-  def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
-                c2=0.5, velocity=0, strength=0.3, *args, **kwargs):
-    """
-    Replacement for a particle swarm optimization iterative step for arrays.
-    Given a target cfg and 2 parent cfgs, probabilistically performs an
-    :py:meth:`op3_cross` with one of the 2 parents.
-
-    :param cfg: the configuration to be changed. Represents the cfg position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param c: the probability of not performing a crossover
-    :param c1: the probability of performing a crossover with cfg1 (if a
-     crossover is performed)
-    :param c2: unused
-    :param velocity: the old velocity - unused
-    :param strength: the strength of the crossover
-    """
-    if random.uniform(0, 1) > c:
-      if random.uniform(0, 1) < c1:
-        # Select crossover operator
-        self.op3_cross(cfg, cfg, cfg1, strength)
-      else:
-        self.op3_cross(cfg, cfg, cfg2, strength)
-
-  def get_value(self, config):
-    return self._get(config)
-
-  def set_value(self, config, value):
-    self._set(config, value)
-
-
-class BooleanArray(Array):
-  """
-  Represents an array of boolean values which are either 0 or 1
-  """
-  def op3_swarm_parallel(self, cfg, cfg1, cfg2, c=1,
-                         c1=0.5, c2=0.5, velocities=0):
-    """
-    Simulates a single particle swarm optimization step for each element in the
-    array by updating each position and returning an array of new velocities.
-
-    The new velocities are given by
-
-    .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
-    where r1 and r2 are random values between 0 and 1. In each iteration, r1 and
-    r2 are constant across array elements
-
-    The new cfg positions are randomly chosen based on the new velocities
-
-    :param cfg: the configuration to be changed. This represents the current
-     position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param c: the weight of the current velocities
-    :param c1: weight of cfg1
-    :param c2: weight of cfg2
-    :param velocities: the current velocities
-    :return: a numpy array of new velocities
-    """
-    vs = velocities * c + (self.get_value(cfg1) - self.get_value(
-        cfg)) * c1 * random.random() + (self.get_value(
-            cfg2) - self.get_value(cfg)) * c2 * random.random()
-    # Map velocity to continuous space with sigmoid
-    ss = 1 / (1 + numpy.exp(-vs))
-    # Decide position randomly
-    ps = (ss - numpy.random.rand(1, self.size)) > 0
-    self.set_value(cfg, ps)
-    return vs
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration randomly
-
-    :param config: the configuration to be changed
-    """
-    value = numpy.random.rand(1, self.size) > 0.5
-    self._set(config, value)
-
-  def seed_value(self):
-    return numpy.random.rand(1, self.size) > 0.5
-
-
-class FloatArray(Array):
-  """
-  Represents an array of float values
-  """
-  def __init__(self, name, size, fmax, fmin):
-    super(FloatArray, self).__init__(name, size)
-    self.fmax = fmax
-    self.fmin = fmin
-
-  def op1_randomize(self, config):
-    """
-    Set this parameter's value in a configuration randomly
-
-    :param config: the configuration to be changed
-    """
-    value = numpy.random.rand(1, self.size) * (
-        self.fmax - self.fmin) + self.fmin
-    self._set(config, value)
-
-  def seed_value(self):
-    value = numpy.random.rand(1, self.size) * (
-        self.fmax - self.fmin) + self.fmin
-    return value
-
-  def op3_swarm_parallel(self, cfg, cfg1, cfg2, c=1,
-                         c1=0.5, c2=0.5, velocities=0):
-    """
-    Simulates a single particle swarm optimization step for each element in the
-    array by updating the each position and returning an array of new velocities
-
-    The new velocity is given by
-
-    .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
-    where r1 and r2 are random values between 0 and 1. In each iteration, r1 and
-    r2 are constant across array elements
-
-    The new cfg positions are randomly chosen based on the new velocities
-
-    :param cfg: the configuration to be changed. This represents the current
-     position
-    :param cfg1: a configuration to shift towards. Should be the local best
-     position
-    :param cfg2: a configuration to shift towards. Should be the global best
-     position
-    :param c: the weight of the cfg velocities
-    :param c1: weight of cfg1
-    :param c2: weight of cfg2
-    :param velocities: the cfg velocities
-    :return: a numpy array of new velocities
-    """
-    vs = velocities * c + (self.get_value(cfg1) - self.get_value(
-        cfg)) * c1 * random.random() + (self.get_value(
-        cfg2) - self.get_value(cfg)) * c2 * random.random()
-    p = self.get_value(cfg) + vs
-    p[p > self.fmax] = self.fmax
-    p[p < self.fmin] = self.fmin
-    self.set_value(cfg, p)
-    return vs
-
-
-##################
-
-class ManipulatorProxy(object):
-  """
-  wrapper around configuration manipulator and config pair
-  """
-
-  def __init__(self, manipulator, cfg):
-    self.cfg = cfg
-    self.manipulator = manipulator
-    self.params = manipulator.parameters_dict(self.cfg)
-
-  def keys(self):
-    return self.params.keys()
-
-  def __getitem__(self, k):
-    return ParameterProxy(self.params[k], self.cfg)
-
-
-class ParameterProxy(object):
-  """
-  wrapper aint parameter and config pair, adds config
-  as first argument to all method calls to parameter
-  """
-
-  def __init__(self, param, cfg):
-    self.cfg = cfg
-    self.param = param
-
-  def __getattr__(self, key):
-    """equivalent of self.param.key(self.cfg, ...)"""
-    member = getattr(self.param, key)
-
-    def param_method_proxy(*args, **kwargs):
-      return member(self.cfg, *args, **kwargs)
-
-    if callable(member):
-      return param_method_proxy
-    else:
-      # we should only hit this for key == 'name'
-      return member
-
-
-# Inspection Methods
-def operators(param, num_parents):
-  """
-  Return a list of operators for the given parameter that take the specified
-  number of input configurations
-
-  :param param: a Parameter class
-  :param num_parents: a String specifying number of inputs required by the operator.
-    should be one of '1', '2', '3', '4', or 'n'
-  """
-  ops = []
-  methods = inspect.getmembers(param, inspect.ismethod)
-  for m in methods:
-    name, obj = m
-    if is_operator(name, num_parents):
-      ops.append(name)
-  return ops
-
-def composable_operators(param, min_num_parents):
-  """
-  Return a list of operators for the given parameter that can be programatically composed
-  with a composable technique generating min_num_parents.
-
-  Programatically composable operators have no non-cfg arguments
-
-  :param param: a Parameter class
-  :param min_num_parents: the minimum number of parents passed to the operator
-  """
-  if min_num_parents < 1:
-    return []
-
-  allowed_num_parents = ['n']
-  for i in range(1,5):
-    if i > min_num_parents:
-      break
-    allowed_num_parents.append(str(i))
-
-  ops = []
-  methods = inspect.getmembers(param, inspect.ismethod)
-  for m in methods:
-    name, obj = m
-    argspec = inspect.getargspec(obj)
-    numargs = len(argspec.args) - (len(argspec.defaults) if argspec.defaults else 0)
-    for num_parents in allowed_num_parents:
-      if is_operator(name, num_parents):
-        if num_parents == 'n':
-          if numargs == 3: # self, cfg, cfgs
-            ops.append(name)
-        else:
-          if numargs == (1 + int(num_parents)):
-            ops.append(name)
-        break
-  return ops
-
-
-def is_operator(name, num_parents):
-  """
-  Tells whether a method is an operator taking in the specified number of inputs
-  from the method name
-
-  :param name: the method name
-  :param num_parents: a String specifying number of inputs required by the operator.
-    should be one of '1', '2', '3', '4', or 'n'
-  """
-  return ('op' + num_parents + '_') == name[:4]
-
-def all_operators():
-  """
-  Return a dictionary mapping from parameter names to lists of operator function
-  names
-  """
-  ops = {}
-  for p in all_params():
-    name, obj = p
-    all_ops = []
-    for num in ['1', '2', '3', '4', 'n']:
-      all_ops += operators(obj, num)
-    ops[name] = all_ops
-  return ops
-
-def all_params():
-  params = inspect.getmembers(sys.modules[__name__], lambda x: inspect.isclass(
-    x) and x.__module__ == __name__ and issubclass(x, Parameter))
-  return params
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/metatechniques.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/metatechniques.py
deleted file mode 100644
index 2e33e7961ab2d7f9b16ea48cb680dd751af32d7a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/metatechniques.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import abc
-import logging
-from collections import deque, defaultdict
-from fn import _
-
-from .technique import SearchTechniqueBase
-
-log = logging.getLogger(__name__)
-
-class MetaSearchTechnique(SearchTechniqueBase):
-  """
-  a technique made up of a collection of other techniques
-  """
-  def __init__(self, techniques, log_freq = 500, *pargs, **kwargs):
-    super(MetaSearchTechnique, self).__init__(*pargs, **kwargs)
-    self.techniques = techniques
-    self.request_count = 0
-    self.log_freq = log_freq
-    self.logging_use_counters = defaultdict(int)
-    self.unique_names()
-
-  def unique_names(self):
-    names = set()
-    for t in self.techniques:
-      while t.name in names:
-        t.name += '~'
-      t.name = intern(t.name)
-      names.add(t.name)
-
-  def set_driver(self, driver):
-    super(MetaSearchTechnique, self).set_driver(driver)
-    for t in self.techniques:
-      t.set_driver(driver)
-    self.driver = driver
-
-  def desired_result(self):
-    techniques = self.select_technique_order()
-    for technique in techniques:
-      dr = technique.desired_result()
-      if dr is not None:
-        if dr is False:
-          # technique is waiting for results
-          continue
-        self.driver.register_result_callback(dr,
-            lambda result: self.on_technique_result(technique, result))
-        if self.log_freq:
-          self.logging_use_counters[technique.name] += 1
-          self.debug_log()
-        self.request_count += 1
-        return dr
-      else:
-        self.on_technique_no_desired_result(technique)
-    return None
-
-  def on_technique_no_desired_result(self, technique):
-    """called if a sub-technique returns None"""
-    pass
-
-  def on_technique_result(self, technique, result):
-    """callback for results of sub-techniques"""
-    pass
-
-  @abc.abstractmethod
-  def select_technique_order(self):
-    """select the order of next techniques to try"""
-    return []
-
-  def debug_log(self):
-    if self.log_freq and sum(self.logging_use_counters.values())>self.log_freq:
-      log.info("%s: %s", self.name,
-          str(sorted(self.logging_use_counters.items(), key = _[1]*-1)))
-      self.logging_use_counters = defaultdict(int)
-
-class RoundRobinMetaSearchTechnique(MetaSearchTechnique):
-  """evenly switch between all source techniques"""
-  def __init__(self, techniques, **kwargs):
-    techniques = deque(techniques)
-    super(RoundRobinMetaSearchTechnique, self).__init__(techniques, **kwargs)
-
-  def select_technique_order(self):
-    rv = list(self.techniques)
-    self.techniques.rotate(1)
-    return rv
-
-class RecyclingMetaTechnique(MetaSearchTechnique):
-  """
-  periodically restart techniques that are not performing well compared to
-  global best
-  """
-  def __init__(self,
-               techniques_generators,
-               window = 100,
-               factor = 5.0,
-               **kwargs):
-    if 'log_freq' not in kwargs:
-      kwargs['log_freq'] = None
-    techniques = deque((g(seed_cfg = None) for g in techniques_generators))
-    self.rename_i = 0
-    for t in techniques:
-      self.rename_technique(t)
-    super(RecyclingMetaTechnique, self).__init__(techniques, **kwargs)
-    self.best_results = defaultdict(lambda: None)
-    self.factor = factor
-    self.last_check = 0
-    self.old_best_results = defaultdict(lambda: None)
-    self.technique_generators = deque(techniques_generators)
-    self.window = window
-
-  def rename_technique(self, technique):
-    technique.name += ".R%d" % self.rename_i
-    self.rename_i += 1
-
-  def on_technique_result(self, technique, result):
-    """callback for results of sub-techniques"""
-    if (self.best_results[technique] is None or
-        self.driver.objective.lt(result, self.best_results[technique])):
-      self.best_results[technique] = result
-
-  def technique_cmp(self, a, b):
-  # a1 = self.old_best_results[a]
-  # a2 = self.best_results[a]
-  # b1 = self.old_best_results[b]
-  # b2 = self.best_results[b]
-  # if a1 is None and b1 is None:
-  #   return 0
-  # if a1 is None:
-  #   return -1
-  # if b1 is None:
-  #   return 1
-  # return self.driver.objective.project_compare(a1, a2, b1, b2, self.factor)
-
-    # not ready techniques go to the back
-    if not a.is_ready() or not b.is_ready():
-      return cmp(b.is_ready(), a.is_ready())
-
-    a = self.best_results[a]
-    b = self.best_results[b]
-    if a is None and b is None:
-      return 0
-    if a is None:
-      return -1
-    if b is None:
-      return 1
-    return self.driver.objective.compare(a, b)
-
-  def recycle_techniques(self):
-    techniques = list(self.techniques)
-    techniques.sort(cmp=self.technique_cmp)
-    worst = techniques[-1]
-
-    if (not worst.is_ready()
-        or (self.old_best_results[worst] is not None
-            and self.driver.objective.lt(self.driver.best_result,
-                                         self.best_results[worst]))):
-      techniques_new = deque()
-      tn = None
-      for t, gen in zip(self.techniques, self.technique_generators):
-        if t is worst:
-          tn = gen(seed_cfg=self.driver.best_result.configuration.data)
-          self.rename_technique(tn)
-          tn.set_driver(self.driver)
-          log.info("%s replacing %s with %s", self.name, t.name, tn.name)
-          techniques_new.append(tn)
-        else:
-          techniques_new.append(t)
-      self.techniques = techniques_new
-    else:
-      log.debug("%s: not replacing techniques", self.name)
-
-    self.old_best_results = self.best_results
-    self.best_results = defaultdict(lambda: None)
-    for t in self.techniques:
-      self.best_results[t] = self.old_best_results[t]
-
-  def select_technique_order(self):
-    """
-    round robin between techniques
-    """
-    if self.last_check + self.window < self.request_count:
-      self.last_check = self.request_count
-      self.recycle_techniques()
-    rv = list(self.techniques)
-    self.techniques.rotate(1)
-    self.technique_generators.rotate(1)
-    return rv
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/objective.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/objective.py
deleted file mode 100644
index b46a2f54b2f0922f774548c1c2d009ffa581512e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/objective.py
+++ /dev/null
@@ -1,338 +0,0 @@
-import abc
-import logging
-
-from fn import _
-
-import opentuner
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-
-class SearchObjective(object):
-  """
-  delegates the comparison of results and configurations
-  """
-  __metaclass__ = abc.ABCMeta
-
-  @abc.abstractmethod
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return []
-
-  @abc.abstractmethod
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return
-
-  def config_compare(self, config1, config2):
-    """cmp() compatible comparison of resultsdb.models.Configuration"""
-    return self.result_compare(self.driver.results_query(config=config1).one(),
-                               self.driver.results_query(config=config2).one())
-
-  @abc.abstractmethod
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    return
-
-  def config_relative(self, config1, config2):
-    """return None, or a relative goodness of resultsdb.models.Configuration"""
-    return self.result_relative(self.driver.results_query(config=config1).one(),
-                                self.driver.results_query(config=config2).one())
-
-
-  def __init__(self):
-    self.driver = None
-
-  def set_driver(self, driver):
-    self.driver = driver
-
-  def result_order_by(self, q):
-    return q.order_by(*self.result_order_by_terms())
-
-  def compare(self, a, b):
-    """cmp() compatible compare"""
-    if isinstance(a, Configuration):
-      return self.config_compare(a, b)
-    if isinstance(a, Result):
-      return self.result_compare(a, b)
-    assert False
-
-  def relative(self, a, b):
-    if isinstance(a, Configuration):
-      return self.config_relative(a, b)
-    if isinstance(a, Result):
-      return self.result_relative(a, b)
-    assert None
-
-  def lt(self, a, b):
-    return self.compare(a, b) < 0
-
-  def lte(self, a, b):
-    return self.compare(a, b) <= 0
-
-  def gt(self, a, b):
-    return self.compare(a, b) > 0
-
-  def gte(self, a, b):
-    return self.compare(a, b) >= 0
-
-  def min(self, *l):
-    if len(l) == 1:
-      l = l[0]
-    rv = l[0]
-    for i in l[1:]:
-      if self.lt(i, rv):
-        rv = i
-    return rv
-
-  def max(self, *l):
-    if len(l) == 1:
-      l = l[0]
-    rv = l[0]
-    for i in l[1:]:
-      if self.gt(i, rv):
-        rv = i
-    return rv
-
-  def limit_from_config(self, config):
-    """
-    a time limit to kill a result after such that it can be compared to config
-    """
-    results = self.driver.results_query(config=config)
-    if results.count() == 0:
-      return None
-    else:
-      return max(map(_.time, self.driver.results_query(config=config)))
-
-
-  def project_compare(self, a1, a2, b1, b2, factor=1.0):
-    """
-    linearly project both a and b forward to see how they will compare in the
-    future
-    """
-    a3 = Result()
-    b3 = Result()
-    a3.time = _project(a1.time, a2.time, factor)
-    a3.accuracy = _project(a1.accuracy, a2.accuracy, factor)
-    a3.energy = _project(a1.energy, a2.energy, factor)
-    a3.confidence = _project(a1.confidence, a2.confidence, factor)
-    return self.result_compare(a3, b3)
-
-  def display(self, result):
-    """
-    produce a string version of a resultsdb.models.Result()
-    """
-    rv = []
-    for k in ('time', 'accuracy', 'energy', 'size', 'confidence'):
-      v = getattr(result, k)
-      if v is not None:
-        rv.append('%s=%.4f' % (k, float(v)))
-    return ', '.join(rv)
-
-  def filter_acceptable(self, query):
-    """Return a Result() query that only returns acceptable results"""
-    return query
-
-  def is_acceptable(self, result):
-    """Test if a Result() meets thresholds"""
-    return True
-
-  def stats_quality_score(self, result, worst_result, best_result):
-    """return a score for statistics"""
-    if not self.is_acceptable(result):
-      return worst_result.time
-    else:
-      return result.time
-
-
-def _project(a1, a2, factor):
-  if a1 is None or a2 is None:
-    return None
-  return a2 + factor * (a2 - a1)
-
-
-class MinimizeTime(SearchObjective):
-  """
-  minimize Result().time
-  """
-
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return [Result.time]
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return cmp(result1.time, result2.time)
-
-  def config_compare(self, config1, config2):
-    """cmp() compatible comparison of resultsdb.models.Configuration"""
-    return cmp(min(map(_.time, self.driver.results_query(config=config1))),
-               min(map(_.time, self.driver.results_query(config=config2))))
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    if result2.time == 0:
-      return float('inf') * result1.time
-    return result1.time / result2.time
-
-class MinimizeSize(SearchObjective):
-
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return [Result.size]
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return cmp(result1.size, result2.size)
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    if result2.size == 0:
-      return float('inf') * result1.size
-    return result1.size / result2.size
-
-
-class MinimizeSizeMinimizeTime(SearchObjective):
-  """
-  minimize Result.size() and Result.time()
-  """
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return [Result.time, Result.size]
-
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return cmp((result1.time, result1.size),(result2.time,result2.size))
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    log.warning('result_relative() not yet implemented for %s',
-                self.__class__.__name__)
-
-class MaximizeAccuracy(SearchObjective):
-  """
-  maximize Result().accuracy
-  """
-
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return [-Result.accuracy]
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    # note opposite order
-    return cmp(result2.accuracy, result1.accuracy)
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    # note opposite order
-    if result1.accuracy == 0:
-      return float('inf') * result2.accuracy
-    return result2.accuracy / result1.accuracy
-
-  def stats_quality_score(self, result, worst_result, best_result):
-    """return a score for statistics"""
-    if not self.is_acceptable(result):
-      return worst_result.time
-    else:
-      return result.time
-
-  def stats_raw_score(self, result):
-    return result.accuracy
-
-
-class MaximizeAccuracyMinimizeSize(MaximizeAccuracy):
-  """
-  maximize Result().accuracy, break ties with Result().size
-  """
-
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-    return [-Result.accuracy, Result.size]
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return cmp((-result1.accuracy, result1.size),
-               (-result2.accuracy, result2.size))
-
-  def display(self, result):
-    """
-    produce a string version of a resultsdb.models.Result()
-    """
-    return "accuracy=%.8f, size=%.1f" % (result.accuracy, result.size)
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    # unimplemented for now
-    log.warning('result_relative() not yet implemented for %s',
-                self.__class__.__name__)
-    return None
-
-
-class ThresholdAccuracyMinimizeTime(SearchObjective):
-  """
-  if accuracy >= target:
-    minimize time
-  else:
-    maximize accuracy
-  """
-
-  def __init__(self, accuracy_target, low_accuracy_limit_multiplier=10.0):
-    self.accuracy_target = accuracy_target
-    self.low_accuracy_limit_multiplier = low_accuracy_limit_multiplier
-    super(ThresholdAccuracyMinimizeTime, self).__init__()
-
-  def result_order_by_terms(self):
-    """return database columns required to order by the objective"""
-
-    return ["min(accuracy, %f) desc" % self.accuracy_target,
-            opentuner.resultsdb.models.Result.time]
-
-  def result_compare(self, result1, result2):
-    """cmp() compatible comparison of resultsdb.models.Result"""
-    return cmp((-min(self.accuracy_target, result1.accuracy),
-                result1.time),
-               (-min(self.accuracy_target, result2.accuracy), result2.time))
-
-  def config_compare(self, config1, config2):
-    """cmp() compatible comparison of resultsdb.models.Configuration"""
-    return self.result_compare(
-      self.driver.results_query(config=config1, objective_ordered=True)[0],
-      self.driver.results_query(config=config2, objective_ordered=True)[0])
-
-  def limit_from_config(self, config):
-    """
-    a time limit to kill a result after such that it can be compared to config
-    """
-    results = self.driver.results_query(config=config)
-    if results.count() == 0:
-      return None
-    if self.accuracy_target > min(map(_.accuracy, results)):
-      m = self.low_accuracy_limit_multiplier
-    else:
-      m = 1.0
-    return m * max(map(_.time, results))
-
-
-  def filter_acceptable(self, query):
-    """Return a Result() query that only returns acceptable results"""
-    return query.filter(opentuner.resultsdb.models.Result.accuracy
-                        >= self.accuracy_target)
-
-  def is_acceptable(self, result):
-    """Test if a Result() meets thresholds"""
-    return result.accuracy >= self.accuracy_target
-
-  def result_relative(self, result1, result2):
-    """return None, or a relative goodness of resultsdb.models.Result"""
-    # unimplemented for now
-    log.warning('result_relative() not yet implemented for %s',
-                self.__class__.__name__)
-    return None
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/patternsearch.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/patternsearch.py
deleted file mode 100644
index 7b526e7897f2c673552899ae3a115d6e2e06737b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/patternsearch.py
+++ /dev/null
@@ -1,72 +0,0 @@
-
-
-from opentuner.search import technique
-
-class PatternSearch(technique.SequentialSearchTechnique):
-  def main_generator(self):
-
-    objective   = self.objective
-    driver      = self.driver
-    manipulator = self.manipulator
-
-    # start at a random position
-    center = driver.get_configuration(manipulator.random())
-    self.yield_nonblocking(center)
-
-    # initial step size is arbitrary
-    step_size = 0.1
-
-    while True:
-      points = list()
-      for param in manipulator.parameters(center.data):
-        if param.is_primitive():
-          # get current value of param, scaled to be in range [0.0, 1.0]
-          unit_value = param.get_unit_value(center.data)
-
-          if unit_value > 0.0:
-            # produce new config with param set step_size lower
-            down_cfg = manipulator.copy(center.data)
-            param.set_unit_value(down_cfg, max(0.0, unit_value - step_size))
-            down_cfg = driver.get_configuration(down_cfg)
-            self.yield_nonblocking(down_cfg)
-            points.append(down_cfg)
-
-          if unit_value < 1.0:
-            # produce new config with param set step_size higher
-            up_cfg = manipulator.copy(center.data)
-            param.set_unit_value(up_cfg, min(1.0, unit_value + step_size))
-            up_cfg = driver.get_configuration(up_cfg)
-            self.yield_nonblocking(up_cfg)
-            points.append(up_cfg)
-
-        else: # ComplexParameter
-          for mutate_function in param.manipulators(center.data):
-            cfg = manipulator.copy(center.data)
-            mutate_function(cfg)
-            cfg = driver.get_configuration(cfg)
-            self.yield_nonblocking(cfg)
-            points.append(cfg)
-
-
-      yield None # wait for all results
-
-      #sort points by quality, best point will be points[0], worst is points[-1]
-      points.sort(cmp=objective.compare)
-
-      if (objective.lt(driver.best_result.configuration, center)
-          and driver.best_result.configuration != points[0]):
-        # another technique found a new global best, switch to that
-        center = driver.best_result.configuration
-      elif objective.lt(points[0], center):
-        # we found a better point, move there
-        center = points[0]
-      else:
-        # no better point, shrink the pattern
-        step_size /= 2.0
-
-# register our new technique in global list
-technique.register(PatternSearch())
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/plugin.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/plugin.py
deleted file mode 100644
index ad8481837cbee62ba8c3f1c94a27529261953bb0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/plugin.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import abc
-import argparse
-import logging
-import time
-
-from datetime import datetime
-from fn import _
-
-log = logging.getLogger(__name__)
-display_log = logging.getLogger(__name__ + ".DisplayPlugin")
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--results-log',
-    help="file to store log of the best configuration times")
-argparser.add_argument('--results-log-details',
-    help="file to store log of the non-best configuration times")
-argparser.add_argument('--quiet', action='store_true',
-    help="print less information")
-argparser.add_argument('--display-frequency', default=10, type=int,
-    help="how often for DisplayPlugin to print")
-
-class SearchPlugin(object):
-  @property
-  def priority(self):
-    """control order the plugin hooks gets run in, lower runs first"""
-    return 0
-
-  def set_driver(self, driver):
-    """called before all other methods"""
-    self.driver = driver
-
-  def before_main(self): pass
-  def after_main(self):  pass
-
-  def before_techniques(self): pass
-  def after_techniques(self):  pass
-
-  def before_results_wait(self): pass
-  def after_results_wait(self):  pass
-
-  def on_result(self, result):
-    """
-    called once for every new result
-    """
-    pass
-
-  def on_result_for_technique(self, result, technique):
-    """
-    called right before a result is given to a technique
-    (result may be requested by multiple techniques)
-    """
-    pass
-
-  def on_new_best_result(self, result):
-    """
-    called whenever the global best result changes
-    """
-    pass
-
-class DisplayPlugin(SearchPlugin):
-  __metaclass__ = abc.ABCMeta
-  def __init__(self, display_period=5):
-    super(DisplayPlugin, self).__init__()
-    self.last  = time.time()
-    self.start = time.time()
-    self.display_period = display_period
-
-  def after_results_wait(self):
-    t = time.time()
-    if t - self.display_period > self.last:
-      # call display every 5 seconds
-      self.last = t
-      self.display(t)
-
-  def after_main(self):
-    self.display()
-
-  @abc.abstractmethod
-  def display(self, t=None):
-    pass
-
-
-class LogDisplayPlugin(DisplayPlugin):
-  def display(self, t=None):
-    if not t:
-      t = time.time()
-    count = self.driver.results_query().count()
-    best = self.driver.results_query(objective_ordered = True).first()
-    if best is None:
-      log.warning("no results yet")
-      return
-    requestor = ','.join(map(_.requestor, best.desired_results))
-    display_log.info("tests=%d, best %s, cost %s, found by %s",
-                     count,
-                     cfg_repr(best.configuration),
-                     self.driver.objective.display(best),
-                     requestor,
-                     )
-
-class FileDisplayPlugin(SearchPlugin):
-  def __init__(self, out, details, *args, **kwargs):
-    super(FileDisplayPlugin, self).__init__(*args, **kwargs)
-    self.last_best = float('inf')
-    self.start_date = datetime.now()
-    if out:
-      self.out = open(out, "w")
-    else:
-      self.out = None
-    if out == details:
-      self.details = self.out
-      self.out = None
-    elif details:
-      self.details = open(details, "w")
-    else:
-      self.details = None
-
-  def on_result(self, result):
-    if self.out and result.time < self.last_best:
-      self.last_best = result.time
-      print >>self.out, \
-          (result.collection_date - self.start_date).total_seconds(), \
-          result.time
-      self.out.flush()
-    if self.details:
-      print >>self.details, \
-          (result.collection_date - self.start_date).total_seconds(), \
-          result.time
-      self.details.flush()
-
-def get_enabled(args):
-  plugins = []
-  if not args.quiet:
-    plugins.append(LogDisplayPlugin(args.display_frequency))
-  if args.results_log or args.results_log_details:
-    plugins.append(FileDisplayPlugin(args.results_log,
-                                     args.results_log_details))
-  return plugins
-
-def cfg_repr(cfg):
-  try:
-    s = repr(cfg.data)
-    if len(s) < 100:
-      return s
-  except:
-    pass
-  return "#{0}".format(cfg.id)
-
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/pso.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/pso.py
deleted file mode 100644
index 3b8c37a7787b900a70f80ffab00d5c90b46c7541..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/pso.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# -*- coding: utf-8 -*-
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-from manipulator import *
-from opentuner.search import technique
-import random
-import math
-
-class PSO(technique.SequentialSearchTechnique ):
-  """ Particle Swarm Optimization """
-  def __init__(self, crossover, N = 30, init_pop=None, *pargs, **kwargs):
-    """
-    crossover: name of crossover operator function
-    """
-    super(PSO, self).__init__(*pargs, **kwargs)
-    self.crossover = crossover
-    self.name = 'pso-'+crossover.replace("op3_cross_","")
-    self.init_pop = init_pop
-    self.N = N
-
-  def main_generator(self):
-
-    objective   = self.objective
-    driver    = self.driver
-    m = self.manipulator
-    def config(cfg):
-      return driver.get_configuration(cfg)
-  
-    population = self.init_pop
-    if not population:
-      population = [HybridParticle(m, self.crossover) for i in range(self.N)]
-
-    for p in population:
-      yield driver.get_configuration(p.position)
-
-    while True:
-      for particle in population:
-        g = driver.best_result.configuration.data
-        old=m.copy(particle.position)
-        particle.move(g)
-        yield config(particle.position)
-        # update individual best
-        if objective.lt(config(particle.position), config(particle.best)):
-          particle.best = particle.position
-
-class HybridParticle(object):
-  def __init__(self, m, crossover_choice, omega=0.5, phi_l=0.5, phi_g=0.5):
-
-    """
-    m: a configuraiton manipulator
-    omega: influence of the particle's last velocity, a float in range [0,1] ; omega=1 means even speed
-    phi_l: influence of the particle's distance to its historial best position, a float in range [0,1]
-    phi_g: influence of the particle's distance to the global best position, a float in range [0,1]
-    """
-
-    self.manipulator = m
-    self.position = self.manipulator.random()   
-    self.best = self.position
-    self.omega = omega
-    self.phi_l = phi_l
-    self.phi_g = phi_g
-    self.crossover_choice = crossover_choice
-    self.velocity = {}
-    for p in self.manipulator.params:
-      # Velocity as a continous value
-      self.velocity[p.name]=0  
-
-  def move(self, global_best):
-    """
-    Update parameter values using corresponding operators. 
-    TODO: introduce operator choice map
-    """
-    m = self.manipulator
-    for p in m.params:
-      self.velocity[p.name] = p.op3_swarm(self.position, global_best, self.best, c=self.omega, c1=self.phi_g, c2=self.phi_l, xchoice=self.crossover_choice, velocity=self.velocity[p.name])
-
-
-technique.register(PSO(crossover = 'op3_cross_OX3'))
-technique.register(PSO(crossover = 'op3_cross_OX1'))
-technique.register(PSO(crossover = 'op3_cross_PMX'))
-technique.register(PSO(crossover = 'op3_cross_PX'))
-technique.register(PSO(crossover = 'op3_cross_CX'))
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simplextechniques.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simplextechniques.py
deleted file mode 100644
index 3cfec0eebb25cf3c7ff2cc2bc69d558454660e32..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simplextechniques.py
+++ /dev/null
@@ -1,457 +0,0 @@
-import abc
-import logging
-import math
-from collections import defaultdict
-from fn import _
-from fn.iters import map, filter
-from .manipulator import Parameter
-from .metatechniques import RecyclingMetaTechnique
-from .technique import SequentialSearchTechnique, register
-
-log = logging.getLogger(__name__)
-
-
-class SimplexTechnique(SequentialSearchTechnique):
-  """
-  Base class with utility functions common
-  to simplex type methods
-  """
-
-  def __init__(self, seed_cfg=None, *args, **kwargs):
-    super(SimplexTechnique, self).__init__(*args, **kwargs)
-    self.centroid = None
-    self.last_simplex_points = None
-    self.seed_cfg = seed_cfg
-    self.simplex_points = []
-
-  def calculate_centroid(self):
-    """
-    average of all the PrimitiveParameters in self.simplex_points
-    ComplexParameters are copied from self.simplex_points[0]
-    """
-    sums = defaultdict(float)
-    counts = defaultdict(int)
-
-    for config in self.simplex_points:
-      cfg = config.data
-      for param in self.manipulator.parameters(cfg):
-        if param.is_primitive():
-          sums[param.name] += param.get_unit_value(cfg)
-          counts[param.name] += 1
-
-    centroid = self.manipulator.copy(self.simplex_points[0].data)
-    for param in self.manipulator.parameters(centroid):
-      if param.is_primitive():
-        param.set_unit_value(centroid,
-                             sums[param.name] / float(counts[param.name]))
-
-    return centroid
-
-  def cfg_to_str(self, cfg):
-    params = list(filter(Parameter.is_primitive,
-                         self.manipulator.parameters(cfg)))
-    params.sort(key=_.name)
-    return str(tuple(map(lambda x: x.get_unit_value(cfg), params)))
-
-  def debug_log(self):
-    for i, config in enumerate(self.simplex_points):
-      log.debug("simplex_points[%d] = %s", i, self.cfg_to_str(config.data))
-    if self.centroid:
-      log.debug("centroid = %s", self.cfg_to_str(self.centroid))
-
-  def linear_point(self, p1, p2, scale):
-    """
-    return a point on the line passing between p1 and p2 at position scale
-    such that p1 + scale*(p1 - p2)
-    """
-    return self.manipulator.linear_config(1.0, p1, scale, p1, -scale, p2)
-
-  def convergence_criterea(self):
-    """True will cause the simplex method to stop"""
-    if self.rounds_since_novel_request > 3 * len(self.simplex_points) + 1:
-      return True
-    if self.last_simplex_points == self.simplex_points:
-      return True
-    self.last_simplex_points = list(self.simplex_points)
-    return False
-
-  def initial_simplex_seed(self):
-    """
-    return a point to base the initial simplex on
-    """
-    if self.seed_cfg is not None:
-      return self.seed_cfg
-    return self.manipulator.random()
-
-  @abc.abstractmethod
-  def initial_simplex(self):
-    """
-    return a initial list of configurations
-    """
-    return []
-
-
-class RandomInitialMixin(object):
-  """
-  start with random initial simplex
-  """
-
-  def initial_simplex(self):
-    # we implicitly assume number of parameters is fixed here, however
-    # it will work if it isn't (simplex size is undefined)
-    cfg0 = self.initial_simplex_seed()
-    params = self.manipulator.parameters(cfg0)
-    return [cfg0] + [self.manipulator.random()
-                     for p in params
-                     if p.is_primitive()]
-
-
-class RightInitialMixin(object):
-  """
-  start with random initial right triangle like simplex
-  """
-
-  def __init__(self, initial_unit_edge_length=0.1, *args, **kwargs):
-    assert initial_unit_edge_length <= 0.5
-    self.initial_unit_edge_length = initial_unit_edge_length
-    super(RightInitialMixin, self).__init__(*args, **kwargs)
-
-  def initial_simplex(self):
-    cfg0 = self.initial_simplex_seed()
-    simplex = [cfg0]
-    params = self.manipulator.parameters(cfg0)
-    params = filter(lambda x: x.is_primitive(), params)
-    for p in params:
-      simplex.append(self.manipulator.copy(cfg0))
-      v = p.get_unit_value(simplex[-1])
-      if v <= 0.5:
-        v += self.initial_unit_edge_length
-      else:
-        v -= self.initial_unit_edge_length
-      p.set_unit_value(simplex[-1], v)
-    return simplex
-
-
-class RegularInitialMixin(object):
-  """
-  start with random initial regular simplex (all edges equal length)
-  """
-
-  def __init__(self, initial_unit_edge_length=0.1, *args, **kwargs):
-    assert initial_unit_edge_length <= 0.5
-    self.initial_unit_edge_length = initial_unit_edge_length
-    super(RegularInitialMixin, self).__init__(*args, **kwargs)
-
-  def initial_simplex(self):
-    cfg0 = self.initial_simplex_seed()
-    simplex = [cfg0]
-    params = self.manipulator.parameters(cfg0)
-    params = list(filter(lambda x: x.is_primitive(), params))
-    if len(params) == 0:
-      return simplex
-
-    q = (((math.sqrt(len(params) + 1.0) - 1.0) / (len(params) * math.sqrt(2.0)))
-         * self.initial_unit_edge_length)
-    p = q + ((1.0 / math.sqrt(2.0)) * self.initial_unit_edge_length)
-
-    base = [x.get_unit_value(cfg0) for x in params]
-    for j in xrange(len(base)):
-      if max(p, q) + base[j] > 1.0:
-        #flip this dimension as we would overflow our [0,1] bounds
-        base[j] *= -1.0
-
-    for i in xrange(len(params)):
-      simplex.append(self.manipulator.copy(cfg0))
-      params[i].set_unit_value(simplex[-1], abs(base[i] + p))
-      for j in xrange(i + 1, len(params)):
-        params[j].set_unit_value(simplex[-1], abs(base[i] + q))
-
-    return simplex
-
-
-class NelderMead(SimplexTechnique):
-  """
-  Nelder-Mead downhill simplex method.
-
-  Based on description of method on page 82 of
-  'Noisy Optimization With Evolution Strategies' by Dirk V. Arnold.
-
-  We set alpha=2.0 by default instead of the often recommended alpha=1.0 to
-  avoid a common degenerate case, where the volume of the simplex becomes zero.
-  This is easiest to see with a single parameter. Let the simplex points
-  be x0,x1.  Let the centroid be c=(x0+x1)/2.0 and the reflection point be:
-  reflection = c + alpha*(c-x1) = (x0+x1)*(1+alpha)/2 - x1
-  The problem is, if we set alpha = 1.0, then the x1's cancel out and the
-  reflection point becomes just reflection=x0, which also happens to be the
-  second best point, meaning we will use it.  So in a single step of the
-  algorithm the simplex becomes singular.
-  """
-
-  def __init__(self,
-               alpha=2.0,
-               gamma=2.0,
-               beta=0.5,
-               sigma=0.5,
-               *args, **kwargs):
-    self.alpha = alpha
-    self.gamma = gamma
-    self.beta = beta
-    self.sigma = sigma
-    super(NelderMead, self).__init__(*args, **kwargs)
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['alpha', 'gamma', 'beta', 'sigma']
-
-
-  def main_generator(self):
-    objective = self.objective
-    driver = self.driver
-
-    # test the entire initial simplex
-    self.simplex_points = list(map(driver.get_configuration,
-                                   self.initial_simplex()))
-
-    if len(self.simplex_points) <= 1:
-      log.warning("only 1 point in simplex, will not use %s", self.name)
-      return
-
-    log.debug("initial points")
-    for p in self.simplex_points:
-      self.yield_nonblocking(p)
-    yield None  # wait until results are ready
-
-    while not self.convergence_criterea():
-      # next steps assume this ordering
-      self.simplex_points.sort(cmp=objective.compare)
-      # set limit from worst point
-      self.limit = objective.limit_from_config(self.simplex_points[-1])
-      self.centroid = self.calculate_centroid()
-      if log.isEnabledFor(logging.DEBUG):
-        self.debug_log()
-
-      reflection = self.reflection_point()
-      yield reflection
-
-      if objective.lt(reflection, self.simplex_points[0]):
-        #expansion case
-        expansion = self.expansion_point(reflection)
-        yield expansion
-
-        if objective.lt(expansion, reflection):
-          log.debug("using expansion point")
-          self.simplex_points[-1] = expansion
-        else:
-          log.debug("using reflection point (considered expansion)")
-          self.simplex_points[-1] = reflection
-
-      elif objective.lt(reflection, self.simplex_points[1]):
-        #reflection case
-        log.debug("using reflection point")
-        self.simplex_points[-1] = reflection
-      else:
-        # contraction case
-        if objective.lte(reflection, self.simplex_points[-1]):
-          # outside contraction
-          contract_base = reflection
-        else:
-          # inside contraction
-          contract_base = self.simplex_points[-1]
-
-        contraction = self.contraction_point(contract_base)
-        yield contraction
-
-        if objective.lte(contraction, contract_base):
-          log.debug("using contraction point")
-          self.simplex_points[-1] = contraction
-        else:
-          #reduction case
-          log.debug("performing shrink reduction")
-          self.perform_shrink_reduction()
-          for p in self.simplex_points:
-            self.yield_nonblocking(p)
-          yield None  # wait until results are ready
-
-  def reflection_point(self):
-    """
-    reflect worst point across centroid
-    """
-    return self.driver.get_configuration(
-        self.linear_point(self.centroid,
-                          self.simplex_points[-1].data,
-                          self.alpha))
-
-  def expansion_point(self, reflection):
-    """
-    reflect worst point across centroid more (by default 2x as much)
-    """
-    return self.driver.get_configuration(
-        self.linear_point(self.centroid,
-                          reflection.data,
-                          -self.gamma))
-
-  def contraction_point(self, contract_base):
-    """
-    reflect worst point across centroid less
-    """
-    return self.driver.get_configuration(
-        self.linear_point(self.centroid,
-                          contract_base.data,
-                          -self.beta))
-
-  def perform_shrink_reduction(self):
-    """
-    shrink the simplex in size by sigma=1/2 (default), moving it closer to the
-    best point
-    """
-    for i in xrange(1, len(self.simplex_points)):
-      self.simplex_points[i] = self.driver.get_configuration(
-          self.linear_point(self.simplex_points[0].data,
-                            self.simplex_points[i].data,
-                            -self.sigma))
-
-
-class Torczon(SimplexTechnique):
-  """
-  Torczon multi-directional search algorithm.
-
-  Based on description of method on page 85 of
-  'Noisy Optimization With Evolution Strategies' by Dirk V. Arnold.
-  """
-
-  def __init__(self,
-               alpha=1.0,
-               gamma=2.0,
-               beta=0.5,
-               *args, **kwargs):
-    self.alpha = alpha
-    self.gamma = gamma
-    self.beta = beta
-    super(Torczon, self).__init__(*args, **kwargs)
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    return ['alpha', 'gamma', 'beta']
-
-
-  def main_generator(self):
-    objective = self.objective
-    driver = self.driver
-
-    # test the entire initial simplex
-    self.simplex_points = list(map(driver.get_configuration,
-                                   self.initial_simplex()))
-    if len(self.simplex_points) <= 1:
-      log.warning("only 1 point in simplex, will not use %s", self.name)
-      return
-
-    log.debug("initial points")
-    for p in self.simplex_points:
-      self.yield_nonblocking(p)
-    yield None  # wait until results are ready
-    self.simplex_points.sort(cmp=objective.compare)
-
-    while not self.convergence_criterea():
-      # set limit from worst point
-      self.limit = objective.limit_from_config(self.simplex_points[-1])
-
-      if log.isEnabledFor(logging.DEBUG):
-        self.debug_log()
-
-      reflected = self.reflected_simplex()
-      yield None  # wait until results are ready
-      reflected.sort(cmp=objective.compare)
-
-      # this next condition implies reflected[0] < simplex_points[0] since
-      # reflected is sorted and contains simplex_points[0] (saves a db query)
-      if reflected[0] is not self.simplex_points[0]:
-        expanded = self.expanded_simplex()
-        yield None  # wait until results are ready
-        expanded.sort(cmp=objective.compare)
-
-        if objective.lt(expanded[0], reflected[0]):
-          log.debug("expansion performed")
-          self.simplex_points = expanded
-        else:
-          log.debug("reflection performed")
-          self.simplex_points = reflected
-      else:
-        contracted = self.contracted_simplex()
-        yield None  # wait until results are ready
-        contracted.sort(cmp=objective.compare)
-
-        log.debug("contraction performed")
-        self.simplex_points = contracted
-
-  def scaled_simplex(self, scale):
-    """
-    assumes self.simplex_points[0] is best point and returns a new simplex
-    reflected across self.simplex_points[0] by scale
-    """
-    simplex = list(self.simplex_points)  # shallow copy
-    for i in xrange(1, len(simplex)):
-      simplex[i] = self.driver.get_configuration(
-          self.linear_point(simplex[0].data, simplex[i].data, scale))
-      self.yield_nonblocking(simplex[i])
-    return simplex
-
-  def reflected_simplex(self):
-    return self.scaled_simplex(self.alpha)
-
-  def expanded_simplex(self):
-    return self.scaled_simplex(self.gamma)
-
-  def contracted_simplex(self):
-    return self.scaled_simplex(-self.beta)
-
-
-class RandomNelderMead(RandomInitialMixin, NelderMead):
-  pass
-
-
-class RightNelderMead(RightInitialMixin, NelderMead):
-  pass
-
-
-class RegularNelderMead(RegularInitialMixin, NelderMead):
-  pass
-
-
-class RandomTorczon(RandomInitialMixin, Torczon):
-  pass
-
-
-class RightTorczon(RightInitialMixin, Torczon):
-  pass
-
-
-class RegularTorczon(RegularInitialMixin, Torczon):
-  pass
-
-
-class MultiNelderMead(RecyclingMetaTechnique):
-  def __init__(self):
-    super(MultiNelderMead, self).__init__([RightNelderMead, RandomNelderMead,
-                                           RegularNelderMead])
-
-
-class MultiTorczon(RecyclingMetaTechnique):
-  def __init__(self):
-    super(MultiTorczon, self).__init__([RightTorczon, RandomTorczon,
-                                        RegularTorczon])
-
-
-register(RandomNelderMead())
-register(RegularNelderMead())
-register(RightNelderMead())
-register(MultiNelderMead())
-register(RandomTorczon())
-register(RegularTorczon())
-register(RightTorczon())
-register(MultiTorczon())
-
-
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simulatedannealing.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simulatedannealing.py
deleted file mode 100644
index 45b315f2e6bbceda2822ae72623e8c0032afe66b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/simulatedannealing.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from opentuner.search import technique
-import math
-import random
-#Default interval steps for cooling schedules
-DEFAULT_INTERVAL = 100
-
-#Pseudo-annealing - no relative energy input into acceptance function
-class PseudoAnnealingSearch(technique.SequentialSearchTechnique):
-  def __init__(self,
-               temps = [30,0], #temperature schedule
-               intervals = [],  #duration schedule
-          		 loop = True, #Do we loop the schedule if we reach the end?
-               *pargs, **kwargs):
-    #fill intervals sufficiently
-    ext_intervals = list(intervals)
-    for i in range(len(temps)-len(intervals)-1):
-      ext_intervals.append(DEFAULT_INTERVAL)
-            
-    #create temperature schedule (list of temps)
-    cool_schedule = [temps[0]]
-    for i in range(len(temps)-1):
-      step = (float(temps[i+1]) - temps[i])/ext_intervals[i]
-      for j in range(ext_intervals[i]):
-        cool_schedule.append(max(cool_schedule[-1] + step,0))
-      
-    self.cool_schedule = cool_schedule
-    self.loop = loop
-    self.scaling = 50 #scaling of acceptance function
-      
-    super(PseudoAnnealingSearch,self).__init__(*pargs,**kwargs)
-
-
-  def main_generator(self):
-    objective = self.objective
-    driver = self.driver
-    manipulator = self.manipulator
-
-    #Start in a random spot
-    state = driver.get_configuration(manipulator.random())
-    yield state
-    #schedule counter
-    counter = 0
-    max_time = len(self.cool_schedule)-1
-    #Check whether relative objective implemented
-    has_rel = objective.relative(state,state) is not None
-    has_rel=False
-              
-    while True:
-      #Determine temperature
-      temp = self.cool_schedule[min(counter,max_time)]
-      #scale stepsize with temp and time (arbitrary)
-      step_size = math.exp(-(20 + counter/100)/(temp+ 1)) 
-          
-      #get candidate neighbors using manipulator
-      points = list()
-      points.append(state)
-      for param in manipulator.parameters(state.data):
-        if param.is_primitive():
-          # get current value of param, scaled to be in range [0.0, 1.0]
-          unit_value = param.get_unit_value(state.data)
-          if unit_value > 0.0:
-            # produce new config with param set step_size lower
-            down_cfg = manipulator.copy(state.data)
-            param.set_unit_value(down_cfg, max(0.0, unit_value - step_size*random.random()))
-            down_cfg = driver.get_configuration(down_cfg)
-            self.yield_nonblocking(down_cfg)
-            points.append(down_cfg)
-
-          if unit_value < 1.0:
-            # produce new config with param set step_size higher
-            up_cfg = manipulator.copy(state.data)
-            param.set_unit_value(up_cfg, min(1.0, unit_value + step_size*random.random()))
-            up_cfg = driver.get_configuration(up_cfg)
-            self.yield_nonblocking(up_cfg)
-            points.append(up_cfg)
-        else: # ComplexParameter
-          for mutate_function in param.manipulators(state.data):
-            cfg = manipulator.copy(state.data)
-            mutate_function(cfg)
-            cfg = driver.get_configuration(cfg)
-            self.yield_nonblocking(cfg)
-            points.append(cfg)
-      yield None # wait for all results
-            
-      #Relative comparison implemented
-      if has_rel:
-        while True:
-          if len(points) == 0:
-            state = driver.best_result.configuration
-            break
-          candidate = points.pop(random.randint(0,len(points)-1))
-          #compare to global best
-          if random.random() < AcceptanceFunction(1, objective.relative(candidate,driver.best_result.configuration), temp, self.scaling):
-            state = candidate
-            break
-      #No relative compare
-      else:
-      #sort points by "energy" (quality)
-        points.sort(cmp=objective.compare)
-            
-        #Make decision about changing state
-        #probability picking next-best state is exp^(-1/temp)
-        #repeat and cycle to get state p-dist resembling this
-        sel = 0
-        while AcceptanceFunction(0,1,temp,1)>random.random():
-          sel += 1
-        state = points[sel%len(points)]
-            
-        #switch to the global best if temperature is low (i.e. we aren't moving much)
-        if AcceptanceFunction(0,1,temp,1)< .0001 and objective.lt(driver.best_result.configuration, state):
-          state = driver.best_result.configuration
-          
-      #update counter
-      counter +=1
-      if counter>max_time and self.loop:
-        counter=counter-max_time
-              
-
-#Acceptance probability function for annealing
-def AcceptanceFunction(e,e_new,temp,scaling):
-  #Standard acceptance probability function using relative "goodness"
-  if e>=e_new:
-    return 1
-  if temp == 0:
-    return 0
-  if scaling*(e_new-e)/temp > 10:
-    #for practical purposes, probability is too low.
-    return 0
-  return math.exp(scaling*(e-e_new)/temp)
-
-
-#register technique
-technique.register(PseudoAnnealingSearch())
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/technique.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/technique.py
deleted file mode 100644
index 849391df9bb37454301c90a520fbbe6b5025c683..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/search/technique.py
+++ /dev/null
@@ -1,358 +0,0 @@
-import abc
-import argparse
-import logging
-import os
-import random
-import sys
-
-from importlib import import_module
-from datetime import datetime
-from fn import _
-
-from opentuner.resultsdb.models import *
-from plugin import SearchPlugin
-
-log = logging.getLogger(__name__)
-#log.setLevel(logging.DEBUG)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--technique','-t', action='append',
-                       help="which technique to use")
-argparser.add_argument('--list-techniques','-lt', action='store_true',
-                       help="list techniques available and exit")
-argparser.add_argument('--generate-bandit-technique','-gbt', action='store_true',
-                       help="randomly generate a bandit to use")
-
-class SearchTechniqueBase(object):
-  """
-  abstract base class for search techniques, with minimal interface
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self, name = None):
-    super(SearchTechniqueBase, self).__init__()
-    if name:
-      self.name = name
-    else:
-      self.name = self.default_name()
-
-  def is_ready(self):
-    """test if enough data has been gathered to use this technique"""
-    return True
-
-  def default_name(self):
-    """name of this SearchTechnique uses for display/accounting"""
-    return self.__class__.__name__
-
-  def handle_requested_result(self, result):
-    """called for each new Result(), requested by this technique"""
-    pass
-
-  @abc.abstractmethod
-  def set_driver(self, driver):
-    """called at start of tuning process"""
-    return
-
-  @abc.abstractmethod
-  def desired_result(self):
-    """
-    return at most count resultsdb.models.DesiredResult objects based on past
-    performance
-    """
-    return
-
-class SearchTechnique(SearchPlugin, SearchTechniqueBase):
-  """
-  a search search technique with basic utility functions
-  """
-
-  def __init__(self, *pargs, **kwargs):
-    super(SearchTechnique, self).__init__(*pargs, **kwargs)
-    self.driver = None
-    self.manipulator = None
-    self.objective = None
-    self.request_count = 0
-
-  def set_driver(self, driver):
-    super(SearchTechnique, self).set_driver(driver)
-    self.manipulator = driver.manipulator
-    self.objective = driver.objective
-    driver.add_plugin(self)
-
-  def desired_result(self):
-    """
-    create and return a resultsdb.models.DesiredResult
-    returns None if no desired results and False if waiting for results
-    """
-    cfg = self.desired_configuration()
-    if cfg is None:
-      return None
-    if cfg is False:
-      return False
-    if type(cfg) is Configuration:
-      config = cfg
-    else:
-      config = self.driver.get_configuration(cfg)
-    desired = DesiredResult(configuration=config,
-                            requestor=self.name,
-                            generation=self.driver.generation,
-                            request_date=datetime.now(),
-                            tuning_run=self.driver.tuning_run)
-    if hasattr(self, 'limit'):
-      desired.limit = self.limit
-    self.driver.register_result_callback(desired, self.handle_requested_result)
-    self.request_count += 1
-    return desired
-
-  @abc.abstractmethod
-  def desired_configuration(self):
-    """
-    return a cfg that we should test
-    given a ConfigurationManipulator and SearchDriver
-    return None if there are no configurations to test
-    return False if waiting for results
-    """
-    return dict()
-
-  def handle_requested_result(self, result):
-    """called for each new Result(), regardless of who requested it"""
-    pass
-
-  def default_generated_name(self):
-    """ The default generated name for this technique """
-    return self.base_name()
-
-  def use_default_generated_name(self):
-    """ set the name of this technique to the default generated name """
-    self.name = self.default_generated_name()
-
-  def base_name(self):
-    """
-    Return the base name of this technique with form
-    classname;hyperparam1,v1;hyperparam2,v2 ...
-    where hyperparams are taken in order from get_hyper_parameters()
-
-    Should only be called after this technique has finished initializing.
-    """
-    out = [self.__class__.__name__]
-    for hyper_parameter in self.get_hyper_parameters():
-      # get hyperparam,v as a string and append
-      try:
-        out.append(hyper_parameter + ',' + str(getattr(self, hyper_parameter)))
-      except AttributeError:
-        log.error("Uninitialized hyper-parameter %s for technique %s.",
-                   hyper_parameter, self.__class__.__name__)
-
-    return ';'.join(out)
-
-  @classmethod
-  def get_hyper_parameters(cls):
-    """
-    return a list of hyper-parameters names for this technique
-
-    Name strings must match the corresponding attribute with the hyper-parameter
-    value on technique instances. Names should also match the key word argument
-    used when initializing an instance. Hyperparameters should only take literal
-    values.
-
-    For example, given hyper parameter "mutation_rate", then the __init__ method
-    should have 'mutation_rate' as a key word argument and later have the line
-    self.mutation_rate = mutation_rate
-    """
-    return []
-
-  @classmethod
-  def generate_technique(cls, manipulator=None, *args, **kwargs):
-    """ return a new technique based off this instance """
-    t = cls(*args, **kwargs)
-    t.use_default_generated_name()
-    return t
-
-class PureRandom(SearchTechnique):
-  """
-  request configurations completely randomly
-  """
-  def desired_configuration(self):
-    return self.manipulator.random()
-
-class AsyncProceduralSearchTechnique(SearchTechnique):
-  def __init__(self, *pargs, **kwargs):
-    super(AsyncProceduralSearchTechnique, self).__init__(*pargs, **kwargs)
-    self.gen = None
-    self.done = False
-    self.latest_results = []
-
-  def call_main_generator(self):
-    """passthrough (used in subclasses)"""
-    return self.main_generator()
-
-  def desired_configuration(self):
-    if self.gen is None:
-      log.debug("%s: creating generator", self.name)
-      self.gen = self.call_main_generator()
-    if not self.done:
-      try:
-        return self.gen.next()
-      except StopIteration:
-        log.debug("%s: generator finished", self.name)
-        self.done = True
-    return None
-
-  @abc.abstractmethod
-  def main_generator(self):
-    """
-    custom generator to conduct this search, should:
-    yield config
-    to request tests and call driver.get_results() to read the results
-
-    in AsyncProceduralSearchTechnique results are ready at an undefined
-    time (`yield False` to stall and wait for them)
-
-    in SequentialSearchTechnique results are ready after the yield
-    """
-    pass
-
-  def is_ready(self):
-    return not self.done
-
-class SequentialSearchTechnique(AsyncProceduralSearchTechnique):
-  def __init__(self, novelty_threshold=50, reset_threshold=500, *pargs, **kwargs):
-    super(SequentialSearchTechnique, self).__init__(*pargs, **kwargs)
-    self.pending_tests = []
-    self.novelty_threshold = novelty_threshold
-    self.rounds_since_novel_request = 0
-    self.reset_threshold = reset_threshold
-
-  def yield_nonblocking(self, cfg):
-    """
-    within self.main_generator() act like `yield cfg`, but don't wait for the
-    results until the following yield (spawn/sync style)
-    """
-    if cfg:
-      self.pending_tests.append(cfg)
-
-  def call_main_generator(self):
-    """insert waits for results after every yielded item"""
-    subgen = self.main_generator()
-    self.rounds_since_novel_request = 0
-    while True:
-      self.rounds_since_novel_request += 1
-      if (self.rounds_since_novel_request % self.novelty_threshold) == 0:
-        log.warning("%s has not requested a new result for %d rounds",
-                    self.name, self.rounds_since_novel_request)
-        if (self.rounds_since_novel_request > self.reset_threshold):
-          log.warning("%s is being reset", self.name)
-          subgen = self.main_generator()
-          self.rounds_since_novel_request = 0
-        yield None # give other techniques a shot
-      try:
-        p = subgen.next()
-        if p:
-          self.pending_tests.append(p)
-      except StopIteration:
-        return
-      finally:
-        for p in self.pending_tests:
-          if not self.driver.has_results(p):
-            self.rounds_since_novel_request = 0
-            yield p
-
-      # wait for all pending_tests to have results
-      c = 0
-      while self.pending_tests:
-        log.debug("%s: waiting for %d pending tests",
-                  self.name, len(self.pending_tests))
-        c += 1
-        if (c % 100) == 0:
-          log.error("%s: still waiting for %d pending tests (c=%d)",
-                     self.name, len(self.pending_tests), c)
-
-        self.pending_tests = filter(lambda x: not self.driver.has_results(x),
-                                    self.pending_tests)
-        if self.pending_tests:
-          self.rounds_since_novel_request = 0
-          yield False # wait
-
-#list of all techniques
-the_registry = list()
-
-#list of technique generators
-the_generator_registry = list()
-
-def register(t):
-  the_registry.append(t)
-
-def register_generator(cls, generator_weight=1.0, *args, **kwargs):
-  """
-  register a technique generator - a tuple of (technique class, args, kwargs)
-  where args and kwargs will be passed into the generate_technique classmethod -
-  with specified probability weight when randomly choosing a generator
-
-  :param cls: a technique class to use as a generator
-  :param generator_weight: probability weighting when randomly choosing a generator
-  :param args: arguments to pass into generate_technique class method
-  :param kwargs: arguments to pass into generate_technique class method
-  """
-  the_generator_registry.append(((cls, args, kwargs), generator_weight))
-
-register(PureRandom())
-
-def get_random_generator_technique(generators=None, manipulator=None):
-  """
-  Takes in a sequence of ((generator, args, kwargs), weight) tuples.
-  Returns a random generated technique info tuple
-
-  :param generators: optional argument to avoid repeated getting of generators
-  :param manipulator: manipulator to pass to generate_technique class method.
-  """
-  if generators is None:
-    techniques, generators = all_techniques()
-  g, args, kwargs = weighted_choice(generators)
-  return g.generate_technique(manipulator, *args, **kwargs)
-
-
-def weighted_choice(choices):
-  """ takes in a sequence of (choice, weight) tuples and randomly returns one """
-  total = sum(w for c, w in choices)
-  r = random.uniform(0, total)
-  upto = 0
-  for c, w in choices:
-    upto += w
-    if upto > r:
-      return c
-  return random.choice([c for c, w in choices])
-
-
-def all_techniques():
-  #import all modules in search to ensure techniques are Registered
-  for f in sorted(os.listdir(os.path.dirname(__file__))):
-    m = re.match(r'^(.*)[.]py$', f)
-    if m:
-      import_module('opentuner.search.'+m.group(1))
-
-  return the_registry, the_generator_registry
-
-def get_enabled(args):
-  techniques, generators = all_techniques()
-  if args.list_techniques:
-    for t in techniques:
-      print t.name
-    sys.exit(0)
-
-  if not args.technique:
-    # no techniques specified, default technique
-    args.technique = ['AUCBanditMetaTechniqueA']
-
-  for unknown in set(args.technique) - set(map(_.name, techniques)):
-    log.error('unknown technique %s', unknown)
-    raise Exception('Unknown technique: --technique={}'.format(unknown))
-
-  return [t for t in techniques if t.name in args.technique]
-
-def get_root(args):
-  from metatechniques import RoundRobinMetaSearchTechnique
-  enabled = get_enabled(args)
-  if len(enabled) == 1:
-    return enabled[0]
-  return RoundRobinMetaSearchTechnique(get_enabled(args))
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/tuningrunmain.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/tuningrunmain.py
deleted file mode 100644
index 9bcf1b5270286ee405373822d3919ae8854a24c3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/tuningrunmain.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-import argparse
-import copy
-import inspect
-import logging
-import math
-import os
-import socket
-import sys
-import time
-import uuid
-from datetime import datetime
-
-from opentuner import resultsdb
-from opentuner.search.driver import SearchDriver
-from opentuner.measurement.driver import MeasurementDriver
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--label',
-                       help="name for the TuningRun")
-argparser.add_argument('--print-search-space-size', action='store_true',
-                       help="Print out the estimated size of the search space and exit")
-argparser.add_argument('--database',
-                       help=("database to store tuning results in, see: "
-                             "http://docs.sqlalchemy.org/en/rel_0_8/core/engines.html#database-urls"))
-argparser.add_argument('--print-params','-pp',action='store_true',
-                       help='show parameters of the configuration being tuned')
-
-
-class CleanStop(Exception):
-  pass
-
-
-class LogFormatter(logging.Formatter):
-  def format(self, record):
-    record.relativeCreated /= 1000.0
-    try:
-      # python 2.7
-      return super(LogFormatter, self).format(record)
-    except:
-      # python 2.6
-      return _OldFormatter.format(self, record)
-
-
-_OldFormatter = logging.Formatter
-logging.Formatter = LogFormatter
-
-try:
-  # python 2.7
-  from logging.config import dictConfig
-except:
-  # python 2.6
-  from .utils.dictconfig import dictConfig
-
-the_logging_config = {
-  'version': 1,
-  'disable_existing_loggers': False,
-  'formatters': {'console': {'format': '[%(relativeCreated)6.0fs] '
-                                       '%(levelname)7s %(name)s: '
-                                       '%(message)s'},
-                 'file': {'format': '[%(asctime)-15s] '
-                                    '%(levelname)7s %(name)s: '
-                                    '%(message)s '
-                                    '@%(filename)s:%(lineno)d'}},
-  'handlers': {'console': {'class': 'logging.StreamHandler',
-                           'formatter': 'console',
-                           'level': 'INFO'},
-               'file': {'class': 'logging.FileHandler',
-                        'filename': 'opentuner.log',
-                        'formatter': 'file',
-                        'level': 'WARNING'}},
-  'loggers': {'': {'handlers': ['console', 'file'],
-                   'level': 'INFO',
-                   'propagate': True}}}
-
-
-def init_logging():
-  dictConfig(the_logging_config)
-  global init_logging
-  init_logging = lambda: None
-
-
-class TuningRunMain(object):
-  def __init__(self,
-               measurement_interface,
-               args,
-               search_driver=SearchDriver,
-               measurement_driver=MeasurementDriver):
-    init_logging()
-
-    manipulator = measurement_interface.manipulator()
-    if args.print_search_space_size:
-      print "10^{%.2f}" % math.log(manipulator.search_space_size(), 10)
-      sys.exit(0)
-    # show internal parameter representation
-    if args.print_params:
-      cfg = manipulator.seed_config()
-      d = manipulator.parameters_dict(cfg)
-      params_dict ={} 
-      for k in d: 
-        cls = d[k].__class__.__name__
-        p = (k, d[k].search_space_size())
-        if cls in params_dict:
-          params_dict[cls].append(p)
-        else:
-          params_dict[cls] = [p]
-      for k in params_dict:
-        print k, params_dict[k]
-        print
-      sys.exit(0)
-
-    input_manager = measurement_interface.input_manager()
-    objective = measurement_interface.objective()
-
-    if not args.database:
-      #args.database = 'sqlite://' #in memory
-      if not os.path.isdir('opentuner.db'):
-        os.mkdir('opentuner.db')
-      args.database = 'sqlite:///' + os.path.join('opentuner.db',
-                                                  socket.gethostname() + '.db')
-
-    if '://' not in args.database:
-      args.database = 'sqlite:///' + args.database
-
-    if not args.label:
-      args.label = 'unnamed'
-
-    #self.fake_commit = ('sqlite' in args.database)
-    self.fake_commit = True
-
-    self.args = args
-
-    self.engine, self.Session = resultsdb.connect(args.database)
-    self.session = self.Session()
-    self.tuning_run = None
-    self.search_driver_cls = search_driver
-    self.measurement_driver_cls = measurement_driver
-    self.measurement_interface = measurement_interface
-    self.input_manager = input_manager
-    self.manipulator = manipulator
-    self.objective = objective
-    self.objective_copy = copy.copy(objective)
-    self.last_commit_time = time.time()
-
-  def init(self):
-    if self.tuning_run is None:
-      program_version = (self.measurement_interface
-                         .db_program_version(self.session))
-      self.session.flush()
-      self.measurement_interface.prefix_hook(self.session)
-      self.tuning_run = (
-        resultsdb.models.TuningRun(
-          uuid=uuid.uuid4().hex,
-          name=self.args.label,
-          args=self.args,
-          start_date=datetime.now(),
-          program_version=program_version,
-          objective=self.objective_copy,
-        ))
-      self.session.add(self.tuning_run)
-
-      driver_kwargs = {
-        'args': self.args,
-        'input_manager': self.input_manager,
-        'manipulator': self.manipulator,
-        'measurement_interface': self.measurement_interface,
-        'objective': self.objective,
-        'session': self.session,
-        'tuning_run_main': self,
-        'tuning_run': self.tuning_run,
-        'extra_seeds': self.measurement_interface.seed_configurations(),
-        'extra_criteria': self.measurement_interface.extra_convergence_criteria
-      }
-
-      self.search_driver = self.search_driver_cls(**driver_kwargs)
-
-      self.measurement_driver = self.measurement_driver_cls(**driver_kwargs)
-      self.measurement_interface.set_driver(self.measurement_driver)
-      self.input_manager.set_driver(self.measurement_driver)
-
-      self.tuning_run.machine_class = self.measurement_driver.get_machine_class()
-      self.tuning_run.input_class = self.input_manager.get_input_class()
-
-  def commit(self, force=False):
-    if (force or not self.fake_commit or
-            time.time() - self.last_commit_time > 30):
-      self.session.commit()
-      self.last_commit_time = time.time()
-    else:
-      self.session.flush()
-
-  def main(self):
-    self.init()
-    try:
-      self.tuning_run.state = 'RUNNING'
-      self.commit(force=True)
-      self.search_driver.main()
-      if self.search_driver.best_result:
-        self.measurement_interface.save_final_config(
-            self.search_driver.best_result.configuration)
-      self.tuning_run.final_config = self.search_driver.best_result.configuration
-      self.tuning_run.state = 'COMPLETE'
-    except:
-      self.tuning_run.state = 'ABORTED'
-      raise
-    finally:
-      self.tuning_run.end_date = datetime.now()
-      self.commit(force=True)
-      self.session.close()
-
-  def results_wait(self, generation):
-    """called by search_driver to wait for results"""
-    #single process version:
-    self.measurement_interface.pre_process()  
-    self.measurement_driver.process_all()
-    self.measurement_interface.post_process()
-
-def main(interface, args, *pargs, **kwargs):
-  if inspect.isclass(interface):
-    interface = interface(args=args, *pargs, **kwargs)
-  return TuningRunMain(interface, args).main()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/adddeps.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/adddeps.py
deleted file mode 100644
index e2fc74064b605e92367907a7641442df0cf97cd9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/adddeps.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-import sys
-from os.path import normpath, realpath, dirname, join, isfile
-
-project_root = normpath(join(dirname(realpath(__file__)), '../..'))
-
-if 'venv' not in ','.join(sys.path):
-  venv_activate = join(project_root, 'venv/bin/activate_this.py')
-  if isfile(venv_activate):
-    execfile(venv_activate, dict(__file__=venv_activate))
-
-sys.path.insert(0, project_root)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/compactdb.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/compactdb.py
deleted file mode 100755
index 25a70d2d3b2658e877aa51a1462d5a9366635057..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/compactdb.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-
-if __name__ == '__main__':
-  import adddeps
-
-import argparse
-import logging
-import sys
-
-import opentuner
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger('opentuner.utils.compactdb')
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('database')
-argparser.add_argument('--level', type=int, default=2)
-
-
-def main(args):
-  if '://' not in args.database:
-    args.database = "sqlite:///" + args.database
-  engine, Session = opentuner.resultsdb.connect(args.database)
-  session = Session()
-
-  config_count = session.query(Configuration).count()
-  # result_count = session.query(Result).count()
-  # desired_result_count = session.query(DesiredResult).count()
-
-  if args.level >= 1:
-    q = (session.query(Configuration)
-         .filter(~Configuration.id.in_(session.query(Result.configuration_id)
-                                       .filter_by(was_new_best=True)
-                                       .subquery()))
-         .filter(Configuration.data != None))
-
-    log.info("%s: compacted %d of %d Configurations",
-             args.database,
-             q.update({'data': None}, False),
-             config_count)
-    session.commit()
-
-  if args.level >= 2:
-    session.execute('VACUUM;')
-    session.commit()
-
-  log.info('done')
-
-
-if __name__ == '__main__':
-  opentuner.tuningrunmain.init_logging()
-  sys.exit(main(argparser.parse_args()))
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/dictconfig.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/dictconfig.py
deleted file mode 100644
index 7b835a41084d1c24f40002e93940c574b60bb696..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/dictconfig.py
+++ /dev/null
@@ -1,544 +0,0 @@
-# This is a copy of the Python logging.config.dictconfig module,
-# reproduced with permission. It is provided here for backwards
-# compatibility for Python versions prior to 2.7.
-#
-# Copyright 2009-2010 by Vinay Sajip. All Rights Reserved.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose and without fee is hereby granted,
-# provided that the above copyright notice appear in all copies and that
-# both that copyright notice and this permission notice appear in
-# supporting documentation, and that the name of Vinay Sajip
-# not be used in advertising or publicity pertaining to distribution
-# of the software without specific, written prior permission.
-# VINAY SAJIP DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING
-# ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL
-# VINAY SAJIP BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR
-# ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER
-# IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-import logging.handlers
-import re
-import sys
-import types
-
-
-IDENTIFIER = re.compile('^[a-z_][a-z0-9_]*$', re.I)
-
-def valid_ident(s):
-    m = IDENTIFIER.match(s)
-    if not m:
-        raise ValueError('Not a valid Python identifier: %r' % s)
-    return True
-
-#
-# This function is defined in logging only in recent versions of Python
-#
-try:
-    from logging import _checkLevel
-except ImportError:
-    def _checkLevel(level):
-        if isinstance(level, int):
-            rv = level
-        elif str(level) == level:
-            if level not in logging._levelNames:
-                raise ValueError('Unknown level: %r' % level)
-            rv = logging._levelNames[level]
-        else:
-            raise TypeError('Level not an integer or a '
-                            'valid string: %r' % level)
-        return rv
-
-# The ConvertingXXX classes are wrappers around standard Python containers,
-# and they serve to convert any suitable values in the container. The
-# conversion converts base dicts, lists and tuples to their wrapped
-# equivalents, whereas strings which match a conversion format are converted
-# appropriately.
-#
-# Each wrapper should have a configurator attribute holding the actual
-# configurator to use for conversion.
-
-class ConvertingDict(dict):
-    """A converting dictionary wrapper."""
-
-    def __getitem__(self, key):
-        value = dict.__getitem__(self, key)
-        result = self.configurator.convert(value)
-        #If the converted value is different, save for next time
-        if value is not result:
-            self[key] = result
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-    def get(self, key, default=None):
-        value = dict.get(self, key, default)
-        result = self.configurator.convert(value)
-        #If the converted value is different, save for next time
-        if value is not result:
-            self[key] = result
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-    def pop(self, key, default=None):
-        value = dict.pop(self, key, default)
-        result = self.configurator.convert(value)
-        if value is not result:
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-class ConvertingList(list):
-    """A converting list wrapper."""
-    def __getitem__(self, key):
-        value = list.__getitem__(self, key)
-        result = self.configurator.convert(value)
-        #If the converted value is different, save for next time
-        if value is not result:
-            self[key] = result
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-    def pop(self, idx=-1):
-        value = list.pop(self, idx)
-        result = self.configurator.convert(value)
-        if value is not result:
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-        return result
-
-class ConvertingTuple(tuple):
-    """A converting tuple wrapper."""
-    def __getitem__(self, key):
-        value = tuple.__getitem__(self, key)
-        result = self.configurator.convert(value)
-        if value is not result:
-            if type(result) in (ConvertingDict, ConvertingList,
-                                ConvertingTuple):
-                result.parent = self
-                result.key = key
-        return result
-
-class BaseConfigurator(object):
-    """
-    The configurator base class which defines some useful defaults.
-    """
-
-    CONVERT_PATTERN = re.compile(r'^(?P<prefix>[a-z]+)://(?P<suffix>.*)$')
-
-    WORD_PATTERN = re.compile(r'^\s*(\w+)\s*')
-    DOT_PATTERN = re.compile(r'^\.\s*(\w+)\s*')
-    INDEX_PATTERN = re.compile(r'^\[\s*(\w+)\s*\]\s*')
-    DIGIT_PATTERN = re.compile(r'^\d+$')
-
-    value_converters = {
-        'ext' : 'ext_convert',
-        'cfg' : 'cfg_convert',
-    }
-
-    # We might want to use a different one, e.g. importlib
-    importer = __import__
-
-    def __init__(self, config):
-        self.config = ConvertingDict(config)
-        self.config.configurator = self
-
-    def resolve(self, s):
-        """
-        Resolve strings to objects using standard import and attribute
-        syntax.
-        """
-        name = s.split('.')
-        used = name.pop(0)
-        try:
-            found = self.importer(used)
-            for frag in name:
-                used += '.' + frag
-                try:
-                    found = getattr(found, frag)
-                except AttributeError:
-                    self.importer(used)
-                    found = getattr(found, frag)
-            return found
-        except ImportError:
-            e, tb = sys.exc_info()[1:]
-            v = ValueError('Cannot resolve %r: %s' % (s, e))
-            v.__cause__, v.__traceback__ = e, tb
-            raise v
-
-    def ext_convert(self, value):
-        """Default converter for the ext:// protocol."""
-        return self.resolve(value)
-
-    def cfg_convert(self, value):
-        """Default converter for the cfg:// protocol."""
-        rest = value
-        m = self.WORD_PATTERN.match(rest)
-        if m is None:
-            raise ValueError("Unable to convert %r" % value)
-        else:
-            rest = rest[m.end():]
-            d = self.config[m.groups()[0]]
-            #print d, rest
-            while rest:
-                m = self.DOT_PATTERN.match(rest)
-                if m:
-                    d = d[m.groups()[0]]
-                else:
-                    m = self.INDEX_PATTERN.match(rest)
-                    if m:
-                        idx = m.groups()[0]
-                        if not self.DIGIT_PATTERN.match(idx):
-                            d = d[idx]
-                        else:
-                            try:
-                                n = int(idx) # try as number first (most likely)
-                                d = d[n]
-                            except TypeError:
-                                d = d[idx]
-                if m:
-                    rest = rest[m.end():]
-                else:
-                    raise ValueError('Unable to convert '
-                                     '%r at %r' % (value, rest))
-        #rest should be empty
-        return d
-
-    def convert(self, value):
-        """
-        Convert values to an appropriate type. dicts, lists and tuples are
-        replaced by their converting alternatives. Strings are checked to
-        see if they have a conversion format and are converted if they do.
-        """
-        if not isinstance(value, ConvertingDict) and isinstance(value, dict):
-            value = ConvertingDict(value)
-            value.configurator = self
-        elif not isinstance(value, ConvertingList) and isinstance(value, list):
-            value = ConvertingList(value)
-            value.configurator = self
-        elif not isinstance(value, ConvertingTuple) and\
-                 isinstance(value, tuple):
-            value = ConvertingTuple(value)
-            value.configurator = self
-        return value
-
-    def configure_custom(self, config):
-        """Configure an object with a user-supplied factory."""
-        c = config.pop('()')
-        if not hasattr(c, '__call__') and hasattr(types, 'ClassType') and type(c) != types.ClassType:
-            c = self.resolve(c)
-        props = config.pop('.', None)
-        # Check for valid identifiers
-        kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
-        result = c(**kwargs)
-        if props:
-            for name, value in props.items():
-                setattr(result, name, value)
-        return result
-
-    def as_tuple(self, value):
-        """Utility function which converts lists to tuples."""
-        if isinstance(value, list):
-            value = tuple(value)
-        return value
-
-class DictConfigurator(BaseConfigurator):
-    """
-    Configure logging using a dictionary-like object to describe the
-    configuration.
-    """
-
-    def configure(self):
-        """Do the configuration."""
-
-        config = self.config
-        if 'version' not in config:
-            raise ValueError("dictionary doesn't specify a version")
-        if config['version'] != 1:
-            raise ValueError("Unsupported version: %s" % config['version'])
-        incremental = config.pop('incremental', False)
-        EMPTY_DICT = {}
-        logging._acquireLock()
-        try:
-            if incremental:
-                handlers = config.get('handlers', EMPTY_DICT)
-                # incremental handler config only if handler name
-                # ties in to logging._handlers (Python 2.7)
-                if sys.version_info[:2] == (2, 7):
-                    for name in handlers:
-                        if name not in logging._handlers:
-                            raise ValueError('No handler found with '
-                                             'name %r'  % name)
-                        else:
-                            try:
-                                handler = logging._handlers[name]
-                                handler_config = handlers[name]
-                                level = handler_config.get('level', None)
-                                if level:
-                                    handler.setLevel(_checkLevel(level))
-                            except StandardError as e:
-                                raise ValueError('Unable to configure handler '
-                                                 '%r: %s' % (name, e))
-                loggers = config.get('loggers', EMPTY_DICT)
-                for name in loggers:
-                    try:
-                        self.configure_logger(name, loggers[name], True)
-                    except StandardError as e:
-                        raise ValueError('Unable to configure logger '
-                                         '%r: %s' % (name, e))
-                root = config.get('root', None)
-                if root:
-                    try:
-                        self.configure_root(root, True)
-                    except StandardError as e:
-                        raise ValueError('Unable to configure root '
-                                         'logger: %s' % e)
-            else:
-                disable_existing = config.pop('disable_existing_loggers', True)
-
-                logging._handlers.clear()
-                del logging._handlerList[:]
-
-                # Do formatters first - they don't refer to anything else
-                formatters = config.get('formatters', EMPTY_DICT)
-                for name in formatters:
-                    try:
-                        formatters[name] = self.configure_formatter(
-                                                            formatters[name])
-                    except StandardError as e:
-                        raise ValueError('Unable to configure '
-                                         'formatter %r: %s' % (name, e))
-                # Next, do filters - they don't refer to anything else, either
-                filters = config.get('filters', EMPTY_DICT)
-                for name in filters:
-                    try:
-                        filters[name] = self.configure_filter(filters[name])
-                    except StandardError as e:
-                        raise ValueError('Unable to configure '
-                                         'filter %r: %s' % (name, e))
-
-                # Next, do handlers - they refer to formatters and filters
-                # As handlers can refer to other handlers, sort the keys
-                # to allow a deterministic order of configuration
-                handlers = config.get('handlers', EMPTY_DICT)
-                for name in sorted(handlers):
-                    try:
-                        handler = self.configure_handler(handlers[name])
-                        handler.name = name
-                        handlers[name] = handler
-                    except StandardError as e:
-                        raise ValueError('Unable to configure handler '
-                                         '%r: %s' % (name, e))
-                # Next, do loggers - they refer to handlers and filters
-
-                #we don't want to lose the existing loggers,
-                #since other threads may have pointers to them.
-                #existing is set to contain all existing loggers,
-                #and as we go through the new configuration we
-                #remove any which are configured. At the end,
-                #what's left in existing is the set of loggers
-                #which were in the previous configuration but
-                #which are not in the new configuration.
-                root = logging.root
-                existing = list(root.manager.loggerDict)
-                #The list needs to be sorted so that we can
-                #avoid disabling child loggers of explicitly
-                #named loggers. With a sorted list it is easier
-                #to find the child loggers.
-                existing.sort()
-                #We'll keep the list of existing loggers
-                #which are children of named loggers here...
-                child_loggers = []
-                #now set up the new ones...
-                loggers = config.get('loggers', EMPTY_DICT)
-                for name in loggers:
-                    if name in existing:
-                        i = existing.index(name)
-                        prefixed = name + "."
-                        pflen = len(prefixed)
-                        num_existing = len(existing)
-                        i = i + 1 # look at the entry after name
-                        while (i < num_existing) and\
-                              (existing[i][:pflen] == prefixed):
-                            child_loggers.append(existing[i])
-                            i = i + 1
-                        existing.remove(name)
-                    try:
-                        self.configure_logger(name, loggers[name])
-                    except StandardError as e:
-                        raise ValueError('Unable to configure logger '
-                                         '%r: %s' % (name, e))
-
-                #Disable any old loggers. There's no point deleting
-                #them as other threads may continue to hold references
-                #and by disabling them, you stop them doing any logging.
-                #However, don't disable children of named loggers, as that's
-                #probably not what was intended by the user.
-                for log in existing:
-                    logger = root.manager.loggerDict[log]
-                    if log in child_loggers:
-                        logger.level = logging.NOTSET
-                        logger.handlers = []
-                        logger.propagate = True
-                    elif disable_existing:
-                        logger.disabled = True
-
-                # And finally, do the root logger
-                root = config.get('root', None)
-                if root:
-                    try:
-                        self.configure_root(root)
-                    except StandardError as e:
-                        raise ValueError('Unable to configure root '
-                                         'logger: %s' % e)
-        finally:
-            logging._releaseLock()
-
-    def configure_formatter(self, config):
-        """Configure a formatter from a dictionary."""
-        if '()' in config:
-            factory = config['()'] # for use in exception handler
-            try:
-                result = self.configure_custom(config)
-            except TypeError as te:
-                if "'format'" not in str(te):
-                    raise
-                #Name of parameter changed from fmt to format.
-                #Retry with old name.
-                #This is so that code can be used with older Python versions
-                #(e.g. by Django)
-                config['fmt'] = config.pop('format')
-                config['()'] = factory
-                result = self.configure_custom(config)
-        else:
-            fmt = config.get('format', None)
-            dfmt = config.get('datefmt', None)
-            result = logging.Formatter(fmt, dfmt)
-        return result
-
-    def configure_filter(self, config):
-        """Configure a filter from a dictionary."""
-        if '()' in config:
-            result = self.configure_custom(config)
-        else:
-            name = config.get('name', '')
-            result = logging.Filter(name)
-        return result
-
-    def add_filters(self, filterer, filters):
-        """Add filters to a filterer from a list of names."""
-        for f in filters:
-            try:
-                filterer.addFilter(self.config['filters'][f])
-            except StandardError as e:
-                raise ValueError('Unable to add filter %r: %s' % (f, e))
-
-    def configure_handler(self, config):
-        """Configure a handler from a dictionary."""
-        formatter = config.pop('formatter', None)
-        if formatter:
-            try:
-                formatter = self.config['formatters'][formatter]
-            except StandardError as e:
-                raise ValueError('Unable to set formatter '
-                                 '%r: %s' % (formatter, e))
-        level = config.pop('level', None)
-        filters = config.pop('filters', None)
-        if '()' in config:
-            c = config.pop('()')
-            if not hasattr(c, '__call__') and hasattr(types, 'ClassType') and type(c) != types.ClassType:
-                c = self.resolve(c)
-            factory = c
-        else:
-            klass = self.resolve(config.pop('class'))
-            #Special case for handler which refers to another handler
-            if issubclass(klass, logging.handlers.MemoryHandler) and\
-                'target' in config:
-                try:
-                    config['target'] = self.config['handlers'][config['target']]
-                except StandardError as e:
-                    raise ValueError('Unable to set target handler '
-                                     '%r: %s' % (config['target'], e))
-            elif issubclass(klass, logging.handlers.SMTPHandler) and\
-                'mailhost' in config:
-                config['mailhost'] = self.as_tuple(config['mailhost'])
-            elif issubclass(klass, logging.handlers.SysLogHandler) and\
-                'address' in config:
-                config['address'] = self.as_tuple(config['address'])
-            factory = klass
-        kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
-        try:
-            result = factory(**kwargs)
-        except TypeError as te:
-            if "'stream'" not in str(te):
-                raise
-            #The argument name changed from strm to stream
-            #Retry with old name.
-            #This is so that code can be used with older Python versions
-            #(e.g. by Django)
-            kwargs['strm'] = kwargs.pop('stream')
-            result = factory(**kwargs)
-        if formatter:
-            result.setFormatter(formatter)
-        if level is not None:
-            result.setLevel(_checkLevel(level))
-        if filters:
-            self.add_filters(result, filters)
-        return result
-
-    def add_handlers(self, logger, handlers):
-        """Add handlers to a logger from a list of names."""
-        for h in handlers:
-            try:
-                logger.addHandler(self.config['handlers'][h])
-            except StandardError as e:
-                raise ValueError('Unable to add handler %r: %s' % (h, e))
-
-    def common_logger_config(self, logger, config, incremental=False):
-        """
-        Perform configuration which is common to root and non-root loggers.
-        """
-        level = config.get('level', None)
-        if level is not None:
-            logger.setLevel(_checkLevel(level))
-        if not incremental:
-            #Remove any existing handlers
-            for h in logger.handlers[:]:
-                logger.removeHandler(h)
-            handlers = config.get('handlers', None)
-            if handlers:
-                self.add_handlers(logger, handlers)
-            filters = config.get('filters', None)
-            if filters:
-                self.add_filters(logger, filters)
-
-    def configure_logger(self, name, config, incremental=False):
-        """Configure a non-root logger from a dictionary."""
-        logger = logging.getLogger(name)
-        self.common_logger_config(logger, config, incremental)
-        propagate = config.get('propagate', None)
-        if propagate is not None:
-            logger.propagate = propagate
-
-    def configure_root(self, config, incremental=False):
-        """Configure a root logger from a dictionary."""
-        root = logging.getLogger()
-        self.common_logger_config(root, config, incremental)
-
-dictConfigClass = DictConfigurator
-
-def dictConfig(config):
-    """Configure logging using a dictionary."""
-    dictConfigClass(config).configure()
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats.py
deleted file mode 100755
index 99449c8a900a3f8ad53c6c12fbbc4d2197b1cb45..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats.py
+++ /dev/null
@@ -1,468 +0,0 @@
-#!/usr/bin/env python
-
-if __name__ == '__main__':
-  import adddeps
-
-import argparse
-import csv
-import hashlib
-import itertools
-import logging
-import math
-import os
-import sqlalchemy.orm.exc
-import subprocess
-import sys
-
-from collections import defaultdict
-from fn import _
-from fn import Stream
-from fn.iters import repeat
-from pprint import pprint
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger('opentuner.utils.stats')
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('--label')
-argparser.add_argument('--stats', action='store_true',
-                       help="run in stats mode")
-argparser.add_argument('--by-request-count', action='store_true',
-                       help='report stats by request count')
-argparser.add_argument('--stats-quanta', type=float, default=10,
-                       help="step size in seconds for binning with --stats")
-argparser.add_argument('--stats-dir', default='stats',
-                       help="directory to output --stats to")
-argparser.add_argument('--stats-input', default="opentuner.db")
-argparser.add_argument('--min-runs',  type=int, default=1,
-                       help="ignore series with less then N runs")
-
-PCTSTEPS = map(_/20.0, xrange(21))
-
-def mean(vals):
-  n = 0.0
-  d = 0.0
-  for v in vals:
-    if v is not None:
-      n += v
-      d += 1.0
-  if d == 0.0:
-    return None
-  return n/d
-
-def median(vals):
-  vals = sorted(vals)
-  a = (len(vals)-1)/2
-  b = (len(vals))/2
-  return (vals[a]+vals[b])/2.0
-
-def percentile(vals, pct):
-  vals = sorted(vals)
-  pos = (len(vals)-1) * pct
-  a = int(math.floor(pos))
-  b = min(len(vals) - 1, a + 1)
-  return (1.0-(pos-a))*vals[a] + (pos-a)*vals[b]
-
-def variance(vals):
-  vals = filter(lambda x: x is not None, vals)
-  avg = mean(vals)
-  if avg is None:
-    return None
-  if avg in (float('inf'), float('-inf')):
-    return avg
-  return mean(map((_ - avg) ** 2, vals))
-
-def stddev(vals):
-  var = variance(vals)
-  if var is None:
-    return None
-  return math.sqrt(var)
-
-def hash_args(x):
-  d = dict(vars(x))
-  for k in ('database', 'results_log', 'results_log_details'):
-    d[k] = None
-  return hashlib.sha256(str(sorted(d.items()))).hexdigest()[:20]
-
-def run_label(tr, short = False):
-  techniques = ','.join(tr.args.technique)
-  if not tr.name or tr.name=='unnamed':
-    if short:
-      return techniques
-    else:
-      return "%s_%s" % (techniques, hash_args(tr.args)[:6])
-  else:
-    return tr.name
-
-def run_dir(base, tr):
-  return os.path.join(base,
-                      tr.program.project,
-                      tr.program.name.split('/')[-1],
-                      tr.program_version.version[:16])
-
-class StatsMain(object):
-  def __init__(self, args):
-    self.args = args
-    path = args.stats_input
-    self.dbs = list()
-    for f in os.listdir(path):
-      if 'journal' in f:
-        continue
-      try:
-        e, sm = resultsdb.connect('sqlite:///'+os.path.join(path, f))
-        self.dbs.append(sm())
-      except:
-        log.error('failed to load database: %s', 
-                  os.path.join(path, f),
-                  exc_info=True)
-
-  def main(self):
-    dir_label_runs = defaultdict(lambda: defaultdict(list))
-    for session in self.dbs:
-      q = (session.query(resultsdb.models.TuningRun)
-          .filter_by(state='COMPLETE')
-          .order_by('name'))
-
-      if self.args.label:
-        q = q.filter(TuningRun.name.in_(
-          map(str.strip,self.args.label.split(','))))
-
-      for tr in q:
-        d = run_dir(self.args.stats_dir, tr)
-        d = os.path.normpath(d)
-        dir_label_runs[d][run_label(tr)].append((tr, session))
-
-    summary_report = defaultdict(lambda: defaultdict(list))
-    for d, label_runs in dir_label_runs.iteritems():
-      if not os.path.isdir(d):
-        os.makedirs(d)
-      session = label_runs.values()[0][0][1]
-      objective = label_runs.values()[0][0][0].objective
-      all_run_ids = map(_[0].id, itertools.chain(*label_runs.values()))
-      q = (session.query(Result)
-           .filter(Result.tuning_run_id.in_(all_run_ids))
-           .filter(Result.time < float('inf'))
-           .filter_by(was_new_best=True, state='OK'))
-      total = q.count()
-      if total == 0: 
-          continue
-      q = objective.filter_acceptable(q)
-      acceptable = q.count()
-      q = q.order_by(*objective.result_order_by_terms())
-      best = q.limit(1).one()
-      worst = q.offset(acceptable-1).limit(1).one()
-
-      map(len, label_runs.values())
-
-      log.info("%s -- best %.4f / worst %.f4 "
-               "-- %d of %d acceptable -- %d techniques with %d to %d runs",
-               d,
-               best.time,
-               worst.time,
-               acceptable,
-               total,
-               len(label_runs.values()),
-               min(map(len, label_runs.values())),
-               max(map(len, label_runs.values())))
-
-      for label, runs in sorted(label_runs.items()):
-        if len(runs) < self.args.min_runs:
-          print len(runs) ,self.args.min_runs
-          continue
-        log.debug('%s/%s has %d runs %s',d, label, len(runs), runs[0][0].args.technique)
-        self.combined_stats_over_time(d, label, runs, objective, worst, best)
-
-        final_scores = list()
-        for run, session in runs:
-          try:
-            final = (session.query(Result)
-                    .filter_by(tuning_run=run,
-                               configuration=run.final_config)
-                    .limit(1)
-                    .one())
-          except sqlalchemy.orm.exc.NoResultFound:
-            continue
-          final_scores.append(objective.stats_quality_score(final, worst, best))
-        final_scores.sort()
-        if final_scores:
-          norm = objective.stats_quality_score(best, worst, best)
-          if norm > 0.00001:
-            summary_report[d][run_label(run, short=True)] = (
-                percentile(final_scores, 0.5) / norm,
-                percentile(final_scores, 0.1) / norm,
-                percentile(final_scores, 0.9) / norm,
-              )
-          else:
-            summary_report[d][run_label(run, short=True)] = (
-                percentile(final_scores, 0.5) + norm + 1.0,
-                percentile(final_scores, 0.1) + norm + 1.0,
-                percentile(final_scores, 0.9) + norm + 1.0,
-              )
-
-
-    with open(self.args.stats_dir+ "/summary.dat", 'w') as o:
-      # make summary report
-      keys = sorted(reduce(set.union,
-                           [set(x.keys()) for x in summary_report.values()],
-                           set()))
-      print >>o, '#####',
-      for k in keys:
-        print >>o, k,
-      print >>o
-      for d, label_vals in sorted(summary_report.items()):
-        print >>o, d.split('/')[-2],
-        for k in keys:
-          if k in label_vals:
-            print >>o, '-', label_vals[k][0], label_vals[k][1], label_vals[k][2],
-          else:
-            print >>o, '-', '-', '-', '-',
-        print >>o
-
-    if keys:
-      plotcmd = ["""1 w lines lt 1 lc rgb "black" notitle""",
-                 """'summary.dat' using 3:4:5:xtic(1) ti "%s" """ % keys[0]]
-      for n, k in enumerate(keys[1:]):
-        plotcmd.append("""'' using %d:%d:%d ti "%s" """ % (
-                        4*n + 7,
-                        4*n + 8,
-                        4*n + 9,
-                        k))
-      self.gnuplot_summary_file(self.args.stats_dir, 'summary', plotcmd)
-
-
-
-    for d, label_runs in dir_label_runs.iteritems():
-      labels = [k for k,v in label_runs.iteritems()
-                if len(v)>=self.args.min_runs]
-      self.gnuplot_file(d,
-                        "medianperfe",
-                        ['"%s_percentiles.dat" using 1:12:4:18 with errorbars title "%s"' % (l,l) for l in labels])
-      self.gnuplot_file(d,
-                        "meanperfe",
-                        ['"%s_percentiles.dat" using 1:21:4:18 with errorbars title "%s"' % (l,l) for l in labels])
-      self.gnuplot_file(d,
-                        "medianperfl",
-                        ['"%s_percentiles.dat" using 1:12 with lines title "%s"' % (l,l) for l in labels])
-      self.gnuplot_file(d,
-                        "meanperfl",
-                        ['"%s_percentiles.dat" using 1:21 with lines title "%s"' % (l,l) for l in labels])
-
-    # print
-    # print "10% Scores", d
-    # pprint(self.technique_scores(d, labels, '0.1'))
-    # print
-    # print "90% Scores", d
-    # pprint(self.technique_scores(d, labels, '0.9'))
-    # print
-    # print "Mean Scores", d
-    # pprint(self.technique_scores(d, labels, 'mean'))
-      print
-      print "Median Scores", d
-      pprint(self.technique_scores(d, labels, '0.5'))
-
-
-  def technique_scores(self, directory, labels, ykey, xkey='#sec', factor=10.0):
-    max_duration = None
-    min_value = float('inf')
-    for label in labels:
-      try:
-        dr = csv.DictReader(open(os.path.join(directory,label+"_percentiles.dat")), delimiter=' ', lineterminator='\n')
-        lastrow = list(dr)[-1]
-        max_duration = max(max_duration, float(lastrow[xkey]))
-        min_value = min(min_value, float(lastrow[ykey]))
-      except:
-        log.exception("failed computing score")
-
-    scores = list()
-
-    for label in labels:
-      try:
-        dr = csv.DictReader(open(os.path.join(directory,label+"_percentiles.dat")), delimiter=' ', lineterminator='\n')
-        score = 0.0
-        lastsec = 0.0
-        value = float('inf')
-        for row in dr:
-          duration = float(row[xkey]) - lastsec
-          lastsec = float(row[xkey])
-          value = float(row[ykey])
-          score += duration * (value - min_value)
-        score += (factor*max_duration - lastsec) * (value - min_value)
-        scores.append((score, label))
-      except:
-        log.exception("failed computing score")
-
-    return sorted(scores)
-
-
-  def combined_stats_over_time(self,
-                               output_dir,
-                               label,
-                               runs,
-                               objective,
-                               worst,
-                               best,
-                               ):
-    """
-    combine stats_over_time() vectors for multiple runs
-    """
-
-    #extract_fn = lambda dr: objective.stats_quality_score(dr.result, worst, best)
-    extract_fn = _.result.time
-    combine_fn = min
-    no_data = 999
-
-    log.debug("writing stats for %s to %s", label, output_dir)
-    by_run = [self.stats_over_time(session, run, extract_fn, combine_fn, no_data)
-              for run, session in runs]
-    max_len = max(map(len, by_run))
-
-    by_run_streams = [Stream() << x << repeat(x[-1], max_len-len(x))
-                      for x in by_run]
-    by_quanta = zip(*by_run_streams[:])
-
-    def data_file(suffix, headers, value_function):
-      with open(os.path.join(output_dir, label+suffix), 'w') as fd:
-        out = csv.writer(fd, delimiter=' ', lineterminator='\n')
-        out.writerow(['#sec'] + headers)
-        for quanta, values in enumerate(by_quanta):
-          sec = quanta*self.args.stats_quanta
-          out.writerow([sec] + value_function(values))
-
-   #data_file('_details.dat',
-   #          map(lambda x: 'run%d'%x, xrange(max_len)),
-   #          list)
-   #self.gnuplot_file(output_dir,
-   #                  label+'_details',
-   #                  [('"'+label+'_details.dat"'
-   #                    ' using 1:%d'%i +
-   #                    ' with lines'
-   #                    ' title "Run %d"'%i)
-   #                   for i in xrange(max_len)])
-
-    data_file('_mean.dat',
-              ['#sec', 'mean', 'stddev'],
-              lambda values: [mean(values), stddev(values)])
-    self.gnuplot_file(output_dir,
-                      label+'_mean',
-                      ['"'+label+'_mean.dat" using 1:2 with lines title "Mean"'])
-
-    def extract_percentiles(values):
-      values = sorted(values)
-      return ([values[int(round(p*(len(values)-1)))] for p in PCTSTEPS]
-             + [mean(values)])
-    data_file("_percentiles.dat", PCTSTEPS + ['mean'], extract_percentiles)
-    self.gnuplot_file(output_dir,
-                      label+'_percentiles',
-                      reversed([
-                        '"'+label+'_percentiles.dat" using 1:2  with lines title "0%"',
-                      # '""                          using 1:3  with lines title "5%"',
-                        '""                          using 1:4  with lines title "10%"',
-                      # '""                          using 1:5  with lines title "25%"',
-                        '""                          using 1:6  with lines title "20%"',
-                      # '""                          using 1:7  with lines title "35%"',
-                        '""                          using 1:8  with lines title "30%"',
-                      # '""                          using 1:9  with lines title "45%"',
-                        '""                          using 1:10 with lines title "40%"',
-                      # '""                          using 1:11 with lines title "55%"',
-                        '""                          using 1:12 with lines title "50%"',
-                      # '""                          using 1:13 with lines title "65%"',
-                        '""                          using 1:14 with lines title "70%"',
-                      # '""                          using 1:15 with lines title "75%"',
-                        '""                          using 1:16 with lines title "80%"',
-                      # '""                          using 1:17 with lines title "85%"',
-                        '""                          using 1:18 with lines title "90%"',
-                      # '""                          using 1:19 with lines title "95%"',
-                        '"'+label+'_percentiles.dat" using 1:20 with lines title "100%"',
-                       ]))
-
-  def gnuplot_file(self, output_dir, prefix, plotcmd):
-    with open(os.path.join(output_dir, prefix+'.gnuplot'), 'w') as fd:
-      print >>fd, 'set terminal postscript eps enhanced color'
-      print >>fd, 'set output "%s"' % (prefix+'.eps')
-      print >>fd, 'set ylabel "Execution Time (seconds)"'
-      print >>fd, 'set xlabel "Autotuning Time (seconds)"'
-      print >>fd, 'plot', ',\\\n'.join(plotcmd)
-
-    try:
-      subprocess.call(['gnuplot', prefix+'.gnuplot'], cwd=output_dir, stdin=None)
-    except OSError:
-      log.error("command gnuplot not found")
-
-  def gnuplot_summary_file(self, output_dir, prefix, plotcmd):
-    with open(os.path.join(output_dir, prefix+'.gnuplot'), 'w') as fd:
-      print >>fd, 'set terminal postscript eps enhanced color'
-      print >>fd, 'set output "%s"' % (prefix+'.eps')
-      print >>fd, '''
-set boxwidth 0.9
-set style fill solid 1.00 border 0
-set style histogram errorbars gap 2 lw 1
-set style data histograms
-set xtics rotate by -45
-set bars 0.5
-set yrange [0:20]
-
-set yrange [0:10]
-set key out vert top left
-set size 1.5,1
-set ytics 1
-
-'''
-      print >>fd, 'plot', ',\\\n'.join(plotcmd)
-    subprocess.call(['gnuplot', prefix+'.gnuplot'], cwd=output_dir, stdin=None)
-
-
-  def stats_over_time(self,
-                      session,
-                      run,
-                      extract_fn,
-                      combine_fn,
-                      no_data = None):
-    """
-    return reduce(combine_fn, map(extract_fn, data)) for each quanta of the
-    tuning run
-    """
-    value_by_quanta = [ no_data ]
-    start_date = run.start_date
-
-    subq = (session.query(Result.id)
-           .filter_by(tuning_run = run, was_new_best = True, state='OK'))
-
-    q = (session.query(DesiredResult)
-         .join(Result)
-         .filter(DesiredResult.state=='COMPLETE',
-                 DesiredResult.tuning_run == run,
-                 DesiredResult.result_id.in_(subq.subquery()))
-         .order_by(DesiredResult.request_date))
-
-    first_id = None
-    for dr in q:
-      if first_id is None:
-        first_id = dr.id
-      td = (dr.request_date - start_date)
-      duration = td.seconds + (td.days * 24 * 3600.0)
-      if self.args.by_request_count:
-        quanta = dr.id - first_id
-      else:
-        quanta = int(duration / self.args.stats_quanta)
-      while len(value_by_quanta) <= quanta:
-        value_by_quanta.append(value_by_quanta[-1])
-
-      if value_by_quanta[-1] is no_data:
-        value_by_quanta[-1] = extract_fn(dr)
-      else:
-        value_by_quanta[-1] = combine_fn(value_by_quanta[-1], extract_fn(dr))
-
-    return value_by_quanta
-
-
-
-
-
-if __name__ == '__main__':
-  opentuner.tuningrunmain.init_logging()
-  sys.exit(StatsMain(argparser.parse_args()).main())
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats_matplotlib.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats_matplotlib.py
deleted file mode 100644
index 54e9132a662fa68089ce3d0ba00cb6502bd2c712..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/opentuner/utils/stats_matplotlib.py
+++ /dev/null
@@ -1,290 +0,0 @@
-#!usr/bin/python
-
-if __name__ == '__main__':
-  import adddeps
-
-import itertools
-import math
-import matplotlib.pyplot as plt
-import numpy
-import os
-import sqlalchemy
-import sqlalchemy.orm.exc
-
-from collections import defaultdict
-from fn import _
-from fn import Stream
-from fn.iters import repeat
-from opentuner import resultsdb
-
-PCTSTEPS = map(_/20.0, xrange(21))
-
-
-def mean(vals):
-  """
-  Arguments,
-    vals: List of floating point numbers
-  Returns,
-    The mean of the numbers in the input list
-    None if all values in the list are None
-  """
-  filtered_values = [float(x) for x in vals if x is not None]
-  if len(filtered_values) == 0:
-    return None
-  return numpy.mean(numpy.array(filtered_values))
-
-
-def stddev(vals):
-  """
-  Arguments,
-    vals: List of floating point numbers
-  Returns,
-    The standard deviation of numbers in the input list
-    None if all values in the list are None
-  """
-  filtered_values = [float(x) for x in vals if x is not None]
-  if len(filtered_values) == 0:
-    return None
-  return math.sqrt(numpy.var(numpy.array(filtered_values)))
-
-
-def get_dbs(path, db_type='sqlite:///'):
-  """
-  Arguments,
-    path: Path of directory containing .db files
-  Returns,
-    A list of (engine, session) pairs to the dbs pointed to by
-    the db files
-  """
-  dbs = list()
-  for f in os.listdir(path):
-    if 'journal' in f:
-      continue
-    try:
-      db_path = os.path.join(path, f)
-      e, sm = resultsdb.connect(db_type + db_path)
-      dbs.append(sm())
-    except Exception as e:
-      print e
-      print "Error encountered while connecting to db"
-  return dbs
-
-
-def matplotlibplot_file(labels, xlim = None, ylim = None, disp_types=['median']):
-  """
-  Arguments,
-    labels: List of labels that need to be included in the plot
-    xlim: Integer denoting the maximum X-coordinate in the plot
-    ylim: Integer denoting the maximum Y-coordinate in the plot
-    disp_types: List of measures that are to be displayed in the plot
-  Returns,
-    A figure object representing the required plot
-  """
-
-  figure = plt.figure()
-  values = get_values(labels)
-  for label in values:
-    (mean_values, percentile_values) = values[label]
-    for disp_type in disp_types:
-      cols = None
-      data = percentile_values
-
-      if disp_type == 'median':
-        cols = [11]
-      elif disp_type == 'mean':
-        cols = [1]
-        data = mean_values
-      elif disp_type == 'all_percentiles':
-        cols = range(1,22)
-
-      plotted_data = [[] for x in xrange(len(cols))]
-
-      x_indices = []
-      for data_point in data[1:]:
-        x_indices.append(int(data_point[0]))
-        for i in range(0, len(cols)):
-          plotted_data[i].append(float(data_point[cols[i]]))
-      args = []
-      for to_plot in plotted_data:
-        args.append(x_indices)
-        args.append(to_plot)
-
-      plt.plot(*args, label='%s(%s)' % (label, disp_type))
-
-  if xlim is not None:
-    plt.xlim(xlim)
-  if ylim is not None:
-    plt.ylim(ylim)
-
-  plt.xlabel('Autotuning Time (seconds)')
-  plt.ylabel('Execution Time (seconds)')
-  plt.legend(loc='upper right')
-  return figure
-
-
-def run_label(tr):
-  techniques = ','.join(tr.args.technique)
-  if not tr.name or tr.name == 'unnamed':
-    return techniques
-  return tr.name
-
-
-def combined_stats_over_time(label,
-                             runs,
-                             objective,
-                             worst,
-                             best,
-                             ):
-  """
-  combine stats_over_time() vectors for multiple runs
-  """
-
-  extract_fn = _.result.time
-  combine_fn = min
-  no_data = 999
-
-  by_run = [stats_over_time(session, run, extract_fn, combine_fn, no_data)
-            for run, session in runs]
-  max_len = max(map(len, by_run))
-
-  by_run_streams = [Stream() << x << repeat(x[-1], max_len-len(x))
-                    for x in by_run]
-  by_quanta = zip(*by_run_streams[:])
-
-  # TODO: Fix this, this variable should be configurable
-  stats_quanta = 10
-  def get_data(value_function):
-    final_values = []
-    for quanta, values in enumerate(by_quanta):
-      sec = quanta*stats_quanta
-      final_values.append([sec] + value_function(values))
-    return final_values
-
-  mean_values = get_data(lambda values: [mean(values), stddev(values)])
-
-  def extract_percentiles(values):
-    values = sorted(values)
-    return ([values[int(round(p*(len(values)-1)))] for p in PCTSTEPS]
-           + [mean(values)])
-  percentile_values = get_data(extract_percentiles)
-  return mean_values, percentile_values
-
-
-def stats_over_time(session,
-                    run,
-                    extract_fn,
-                    combine_fn,
-                    no_data = None):
-  """
-  return reduce(combine_fn, map(extract_fn, data)) for each quanta of the
-  tuning run
-  """
-  value_by_quanta = [ no_data ]
-  start_date = run.start_date
-
-  subq = (session.query(resultsdb.models.Result.id)
-         .filter_by(tuning_run = run, was_new_best = True, state='OK'))
-
-  q = (session.query(resultsdb.models.DesiredResult)
-       .join(resultsdb.models.Result)
-       .filter(resultsdb.models.DesiredResult.state=='COMPLETE',
-               resultsdb.models.DesiredResult.tuning_run == run,
-               resultsdb.models.DesiredResult.result_id.in_(subq.subquery()))
-       .order_by(resultsdb.models.DesiredResult.request_date))
-
-  first_id = None
-  for dr in q:
-    if first_id is None:
-      first_id = dr.id
-    td = (dr.request_date - start_date)
-    duration = td.seconds + (td.days * 24 * 3600.0)
-    # TODO: Make this variable configurable
-    by_request_count = True
-    stats_quanta = 10
-    if by_request_count:
-      quanta = dr.id - first_id
-    else:
-      quanta = int(duration / stats_quanta)
-    while len(value_by_quanta) <= quanta:
-      value_by_quanta.append(value_by_quanta[-1])
-
-    if value_by_quanta[-1] is no_data:
-      value_by_quanta[-1] = extract_fn(dr)
-    else:
-      value_by_quanta[-1] = combine_fn(value_by_quanta[-1], extract_fn(dr))
-
-  return value_by_quanta
-
-
-def get_all_labels():
-  """
-  Returns,
-    List of labels that are in the complete state
-  """
-  dbs = get_dbs(os.getcwd())
-  all_labels = list()
-  for db in dbs:
-    all_labels.extend(db.query(resultsdb.models.TuningRun.name)
-                        .filter_by(state='COMPLETE')
-                        .distinct()
-                        .all())
-  all_labels = [str(element[0]) for element in all_labels]
-  return all_labels
-
-
-def get_values(labels):
-  """
-  Arguments,
-    labels: List of labels whose values are of interest
-  Returns,
-    A list of (mean, percentile) tuples, corresponding to the
-    provided list of labels
-  """
-  dbs = get_dbs(os.getcwd())
-  dir_label_runs = defaultdict(lambda: defaultdict(list))
-  for db in dbs:
-    q = (db.query(resultsdb.models.TuningRun)
-            .filter_by(state='COMPLETE')
-            .order_by('name'))
-    if labels:
-      q = q.filter(resultsdb.models.TuningRun.name.in_(labels))
-    for tr in q:
-      dir_label_runs[run_label(tr)][run_label(tr)].append((tr, db))
-  all_run_ids = list()
-  returned_values = {}
-  for d, label_runs in dir_label_runs.iteritems():
-    all_run_ids = map(_[0].id, itertools.chain(*label_runs.values()))
-    session = label_runs.values()[0][0][1]
-    objective = label_runs.values()[0][0][0].objective
-
-    q = (session.query(resultsdb.models.Result)
-         .filter(resultsdb.models.Result.tuning_run_id.in_(all_run_ids))
-         .filter(resultsdb.models.Result.time < float('inf'))
-         .filter_by(was_new_best=True, state='OK'))
-    total = q.count()
-    q = objective.filter_acceptable(q)
-    acceptable = q.count()
-    q = q.order_by(*objective.result_order_by_terms())
-    best = q.limit(1).one()
-    worst = q.offset(acceptable - 1).limit(1).one()
-
-    for label, runs in sorted(label_runs.items()):
-      (mean_values, percentile_values) = combined_stats_over_time(label, runs, objective, worst, best)
-      returned_values[label] = (mean_values, percentile_values)
-      final_scores = list()
-      for run, session in runs:
-        try:
-          final = (session.query(resultsdb.models.Result)
-                  .filter_by(tuning_run = run,
-                             configuration = run.final_config)
-                  .limit(1).one())
-        except sqlalchemy.orm.exc.NoResultFound:
-          continue
-        final_scores.append(objective.stats_quality_score(final, worst, best))
-      final_scores.sort()
-  return returned_values
-
-if __name__ == '__main__':
-    labels = [u'timeouts', u'always_reorder', u'add_store_at', u'all_options']
-    get_values(labels)
-    print get_all_labels()
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/optional-requirements.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/optional-requirements.txt
deleted file mode 100644
index 9848f674cb6e5ca1faba757abd98eb5066e4688d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/optional-requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-django==1.6.1
-matplotlib==1.1.1
-virtualenv==1.9.1
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/requirements.txt b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/requirements.txt
deleted file mode 100644
index fa9cfeca2ede04002798fea0db669de3c87879d4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-argparse>=1.2.1
-fn>=0.2.12
-numpy>=1.8.0
-pysqlite>=2.6.3
-SQLAlchemy>=0.8.2
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/setup.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/setup.py
deleted file mode 100755
index 633d4359d9e9655b5241521208fecc37bc4ab65f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/setup.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/python
-try:
-    from setuptools import setup
-except ImportError:
-    try:
-        from setuptools.core import setup
-    except ImportError:
-        from distutils.core import setup
-
-try:
-    from pypandoc import convert
-    read_md = lambda f: convert(f, 'rest')
-except ImportError:
-    print("warning: pypandoc module not found, could not convert Markdown to RST")
-    read_md = lambda f: open(f, 'r').read()
-
-required = open('requirements.txt').read().splitlines()
-required = [l.strip() for l in required
-            if l.strip() and not l.strip().startswith('#')]
-
-setup(
-    name='opentuner',
-    version='0.8.0',
-    url='http://opentuner.org/',
-    license='MIT',
-    author='Jason Ansel',
-    author_email='jansel@jansel.net',
-    description='An extensible framework for program autotuning',
-    long_description=read_md('README.md'),
-    packages=['opentuner', 'opentuner.resultsdb', 'opentuner.utils',
-              'opentuner.measurement', 'opentuner.search'],
-    install_requires=required,
-)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/manage.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/manage.py
deleted file mode 100644
index f27b5b8db13b490f7599856364f59c6fedcbfe6e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/manage.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env python
-import os
-import sys
-
-if __name__ == "__main__":
-    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "stats_app.settings")
-
-    from django.core.management import execute_from_command_line
-
-    execute_from_command_line(sys.argv)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/settings.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/settings.py
deleted file mode 100644
index 09505be03e5621e4df952e878b52973da9588ffc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/settings.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# Django settings for stats_app project.
-import os
-
-DEBUG = True
-TEMPLATE_DEBUG = DEBUG
-
-ADMINS = (
-    # ('Your Name', 'your_email@example.com'),
-)
-
-MANAGERS = ADMINS
-DIRECTORY_NAME = os.path.dirname(os.path.realpath(__file__))
-
-DATABASES = {
-    'default': {
-        'ENGINE': 'django.db.backends.sqlite3',  # Add 'postgresql_psycopg2', 'mysql', 'sqlite3' or 'oracle'.
-        'NAME': DIRECTORY_NAME + '/db',      # Or path to database file if using sqlite3.
-        # The following settings are not used with sqlite3:
-        'USER': '',
-        'PASSWORD': '',
-        'HOST': '',                      # Empty for localhost through domain sockets or '127.0.0.1' for localhost through TCP.
-        'PORT': '',                      # Set to empty string for default.
-    }
-}
-
-# Hosts/domain names that are valid for this site; required if DEBUG is False
-# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts
-ALLOWED_HOSTS = []
-
-# Local time zone for this installation. Choices can be found here:
-# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
-# although not all choices may be available on all operating systems.
-# In a Windows environment this must be set to your system time zone.
-TIME_ZONE = 'America/Chicago'
-
-# Language code for this installation. All choices can be found here:
-# http://www.i18nguy.com/unicode/language-identifiers.html
-LANGUAGE_CODE = 'en-us'
-
-SITE_ID = 1
-
-# If you set this to False, Django will make some optimizations so as not
-# to load the internationalization machinery.
-USE_I18N = True
-
-# If you set this to False, Django will not format dates, numbers and
-# calendars according to the current locale.
-USE_L10N = True
-
-# If you set this to False, Django will not use timezone-aware datetimes.
-USE_TZ = True
-
-# Absolute filesystem path to the directory that will hold user-uploaded files.
-# Example: "/var/www/example.com/media/"
-MEDIA_ROOT = ''
-
-# URL that handles the media served from MEDIA_ROOT. Make sure to use a
-# trailing slash.
-# Examples: "http://example.com/media/", "http://media.example.com/"
-MEDIA_URL = ''
-
-# Absolute path to the directory static files should be collected to.
-# Don't put anything in this directory yourself; store your static files
-# in apps' "static/" subdirectories and in STATICFILES_DIRS.
-# Example: "/var/www/example.com/static/"
-STATIC_ROOT = ''
-
-# URL prefix for static files.
-# Example: "http://example.com/static/", "http://static.example.com/"
-STATIC_URL = '/static/'
-
-# Additional locations of static files
-STATICFILES_DIRS = (
-    # Put strings here, like "/home/html/static" or "C:/www/django/static".
-    # Always use forward slashes, even on Windows.
-    # Don't forget to use absolute paths, not relative paths.
-    DIRECTORY_NAME + '/static',
-)
-
-# List of finder classes that know how to find static files in
-# various locations.
-STATICFILES_FINDERS = (
-    'django.contrib.staticfiles.finders.FileSystemFinder',
-    'django.contrib.staticfiles.finders.AppDirectoriesFinder',
-#    'django.contrib.staticfiles.finders.DefaultStorageFinder',
-)
-
-# Make this unique, and don't share it with anybody.
-SECRET_KEY = 't!!j*1gt0(5n%6nj-lirzja-9uj6s86s#0@kp2@8v&x#+c2+c-'
-
-# List of callables that know how to import templates from various sources.
-TEMPLATE_LOADERS = (
-    'django.template.loaders.filesystem.Loader',
-    'django.template.loaders.app_directories.Loader',
-#     'django.template.loaders.eggs.Loader',
-)
-
-MIDDLEWARE_CLASSES = (
-    'django.middleware.common.CommonMiddleware',
-    'django.contrib.sessions.middleware.SessionMiddleware',
-    'django.middleware.csrf.CsrfViewMiddleware',
-    'django.contrib.auth.middleware.AuthenticationMiddleware',
-    'django.contrib.messages.middleware.MessageMiddleware',
-    # Uncomment the next line for simple clickjacking protection:
-    # 'django.middleware.clickjacking.XFrameOptionsMiddleware',
-)
-
-ROOT_URLCONF = 'stats_app.urls'
-
-# Python dotted path to the WSGI application used by Django's runserver.
-WSGI_APPLICATION = 'stats_app.wsgi.application'
-
-TEMPLATE_DIRS = (
-    # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates".
-    # Always use forward slashes, even on Windows.
-    # Don't forget to use absolute paths, not relative paths.
-    DIRECTORY_NAME + '/templates',
-)
-
-INSTALLED_APPS = (
-    'django.contrib.auth',
-    'django.contrib.contenttypes',
-    'django.contrib.sessions',
-    'django.contrib.sites',
-    'django.contrib.messages',
-    'django.contrib.staticfiles',
-    # Uncomment the next line to enable the admin:
-    'django.contrib.admin',
-    # Uncomment the next line to enable admin documentation:
-    'django.contrib.admindocs',
-)
-
-SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer'
-
-# A sample logging configuration. The only tangible logging
-# performed by this configuration is to send an email to
-# the site admins on every HTTP 500 error when DEBUG=False.
-# See http://docs.djangoproject.com/en/dev/topics/logging for
-# more details on how to customize your logging configuration.
-LOGGING = {
-    'version': 1,
-    'disable_existing_loggers': False,
-    'filters': {
-        'require_debug_false': {
-            '()': 'django.utils.log.RequireDebugFalse'
-        }
-    },
-    'handlers': {
-        'mail_admins': {
-            'level': 'ERROR',
-            'filters': ['require_debug_false'],
-            'class': 'django.utils.log.AdminEmailHandler'
-        }
-    },
-    'loggers': {
-        'django.request': {
-            'handlers': ['mail_admins'],
-            'level': 'ERROR',
-            'propagate': True,
-        },
-    }
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/static/charts.css b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/static/charts.css
deleted file mode 100644
index e32e2832aeac39540f2fb2c39e3817b6ab85cf3b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/static/charts.css
+++ /dev/null
@@ -1,11 +0,0 @@
-img.center {
-display: block;
-margin-left: auto;
-margin-right: auto;
-
-padding: 8px;
-border: solid;
-border-color: #dddddd #aaaaaa #aaaaaa #dddddd;
-border-width: 1px 2px 2px 1px;
-background-color: white;
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/templates/charts.html b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/templates/charts.html
deleted file mode 100644
index d38bb4c0c6c7c31d9cfce0bbc74d57f7601b3c83..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/templates/charts.html
+++ /dev/null
@@ -1,41 +0,0 @@
-<!DOCTYPE html>
-<head>
-<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}charts.css" media="screen" />
-<html lang="en">
-  <title>{% block title %}Graph{% endblock %}</title>
-</head>
-
-<body>
-  <div id="graphForm">
-  <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"> </script>
-  <script>
-    function callback() {{
-      var values = $('#graphForm form').serialize();
-      $('#graphForm img').attr("src", "graph.png?" + values);
-    }}
-  </script>
-  <p style="text-align:center">
-  <img src="graph.png" id="graph" />
-  </p>
-  <form method = "GET" action="" style="text-align:center">
-    <h3>X Limits:</h3>
-    <input type="range" name="xlim" min="0" max="10000">
-    <br><h3>Y Limits:</h3>
-    <input type="range" name="ylim" min="0" max="20">
-    <br>
-    <h3>Labels:</h3>
-    {0}
-    <br>
-    <h3>Measure:</h3>
-    <b>Mean:</b>
-    <input type="checkbox" name="disp_type" value="mean">
-    <b>Median:</b>
-    <input type="checkbox" name="disp_type" value="median">
-    <b>All percentiles:</b>
-    <input type="checkbox" name="disp_type" value="all_percentiles">
-    <br>
-    <input type="button" value="Graph!" onclick="callback()">
-  </form>
-  </div>
-</body>
-</html>
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/urls.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/urls.py
deleted file mode 100644
index 15743290ec27ae4f2a4e633e80483d972190c870..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/urls.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from django.conf.urls import patterns, include, url
-
-# Uncomment the next two lines to enable the admin:
-from django.contrib import admin
-import views.charts
-admin.autodiscover()
-
-urlpatterns = patterns('',
-    # Examples:
-    # url(r'^$', 'stats_app.views.home', name='home'),
-    # url(r'^stats_app/', include('stats_app.foo.urls')),
-
-    # Uncomment the admin/doc line below to enable admin documentation:
-    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),
-
-    # Uncomment the next line to enable the admin:
-    url(r'^admin/', include(admin.site.urls)),
-    url(r'^graph.png$', views.charts.display_graph, name='graph'),
-    url(r'^$', views.charts.display_full_page, name='graph_page'),
-)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views.py
deleted file mode 100644
index 7cb32b3655aa032c745e97a088ad80365dd9c551..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from django.http import HttpResponse
-
-
-def index(request):
-    return HttpResponse("Hello, world. You're at the stats application index.")
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views/charts.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views/charts.py
deleted file mode 100644
index c3a2ebff32281967f11640ef16a39353ca501d1a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/views/charts.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import datetime
-import django
-from django.shortcuts import render
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from matplotlib.dates import DateFormatter
-from matplotlib.figure import Figure
-import random
-
-from opentuner.utils import stats_matplotlib as stats
-
-
-def display_graph(request):
-  """
-  Handles request to display graph with provided parameters
-  """
-  request_dict = dict(request.GET.iterlists())
-
-  xlim = request_dict.get('xlim', None)
-  if xlim:
-    xlim = int(xlim[0])
-  else:
-    xlim = 5000
-  xlim = [0, xlim]
-
-  ylim = request_dict.get('ylim', None)
-  if ylim:
-    ylim = int(ylim[0])
-  else:
-    ylim = 10
-  ylim = [0, ylim]
-
-  labels = request_dict.get('labels', None)
-
-  disp_types = request_dict.get('disp_type', None)
-  if not disp_types:
-    disp_types = ['median']
-
-  fig = stats.matplotlibplot_file(labels, xlim=xlim, ylim=ylim, disp_types=disp_types)
-  canvas = FigureCanvas(fig)
-  response = django.http.HttpResponse(content_type='image/png')
-  canvas.print_png(response)
-  return response
-
-
-def display_full_page(request):
-  """
-  Handles request to display the full page
-  """
-  all_labels = stats.get_all_labels()
-  label_list = get_label_list(all_labels)
-  html = render(request, 'charts.html')
-  content = html.content
-  content = content.format(label_list)
-  html.content = content
-  return html
-
-
-def get_label_list(all_labels):
-  """
-  Returns list of html form inputs corresponding to the different
-  labels in the provided db file
-  """
-  label_list = ''
-  for label in all_labels:
-    label_list += '<b>%s</b>:<input type="checkbox" name="labels" value="%s">' % (label, label)
-  return label_list
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/wsgi.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/wsgi.py
deleted file mode 100644
index 90f54d8e3dd53cadeeb3eafa33e1abd734485cd0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/stats_app/stats_app/wsgi.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""
-WSGI config for stats_app project.
-
-This module contains the WSGI application used by Django's development server
-and any production WSGI deployments. It should expose a module-level variable
-named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover
-this application via the ``WSGI_APPLICATION`` setting.
-
-Usually you will have the standard Django WSGI application here, but it also
-might make sense to replace the whole Django WSGI application with a custom one
-that later delegates to the Django one. For example, you could introduce WSGI
-middleware here, or combine a Django application with an application of another
-framework.
-
-"""
-import os
-
-# We defer to a DJANGO_SETTINGS_MODULE already in the environment. This breaks
-# if running multiple sites in the same mod_wsgi process. To fix this, use
-# mod_wsgi daemon mode with each site in its own daemon process, or use
-# os.environ["DJANGO_SETTINGS_MODULE"] = "stats_app.settings"
-os.environ.setdefault("DJANGO_SETTINGS_MODULE", "stats_app.settings")
-
-# This application object is used by any WSGI server configured to use this
-# file. This includes Django's development server, if the WSGI_APPLICATION
-# setting points here.
-from django.core.wsgi import get_wsgi_application
-application = get_wsgi_application()
-
-# Apply WSGI middleware here.
-# from helloworld.wsgi import HelloWorldApplication
-# application = HelloWorldApplication(application)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_manipulator.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_manipulator.py
deleted file mode 100644
index c6548510fdc40036978728a1c2ad06ff05a6b9e9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_manipulator.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import unittest
-import opentuner
-import mock
-import random
-import numpy
-from opentuner.search import manipulator
-
-def faked_random(nums):
-    f = fake_random(nums)
-    def inner(*args, **kwargs):
-        return f.next()
-    return inner
-
-def fake_random(nums):
-    i = 0
-    while True:
-        yield nums[i]
-        i = (i+1) % len(nums)
-
-
-class PermutationOperatorTests(unittest.TestCase):
-
-    def setUp(self):
-        """
-        Set up a few configurations. The values of the PermutationParameter are:
-        config1 - 0 1 2 3 4 5 6 7 8 9
-        config2 - 4 3 2 1 0 9 8 7 6 5
-        config3 - 1 0 4 2 7 9 5 3 6 8
-
-        """
-        self.manipulator = manipulator.ConfigurationManipulator()
-        self.param1 = manipulator.PermutationParameter("param1", [0,1,2,3,4,5,6,7,8,9])
-        self.manipulator.add_parameter(self.param1)
-
-        self.cfg = self.manipulator.seed_config()
-        self.config1 = self.manipulator.seed_config()
-        self.config2 = self.manipulator.seed_config()
-        self.config3 = self.manipulator.seed_config()
-
-        # repeating values
-        self.config4 = self.manipulator.seed_config()
-        self.config5 = self.manipulator.seed_config()
-
-
-        self.param1.set_value(self.config1, [0,1,2,3,4,5,6,7,8,9])
-        self.param1.set_value(self.config2, [4,3,2,1,0,9,8,7,6,5])
-        self.param1.set_value(self.config3, [1,0,4,2,7,9,5,3,6,8])
-
-        # repeating values
-        self.param1.set_value(self.config4, [1,2,3,4,2,3,4,3,4,4])
-        self.param1.set_value(self.config5, [4,2,4,3,3,1,3,4,2,4])
-
-    @mock.patch('random.randint', side_effect=faked_random([1,6]))
-    def test_op2_random_swap_1_6(self, randint_func):
-        # operator shouuld swap the indices at 1 and 6
-        self.param1.op2_random_swap(self.cfg, self.config1)
-
-        self.assertEqual(self.param1.get_value(self.cfg),[0,6,2,3,4,5,1,7,8,9])
-        self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,5,6,7,8,9])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([7,2]))
-    def test_op2_random_invert(self, randint_func):
-        #should reverse a section of length 3 starting at index given by randint
-        self.param1.op2_random_invert(self.cfg, self.config1)
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,5,6,9,8,7])
-
-        self.param1.op2_random_invert(self.cfg, self.config1)
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,4,3,2,5,6,7,8,9])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([0]))
-    def test_op3_cross_PMX_str5(self, randint_func):
-        # should perform PMX with a cut at 0 and crossover size 5
-        self.param1.op3_cross(self.cfg, self.config1, self.config3,
-                                xchoice='op3_cross_PMX', strength=0.5)
-        self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,7,5,6,3,8,9])
-
-    @mock.patch('random.randint', side_effect=faked_random([5]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_CX_no_cross(self, uniform_func, randint_func):
-        # should perform no cross
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.8)
-        self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,5,6,7,8,9])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([5]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_CX_cross_p1(self, uniform_func, randint_func):
-        # should cross the first parent
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.3, c1=0.5, c2="unused")
-        self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,9,6,7,8,5])
-
-    @mock.patch('random.randint', side_effect=faked_random([5]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_CX_cross_p2(self, uniform_func, randint_func):
-        # should cross the second parent
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.3, c1=0.3, c2="unused")
-        self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,9,5,7,6,8])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([5]))
-    def test_op3_cross_PX_5(self, randint_func):
-        # Random cut point = 5 (index = 4)
-        self.param1.op3_cross_PX(self.cfg, self.config1, self.config3, 2)
-        self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,3,5,6,7,8,9])
-
-    @mock.patch('random.randint', side_effect=faked_random([2]))
-    def test_op3_cross_PMX_0_d4(self, randint_func):
-        # cut = 2, d = 4
-        self.param1.op3_cross_PMX(self.cfg, self.config2, self.config3, 4)
-        self.assertEqual(self.param1.get_value(self.cfg),[1,3,4,2,7,9,8,0,6,5])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([0]))
-    def test_op3_cross_PMX_0_d5(self, randint_func):
-        # cut = 0, d = 5
-        self.param1.op3_cross_PMX(self.cfg, self.config1, self.config3, 5)
-        self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,7,5,6,3,8,9])
-
-    @mock.patch('random.randint', side_effect=faked_random([4]))
-    def test_op3_cross_PMX_dups(self, randint_func):
-        # cut = 4, d = 5
-        self.param1.op3_cross_PMX(self.cfg, self.config5, self.config4, 5)
-
-        # [4,2,4,3,3,1,3,4,2,4]
-        # [1,2,3,4,2,3,4,3,4,4]
-        # expected:
-        # [1,2,4,3,2,3,4,3,4,4]
-
-        self.assertEqual(self.param1.get_value(self.cfg), [1,2,4,3,2,3,4,3,4,4])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([5]))
-    def test_op3_cross_CX_5(self, randint_func):
-        # initial replacement at index 5
-        self.param1.op3_cross_CX(self.cfg, self.config1, self.config2, "unused")
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,9,6,7,8,5])
-        self.param1.op3_cross_CX(self.cfg, self.config1, self.config3, "unused")
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,9,5,7,6,8])
-
-    @mock.patch('random.randint', side_effect=faked_random([0]))
-    def test_op3_cross_CX_dups(self, randint_func):
-        # initial replacement at index 4
-        self.param1.op3_cross_CX(self.cfg, self.config5, self.config4, "unused")
-
-        # [4,2,4,3,3,1,3,4,2,4]
-        # [1,2,3,4,2,3,4,3,4,4]
-        # expected:
-        # [1,2,3,4,3,3,4,4,2,4]
-
-        self.assertEqual(self.param1.get_value(self.cfg), [1,2,3,4,3,3,4,4,2,4])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([3]))
-    def test_op3_cross_OX1_3_d4(self, randint_func):
-        # cut at 3
-        # d = 4
-        self.param1.op3_cross_OX1(self.cfg, self.config1, self.config2, 4)
-        self.assertEqual(self.param1.get_value(self.cfg),[2,3,4,1,0,9,8,5,6,7])
-        self.param1.op3_cross_OX1(self.cfg, self.config1, self.config3, 4)
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,3,2,7,9,5,4,6,8])
-
-    @mock.patch('random.randint', side_effect=faked_random([4,2]))
-    def test_op3_cross_OX3_2_5_d4(self, randint_func):
-        # cuts at 4,2
-        # d = 4
-        self.param1.op3_cross_OX3(self.cfg, self.config1, self.config2, 4)
-        self.assertEqual(self.param1.get_value(self.cfg),[3,4,5,6,2,1,0,9,7,8])
-        self.param1.op3_cross_OX3(self.cfg, self.config1, self.config3, 4)
-        self.assertEqual(self.param1.get_value(self.cfg),[0,1,3,5,4,2,7,9,6,8])
-
-
-class FloatArrayOperatorTests(unittest.TestCase):
-    """
-    also tests the operators for Array (since Array is abstract)
-    """
-
-    def setUp(self):
-        """
-        Set up a few configurations. The values of the FloatArray are:
-        config1 - 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
-        config2 - 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9
-        config3 - 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
-        """
-        self.manipulator = manipulator.ConfigurationManipulator()
-        self.param1 = manipulator.FloatArray("param1", 10, 4, 0)
-        self.manipulator.add_parameter(self.param1)
-
-        self.cfg = self.manipulator.seed_config()
-        self.config1 = self.manipulator.seed_config()
-        self.config2 = self.manipulator.seed_config()
-        self.config3 = self.manipulator.seed_config()
-
-        self.param1.set_value(self.config1, numpy.array([1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9]))
-        self.param1.set_value(self.config2, numpy.array([2.0,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9]))
-        self.param1.set_value(self.config3, numpy.array([3.0,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.8]))
-
-
-    @mock.patch('random.randint', side_effect=faked_random([3]))
-    def test_op3_cross_3_str4(self, randint_func):
-        self.param1.op3_cross(self.cfg, self.config1, self.config2, strength=0.4)
-
-        val = self.param1.get_value(self.cfg)
-        expected = [1.0,1.1,1.2,2.3,2.4,2.5,2.6,1.7,1.8,1.9]
-        for i in range(len(val)):
-            self.assertAlmostEqual(val[i], expected[i])
-
-    @mock.patch('random.randint', side_effect=faked_random([3]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_no_cross(self, uniform_func, randint_func):
-        #should perform no cross
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.8)
-        val = self.param1.get_value(self.config1)
-        expected = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9]
-        for i in range(len(val)):
-            self.assertAlmostEqual(val[i], expected[i])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([3]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_cross_p1(self, uniform_func, randint_func):
-        #should cross the first parent
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.3, c1=0.5, c2="unused")
-        val = self.param1.get_value(self.config1)
-        expected = [1.0,1.1,1.2,2.3,2.4,2.5,1.6,1.7,1.8,1.9]
-        for i in range(len(val)):
-            self.assertAlmostEqual(val[i], expected[i])
-
-
-    @mock.patch('random.randint', side_effect=faked_random([3]))
-    @mock.patch('random.uniform', side_effect=faked_random([0.4]))
-    def test_op3_swarm_cross_p2(self, uniform_func, randint_func):
-        #should cross the second parent
-        self.param1.op3_swarm(self.config1, self.config2, self.config3,
-                                xchoice='op3_cross_CX', c=0.3, c1=0.3, c2="unused")
-        val = self.param1.get_value(self.config1)
-        expected = [1.0,1.1,1.2,3.3,3.4,3.5,1.6,1.7,1.8,1.9]
-        self.assertEqual(len(val),len(expected))
-        for i in range(len(val)):
-            self.assertAlmostEqual(val[i], expected[i])
-
-    @mock.patch('random.random', side_effect=faked_random([0.2, 0.4]))
-    def test_op3_swarm_parallel(self, random_func):
-        # r1 = 0.2, r2 = 0.4, velocities = [-2,0,0,0,0,0,1,1.5,2,3]
-        # max and min are 4, 0
-        velocities = numpy.array([-2.0,0.0,0,0,0,0,1.0,1.5,2,3.0])
-
-        vs = self.param1.op3_swarm_parallel(self.config1, self.config2, self.config3, velocities=velocities)
-        vs_expected = [-1.5,.5,.5,.5,.5,.5,1.5,2.0,2.5,3.48]
-
-        self.assertEqual(len(vs),len(vs_expected))
-
-        for i in range(len(vs)):
-            self.assertAlmostEqual(vs[i], vs_expected[i])
-
-
-        val = self.param1.get_value(self.config1)
-        expected = [0,1.6,1.7,1.8,1.9,2.0,3.1,3.7,4,4]
-        self.assertEqual(len(val),len(expected))
-        for i in range(len(val)):
-            self.assertAlmostEqual(val[i], expected[i])
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_technique.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_technique.py
deleted file mode 100644
index c6107bace942a5ac85533878131fb953439ea3f7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/tests/test_technique.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import unittest
-import opentuner
-import mock
-from opentuner.search.composableevolutionarytechniques import ComposableEvolutionaryTechnique
-from opentuner.search import manipulator
-
-def faked_random(nums):
-  f = fake_random(nums)
-  def inner(*args, **kwargs):
-    return f.next()
-  return inner
-
-def fake_random(nums):
-  i = 0
-  while True:
-    yield nums[i]
-    i = (i+1) % len(nums)
-
-class EmptyComposableEvolutionaryTechnique(ComposableEvolutionaryTechnique):
-  def __init__(self, *pargs, **kwargs):
-    super(EmptyComposableEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
-
-  def minimum_number_of_parents(self):
-    return 4
-
-  def get_parents(self, population):
-    cfg = self.manipulator.copy(population[0].config)
-
-    return [cfg]
-
-  def update_population(self, config, population):
-    # replace the oldest configuration if the new one is better.
-    population[0].config = config
-
-    return population
-
-class ComposableSearchTechniqueTests(unittest.TestCase):
-
-  def setUp(self):
-    self.operator_map = {}
-    ComposableEvolutionaryTechnique.add_to_map(self.operator_map,
-                                  manipulator.PermutationParameter,
-                                  "op3_cross", xchoice='op3_cross_CX')
-    ComposableEvolutionaryTechnique.add_to_map(self.operator_map,
-                                  "FloatArray",
-                                  "op3_cross", strength=0.4)
-    self.technique = EmptyComposableEvolutionaryTechnique(operator_map = self.operator_map)
-
-  def test_add_to_map(self):
-    op_map = {}
-    op_map[manipulator.PermutationParameter] = {'op_name': 'op3_cross',
-                                                'args': (),
-                                                'kwargs': {'xchoice': 'op3_cross_CX'}}
-    op_map[manipulator.FloatArray] = {'op_name': 'op3_cross',
-                                        'args': (),
-                                        'kwargs': {'strength': 0.4}}
-    self.assertDictEqual(self.operator_map, op_map)
-
-  def test_get_default_oeprator(self):
-    default = self.technique.get_default_operator(manipulator.PermutationParameter)
-    self.assertDictEqual(default, {'op_name': 'op1_nop', 'args': [], 'kwargs': {}})
-
-
-  def test_get_operator(self):
-    default = self.technique.get_operator(manipulator.IntegerParameter)
-    self.assertDictEqual(default, {'op_name': 'op1_nop', 'args': [], 'kwargs': {}})
-
-    default = self.technique.get_operator(manipulator.PermutationParameter)
-    self.assertDictEqual(default, {'op_name': 'op3_cross','args': (),'kwargs': {'xchoice': 'op3_cross_CX'}})
-
-  @mock.patch('opentuner.search.manipulator.PermutationParameter.op3_cross')
-  def test_apply_operator(self, op3_cross_func):
-    param_instance = manipulator.PermutationParameter('temp', [1,2,3,4,5])
-    self.technique.apply_operator(param_instance, ['p1', 'p2', 'p3', 'p4'])
-    op3_cross_func.assert_called_once_with('p1', 'p2', 'p3', xchoice='op3_cross_CX')
-
-#TODO tests for RandomThreeParentsComposableTechnique
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/venv-bootstrap.py b/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/venv-bootstrap.py
deleted file mode 100755
index 6d6ad0113b72ffe5610b28ac2717442cba6eff8c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/opentuner/venv-bootstrap.py
+++ /dev/null
@@ -1,2611 +0,0 @@
-#!/usr/bin/env python
-## WARNING: This file is generated
-#!/usr/bin/env python
-"""Create a "virtual" Python installation
-"""
-
-# If you change the version here, change it in setup.py
-# and docs/conf.py as well.
-__version__ = "1.9.1"  # following best practices
-virtualenv_version = __version__  # legacy, again
-
-import base64
-import sys
-import os
-import codecs
-import optparse
-import re
-import shutil
-import logging
-import tempfile
-import zlib
-import errno
-import glob
-import distutils.sysconfig
-from distutils.util import strtobool
-import struct
-import subprocess
-
-if sys.version_info < (2, 6):
-    print('ERROR: %s' % sys.exc_info()[1])
-    print('ERROR: this script requires Python 2.6 or greater.')
-    sys.exit(101)
-
-try:
-    set
-except NameError:
-    from sets import Set as set
-try:
-    basestring
-except NameError:
-    basestring = str
-
-try:
-    import ConfigParser
-except ImportError:
-    import configparser as ConfigParser
-
-join = os.path.join
-py_version = 'python%s.%s' % (sys.version_info[0], sys.version_info[1])
-
-is_jython = sys.platform.startswith('java')
-is_pypy = hasattr(sys, 'pypy_version_info')
-is_win = (sys.platform == 'win32')
-is_cygwin = (sys.platform == 'cygwin')
-is_darwin = (sys.platform == 'darwin')
-abiflags = getattr(sys, 'abiflags', '')
-
-user_dir = os.path.expanduser('~')
-if is_win:
-    default_storage_dir = os.path.join(user_dir, 'virtualenv')
-else:
-    default_storage_dir = os.path.join(user_dir, '.virtualenv')
-default_config_file = os.path.join(default_storage_dir, 'virtualenv.ini')
-
-if is_pypy:
-    expected_exe = 'pypy'
-elif is_jython:
-    expected_exe = 'jython'
-else:
-    expected_exe = 'python'
-
-
-REQUIRED_MODULES = ['os', 'posix', 'posixpath', 'nt', 'ntpath', 'genericpath',
-                    'fnmatch', 'locale', 'encodings', 'codecs',
-                    'stat', 'UserDict', 'readline', 'copy_reg', 'types',
-                    're', 'sre', 'sre_parse', 'sre_constants', 'sre_compile',
-                    'zlib']
-
-REQUIRED_FILES = ['lib-dynload', 'config']
-
-majver, minver = sys.version_info[:2]
-if majver == 2:
-    if minver >= 6:
-        REQUIRED_MODULES.extend(['warnings', 'linecache', '_abcoll', 'abc'])
-    if minver >= 7:
-        REQUIRED_MODULES.extend(['_weakrefset'])
-    if minver <= 3:
-        REQUIRED_MODULES.extend(['sets', '__future__'])
-elif majver == 3:
-    # Some extra modules are needed for Python 3, but different ones
-    # for different versions.
-    REQUIRED_MODULES.extend(['_abcoll', 'warnings', 'linecache', 'abc', 'io',
-                             '_weakrefset', 'copyreg', 'tempfile', 'random',
-                             '__future__', 'collections', 'keyword', 'tarfile',
-                             'shutil', 'struct', 'copy', 'tokenize', 'token',
-                             'functools', 'heapq', 'bisect', 'weakref',
-                             'reprlib'])
-    if minver >= 2:
-        REQUIRED_FILES[-1] = 'config-%s' % majver
-    if minver == 3:
-        import sysconfig
-        platdir = sysconfig.get_config_var('PLATDIR')
-        REQUIRED_FILES.append(platdir)
-        # The whole list of 3.3 modules is reproduced below - the current
-        # uncommented ones are required for 3.3 as of now, but more may be
-        # added as 3.3 development continues.
-        REQUIRED_MODULES.extend([
-            #"aifc",
-            #"antigravity",
-            #"argparse",
-            #"ast",
-            #"asynchat",
-            #"asyncore",
-            "base64",
-            #"bdb",
-            #"binhex",
-            #"bisect",
-            #"calendar",
-            #"cgi",
-            #"cgitb",
-            #"chunk",
-            #"cmd",
-            #"codeop",
-            #"code",
-            #"colorsys",
-            #"_compat_pickle",
-            #"compileall",
-            #"concurrent",
-            #"configparser",
-            #"contextlib",
-            #"cProfile",
-            #"crypt",
-            #"csv",
-            #"ctypes",
-            #"curses",
-            #"datetime",
-            #"dbm",
-            #"decimal",
-            #"difflib",
-            #"dis",
-            #"doctest",
-            #"dummy_threading",
-            "_dummy_thread",
-            #"email",
-            #"filecmp",
-            #"fileinput",
-            #"formatter",
-            #"fractions",
-            #"ftplib",
-            #"functools",
-            #"getopt",
-            #"getpass",
-            #"gettext",
-            #"glob",
-            #"gzip",
-            "hashlib",
-            #"heapq",
-            "hmac",
-            #"html",
-            #"http",
-            #"idlelib",
-            #"imaplib",
-            #"imghdr",
-            "imp",
-            "importlib",
-            #"inspect",
-            #"json",
-            #"lib2to3",
-            #"logging",
-            #"macpath",
-            #"macurl2path",
-            #"mailbox",
-            #"mailcap",
-            #"_markupbase",
-            #"mimetypes",
-            #"modulefinder",
-            #"multiprocessing",
-            #"netrc",
-            #"nntplib",
-            #"nturl2path",
-            #"numbers",
-            #"opcode",
-            #"optparse",
-            #"os2emxpath",
-            #"pdb",
-            #"pickle",
-            #"pickletools",
-            #"pipes",
-            #"pkgutil",
-            #"platform",
-            #"plat-linux2",
-            #"plistlib",
-            #"poplib",
-            #"pprint",
-            #"profile",
-            #"pstats",
-            #"pty",
-            #"pyclbr",
-            #"py_compile",
-            #"pydoc_data",
-            #"pydoc",
-            #"_pyio",
-            #"queue",
-            #"quopri",
-            #"reprlib",
-            "rlcompleter",
-            #"runpy",
-            #"sched",
-            #"shelve",
-            #"shlex",
-            #"smtpd",
-            #"smtplib",
-            #"sndhdr",
-            #"socket",
-            #"socketserver",
-            #"sqlite3",
-            #"ssl",
-            #"stringprep",
-            #"string",
-            #"_strptime",
-            #"subprocess",
-            #"sunau",
-            #"symbol",
-            #"symtable",
-            #"sysconfig",
-            #"tabnanny",
-            #"telnetlib",
-            #"test",
-            #"textwrap",
-            #"this",
-            #"_threading_local",
-            #"threading",
-            #"timeit",
-            #"tkinter",
-            #"tokenize",
-            #"token",
-            #"traceback",
-            #"trace",
-            #"tty",
-            #"turtledemo",
-            #"turtle",
-            #"unittest",
-            #"urllib",
-            #"uuid",
-            #"uu",
-            #"wave",
-            #"weakref",
-            #"webbrowser",
-            #"wsgiref",
-            #"xdrlib",
-            #"xml",
-            #"xmlrpc",
-            #"zipfile",
-        ])
-
-if is_pypy:
-    # these are needed to correctly display the exceptions that may happen
-    # during the bootstrap
-    REQUIRED_MODULES.extend(['traceback', 'linecache'])
-
-class Logger(object):
-
-    """
-    Logging object for use in command-line script.  Allows ranges of
-    levels, to avoid some redundancy of displayed information.
-    """
-
-    DEBUG = logging.DEBUG
-    INFO = logging.INFO
-    NOTIFY = (logging.INFO+logging.WARN)/2
-    WARN = WARNING = logging.WARN
-    ERROR = logging.ERROR
-    FATAL = logging.FATAL
-
-    LEVELS = [DEBUG, INFO, NOTIFY, WARN, ERROR, FATAL]
-
-    def __init__(self, consumers):
-        self.consumers = consumers
-        self.indent = 0
-        self.in_progress = None
-        self.in_progress_hanging = False
-
-    def debug(self, msg, *args, **kw):
-        self.log(self.DEBUG, msg, *args, **kw)
-    def info(self, msg, *args, **kw):
-        self.log(self.INFO, msg, *args, **kw)
-    def notify(self, msg, *args, **kw):
-        self.log(self.NOTIFY, msg, *args, **kw)
-    def warn(self, msg, *args, **kw):
-        self.log(self.WARN, msg, *args, **kw)
-    def error(self, msg, *args, **kw):
-        self.log(self.ERROR, msg, *args, **kw)
-    def fatal(self, msg, *args, **kw):
-        self.log(self.FATAL, msg, *args, **kw)
-    def log(self, level, msg, *args, **kw):
-        if args:
-            if kw:
-                raise TypeError(
-                    "You may give positional or keyword arguments, not both")
-        args = args or kw
-        rendered = None
-        for consumer_level, consumer in self.consumers:
-            if self.level_matches(level, consumer_level):
-                if (self.in_progress_hanging
-                    and consumer in (sys.stdout, sys.stderr)):
-                    self.in_progress_hanging = False
-                    sys.stdout.write('\n')
-                    sys.stdout.flush()
-                if rendered is None:
-                    if args:
-                        rendered = msg % args
-                    else:
-                        rendered = msg
-                    rendered = ' '*self.indent + rendered
-                if hasattr(consumer, 'write'):
-                    consumer.write(rendered+'\n')
-                else:
-                    consumer(rendered)
-
-    def start_progress(self, msg):
-        assert not self.in_progress, (
-            "Tried to start_progress(%r) while in_progress %r"
-            % (msg, self.in_progress))
-        if self.level_matches(self.NOTIFY, self._stdout_level()):
-            sys.stdout.write(msg)
-            sys.stdout.flush()
-            self.in_progress_hanging = True
-        else:
-            self.in_progress_hanging = False
-        self.in_progress = msg
-
-    def end_progress(self, msg='done.'):
-        assert self.in_progress, (
-            "Tried to end_progress without start_progress")
-        if self.stdout_level_matches(self.NOTIFY):
-            if not self.in_progress_hanging:
-                # Some message has been printed out since start_progress
-                sys.stdout.write('...' + self.in_progress + msg + '\n')
-                sys.stdout.flush()
-            else:
-                sys.stdout.write(msg + '\n')
-                sys.stdout.flush()
-        self.in_progress = None
-        self.in_progress_hanging = False
-
-    def show_progress(self):
-        """If we are in a progress scope, and no log messages have been
-        shown, write out another '.'"""
-        if self.in_progress_hanging:
-            sys.stdout.write('.')
-            sys.stdout.flush()
-
-    def stdout_level_matches(self, level):
-        """Returns true if a message at this level will go to stdout"""
-        return self.level_matches(level, self._stdout_level())
-
-    def _stdout_level(self):
-        """Returns the level that stdout runs at"""
-        for level, consumer in self.consumers:
-            if consumer is sys.stdout:
-                return level
-        return self.FATAL
-
-    def level_matches(self, level, consumer_level):
-        """
-        >>> l = Logger([])
-        >>> l.level_matches(3, 4)
-        False
-        >>> l.level_matches(3, 2)
-        True
-        >>> l.level_matches(slice(None, 3), 3)
-        False
-        >>> l.level_matches(slice(None, 3), 2)
-        True
-        >>> l.level_matches(slice(1, 3), 1)
-        True
-        >>> l.level_matches(slice(2, 3), 1)
-        False
-        """
-        if isinstance(level, slice):
-            start, stop = level.start, level.stop
-            if start is not None and start > consumer_level:
-                return False
-            if stop is not None and stop <= consumer_level:
-                return False
-            return True
-        else:
-            return level >= consumer_level
-
-    #@classmethod
-    def level_for_integer(cls, level):
-        levels = cls.LEVELS
-        if level < 0:
-            return levels[0]
-        if level >= len(levels):
-            return levels[-1]
-        return levels[level]
-
-    level_for_integer = classmethod(level_for_integer)
-
-# create a silent logger just to prevent this from being undefined
-# will be overridden with requested verbosity main() is called.
-logger = Logger([(Logger.LEVELS[-1], sys.stdout)])
-
-def mkdir(path):
-    if not os.path.exists(path):
-        logger.info('Creating %s', path)
-        os.makedirs(path)
-    else:
-        logger.info('Directory %s already exists', path)
-
-def copyfileordir(src, dest):
-    if os.path.isdir(src):
-        shutil.copytree(src, dest, True)
-    else:
-        shutil.copy2(src, dest)
-
-def copyfile(src, dest, symlink=True):
-    if not os.path.exists(src):
-        # Some bad symlink in the src
-        logger.warn('Cannot find file %s (bad symlink)', src)
-        return
-    if os.path.exists(dest):
-        logger.debug('File %s already exists', dest)
-        return
-    if not os.path.exists(os.path.dirname(dest)):
-        logger.info('Creating parent directories for %s' % os.path.dirname(dest))
-        os.makedirs(os.path.dirname(dest))
-    if not os.path.islink(src):
-        srcpath = os.path.abspath(src)
-    else:
-        srcpath = os.readlink(src)
-    if symlink and hasattr(os, 'symlink') and not is_win:
-        logger.info('Symlinking %s', dest)
-        try:
-            os.symlink(srcpath, dest)
-        except (OSError, NotImplementedError):
-            logger.info('Symlinking failed, copying to %s', dest)
-            copyfileordir(src, dest)
-    else:
-        logger.info('Copying to %s', dest)
-        copyfileordir(src, dest)
-
-def writefile(dest, content, overwrite=True):
-    if not os.path.exists(dest):
-        logger.info('Writing %s', dest)
-        f = open(dest, 'wb')
-        f.write(content.encode('utf-8'))
-        f.close()
-        return
-    else:
-        f = open(dest, 'rb')
-        c = f.read()
-        f.close()
-        if c != content.encode("utf-8"):
-            if not overwrite:
-                logger.notify('File %s exists with different content; not overwriting', dest)
-                return
-            logger.notify('Overwriting %s with new content', dest)
-            f = open(dest, 'wb')
-            f.write(content.encode('utf-8'))
-            f.close()
-        else:
-            logger.info('Content %s already in place', dest)
-
-def rmtree(dir):
-    if os.path.exists(dir):
-        logger.notify('Deleting tree %s', dir)
-        shutil.rmtree(dir)
-    else:
-        logger.info('Do not need to delete %s; already gone', dir)
-
-def make_exe(fn):
-    if hasattr(os, 'chmod'):
-        oldmode = os.stat(fn).st_mode & 0xFFF # 0o7777
-        newmode = (oldmode | 0x16D) & 0xFFF # 0o555, 0o7777
-        os.chmod(fn, newmode)
-        logger.info('Changed mode of %s to %s', fn, oct(newmode))
-
-def _find_file(filename, dirs):
-    for dir in reversed(dirs):
-        files = glob.glob(os.path.join(dir, filename))
-        if files and os.path.isfile(files[0]):
-            return True, files[0]
-    return False, filename
-
-def _install_req(py_executable, unzip=False, distribute=False,
-                 search_dirs=None, never_download=False):
-
-    if search_dirs is None:
-        search_dirs = file_search_dirs()
-
-    if not distribute:
-        egg_path = 'setuptools-*-py%s.egg' % sys.version[:3]
-        found, egg_path = _find_file(egg_path, search_dirs)
-        project_name = 'setuptools'
-        bootstrap_script = EZ_SETUP_PY
-        tgz_path = None
-    else:
-        # Look for a distribute egg (these are not distributed by default,
-        # but can be made available by the user)
-        egg_path = 'distribute-*-py%s.egg' % sys.version[:3]
-        found, egg_path = _find_file(egg_path, search_dirs)
-        project_name = 'distribute'
-        if found:
-            tgz_path = None
-            bootstrap_script = DISTRIBUTE_FROM_EGG_PY
-        else:
-            # Fall back to sdist
-            # NB: egg_path is not None iff tgz_path is None
-            # iff bootstrap_script is a generic setup script accepting
-            # the standard arguments.
-            egg_path = None
-            tgz_path = 'distribute-*.tar.gz'
-            found, tgz_path = _find_file(tgz_path, search_dirs)
-            bootstrap_script = DISTRIBUTE_SETUP_PY
-
-    if is_jython and os._name == 'nt':
-        # Jython's .bat sys.executable can't handle a command line
-        # argument with newlines
-        fd, ez_setup = tempfile.mkstemp('.py')
-        os.write(fd, bootstrap_script)
-        os.close(fd)
-        cmd = [py_executable, ez_setup]
-    else:
-        cmd = [py_executable, '-c', bootstrap_script]
-    if unzip and egg_path:
-        cmd.append('--always-unzip')
-    env = {}
-    remove_from_env = ['__PYVENV_LAUNCHER__']
-    if logger.stdout_level_matches(logger.DEBUG) and egg_path:
-        cmd.append('-v')
-
-    old_chdir = os.getcwd()
-    if egg_path is not None and os.path.exists(egg_path):
-        logger.info('Using existing %s egg: %s' % (project_name, egg_path))
-        cmd.append(egg_path)
-        if os.environ.get('PYTHONPATH'):
-            env['PYTHONPATH'] = egg_path + os.path.pathsep + os.environ['PYTHONPATH']
-        else:
-            env['PYTHONPATH'] = egg_path
-    elif tgz_path is not None and os.path.exists(tgz_path):
-        # Found a tgz source dist, let's chdir
-        logger.info('Using existing %s egg: %s' % (project_name, tgz_path))
-        os.chdir(os.path.dirname(tgz_path))
-        # in this case, we want to be sure that PYTHONPATH is unset (not
-        # just empty, really unset), else CPython tries to import the
-        # site.py that it's in virtualenv_support
-        remove_from_env.append('PYTHONPATH')
-    elif never_download:
-        logger.fatal("Can't find any local distributions of %s to install "
-                     "and --never-download is set.  Either re-run virtualenv "
-                     "without the --never-download option, or place a %s "
-                     "distribution (%s) in one of these "
-                     "locations: %r" % (project_name, project_name,
-                                        egg_path or tgz_path,
-                                        search_dirs))
-        sys.exit(1)
-    elif egg_path:
-        logger.info('No %s egg found; downloading' % project_name)
-        cmd.extend(['--always-copy', '-U', project_name])
-    else:
-        logger.info('No %s tgz found; downloading' % project_name)
-    logger.start_progress('Installing %s...' % project_name)
-    logger.indent += 2
-    cwd = None
-    if project_name == 'distribute':
-        env['DONT_PATCH_SETUPTOOLS'] = 'true'
-
-    def _filter_ez_setup(line):
-        return filter_ez_setup(line, project_name)
-
-    if not os.access(os.getcwd(), os.W_OK):
-        cwd = tempfile.mkdtemp()
-        if tgz_path is not None and os.path.exists(tgz_path):
-            # the current working dir is hostile, let's copy the
-            # tarball to a temp dir
-            target = os.path.join(cwd, os.path.split(tgz_path)[-1])
-            shutil.copy(tgz_path, target)
-    try:
-        call_subprocess(cmd, show_stdout=False,
-                        filter_stdout=_filter_ez_setup,
-                        extra_env=env,
-                        remove_from_env=remove_from_env,
-                        cwd=cwd)
-    finally:
-        logger.indent -= 2
-        logger.end_progress()
-        if cwd is not None:
-            shutil.rmtree(cwd)
-        if os.getcwd() != old_chdir:
-            os.chdir(old_chdir)
-        if is_jython and os._name == 'nt':
-            os.remove(ez_setup)
-
-def file_search_dirs():
-    here = os.path.dirname(os.path.abspath(__file__))
-    dirs = ['.', here,
-            join(here, 'virtualenv_support')]
-    if os.path.splitext(os.path.dirname(__file__))[0] != 'virtualenv':
-        # Probably some boot script; just in case virtualenv is installed...
-        try:
-            import virtualenv
-        except ImportError:
-            pass
-        else:
-            dirs.append(os.path.join(os.path.dirname(virtualenv.__file__), 'virtualenv_support'))
-    return [d for d in dirs if os.path.isdir(d)]
-
-def install_setuptools(py_executable, unzip=False,
-                       search_dirs=None, never_download=False):
-    _install_req(py_executable, unzip,
-                 search_dirs=search_dirs, never_download=never_download)
-
-def install_distribute(py_executable, unzip=False,
-                       search_dirs=None, never_download=False):
-    _install_req(py_executable, unzip, distribute=True,
-                 search_dirs=search_dirs, never_download=never_download)
-
-_pip_re = re.compile(r'^pip-.*(zip|tar.gz|tar.bz2|tgz|tbz)$', re.I)
-def install_pip(py_executable, search_dirs=None, never_download=False):
-    if search_dirs is None:
-        search_dirs = file_search_dirs()
-
-    filenames = []
-    for dir in search_dirs:
-        filenames.extend([join(dir, fn) for fn in os.listdir(dir)
-                          if _pip_re.search(fn)])
-    filenames = [(os.path.basename(filename).lower(), i, filename) for i, filename in enumerate(filenames)]
-    filenames.sort()
-    filenames = [filename for basename, i, filename in filenames]
-    if not filenames:
-        filename = 'pip'
-    else:
-        filename = filenames[-1]
-    easy_install_script = 'easy_install'
-    if is_win:
-        easy_install_script = 'easy_install-script.py'
-    # There's two subtle issues here when invoking easy_install.
-    # 1. On unix-like systems the easy_install script can *only* be executed
-    #    directly if its full filesystem path is no longer than 78 characters.
-    # 2. A work around to [1] is to use the `python path/to/easy_install foo`
-    #    pattern, but that breaks if the path contains non-ASCII characters, as
-    #    you can't put the file encoding declaration before the shebang line.
-    # The solution is to use Python's -x flag to skip the first line of the
-    # script (and any ASCII decoding errors that may have occurred in that line)
-    cmd = [py_executable, '-x', join(os.path.dirname(py_executable), easy_install_script), filename]
-    # jython and pypy don't yet support -x
-    if is_jython or is_pypy:
-        cmd.remove('-x')
-    if filename == 'pip':
-        if never_download:
-            logger.fatal("Can't find any local distributions of pip to install "
-                         "and --never-download is set.  Either re-run virtualenv "
-                         "without the --never-download option, or place a pip "
-                         "source distribution (zip/tar.gz/tar.bz2) in one of these "
-                         "locations: %r" % search_dirs)
-            sys.exit(1)
-        logger.info('Installing pip from network...')
-    else:
-        logger.info('Installing existing %s distribution: %s' % (
-                os.path.basename(filename), filename))
-    logger.start_progress('Installing pip...')
-    logger.indent += 2
-    def _filter_setup(line):
-        return filter_ez_setup(line, 'pip')
-    try:
-        call_subprocess(cmd, show_stdout=False,
-                        filter_stdout=_filter_setup)
-    finally:
-        logger.indent -= 2
-        logger.end_progress()
-
-def filter_ez_setup(line, project_name='setuptools'):
-    if not line.strip():
-        return Logger.DEBUG
-    if project_name == 'distribute':
-        for prefix in ('Extracting', 'Now working', 'Installing', 'Before',
-                       'Scanning', 'Setuptools', 'Egg', 'Already',
-                       'running', 'writing', 'reading', 'installing',
-                       'creating', 'copying', 'byte-compiling', 'removing',
-                       'Processing'):
-            if line.startswith(prefix):
-                return Logger.DEBUG
-        return Logger.DEBUG
-    for prefix in ['Reading ', 'Best match', 'Processing setuptools',
-                   'Copying setuptools', 'Adding setuptools',
-                   'Installing ', 'Installed ']:
-        if line.startswith(prefix):
-            return Logger.DEBUG
-    return Logger.INFO
-
-
-class UpdatingDefaultsHelpFormatter(optparse.IndentedHelpFormatter):
-    """
-    Custom help formatter for use in ConfigOptionParser that updates
-    the defaults before expanding them, allowing them to show up correctly
-    in the help listing
-    """
-    def expand_default(self, option):
-        if self.parser is not None:
-            self.parser.update_defaults(self.parser.defaults)
-        return optparse.IndentedHelpFormatter.expand_default(self, option)
-
-
-class ConfigOptionParser(optparse.OptionParser):
-    """
-    Custom option parser which updates its defaults by by checking the
-    configuration files and environmental variables
-    """
-    def __init__(self, *args, **kwargs):
-        self.config = ConfigParser.RawConfigParser()
-        self.files = self.get_config_files()
-        self.config.read(self.files)
-        optparse.OptionParser.__init__(self, *args, **kwargs)
-
-    def get_config_files(self):
-        config_file = os.environ.get('VIRTUALENV_CONFIG_FILE', False)
-        if config_file and os.path.exists(config_file):
-            return [config_file]
-        return [default_config_file]
-
-    def update_defaults(self, defaults):
-        """
-        Updates the given defaults with values from the config files and
-        the environ. Does a little special handling for certain types of
-        options (lists).
-        """
-        # Then go and look for the other sources of configuration:
-        config = {}
-        # 1. config files
-        config.update(dict(self.get_config_section('virtualenv')))
-        # 2. environmental variables
-        config.update(dict(self.get_environ_vars()))
-        # Then set the options with those values
-        for key, val in config.items():
-            key = key.replace('_', '-')
-            if not key.startswith('--'):
-                key = '--%s' % key  # only prefer long opts
-            option = self.get_option(key)
-            if option is not None:
-                # ignore empty values
-                if not val:
-                    continue
-                # handle multiline configs
-                if option.action == 'append':
-                    val = val.split()
-                else:
-                    option.nargs = 1
-                if option.action == 'store_false':
-                    val = not strtobool(val)
-                elif option.action in ('store_true', 'count'):
-                    val = strtobool(val)
-                try:
-                    val = option.convert_value(key, val)
-                except optparse.OptionValueError:
-                    e = sys.exc_info()[1]
-                    print("An error occured during configuration: %s" % e)
-                    sys.exit(3)
-                defaults[option.dest] = val
-        return defaults
-
-    def get_config_section(self, name):
-        """
-        Get a section of a configuration
-        """
-        if self.config.has_section(name):
-            return self.config.items(name)
-        return []
-
-    def get_environ_vars(self, prefix='VIRTUALENV_'):
-        """
-        Returns a generator with all environmental vars with prefix VIRTUALENV
-        """
-        for key, val in os.environ.items():
-            if key.startswith(prefix):
-                yield (key.replace(prefix, '').lower(), val)
-
-    def get_default_values(self):
-        """
-        Overridding to make updating the defaults after instantiation of
-        the option parser possible, update_defaults() does the dirty work.
-        """
-        if not self.process_default_values:
-            # Old, pre-Optik 1.5 behaviour.
-            return optparse.Values(self.defaults)
-
-        defaults = self.update_defaults(self.defaults.copy())  # ours
-        for option in self._get_all_options():
-            default = defaults.get(option.dest)
-            if isinstance(default, basestring):
-                opt_str = option.get_opt_string()
-                defaults[option.dest] = option.check_value(opt_str, default)
-        return optparse.Values(defaults)
-
-
-def main():
-    parser = ConfigOptionParser(
-        version=virtualenv_version,
-        usage="%prog [OPTIONS] DEST_DIR",
-        formatter=UpdatingDefaultsHelpFormatter())
-
-    parser.add_option(
-        '-v', '--verbose',
-        action='count',
-        dest='verbose',
-        default=0,
-        help="Increase verbosity")
-
-    parser.add_option(
-        '-q', '--quiet',
-        action='count',
-        dest='quiet',
-        default=0,
-        help='Decrease verbosity')
-
-    parser.add_option(
-        '-p', '--python',
-        dest='python',
-        metavar='PYTHON_EXE',
-        help='The Python interpreter to use, e.g., --python=python2.5 will use the python2.5 '
-        'interpreter to create the new environment.  The default is the interpreter that '
-        'virtualenv was installed with (%s)' % sys.executable)
-
-    parser.add_option(
-        '--clear',
-        dest='clear',
-        action='store_true',
-        help="Clear out the non-root install and start from scratch")
-
-    parser.set_defaults(system_site_packages=False)
-    parser.add_option(
-        '--no-site-packages',
-        dest='system_site_packages',
-        action='store_false',
-        help="Don't give access to the global site-packages dir to the "
-             "virtual environment (default)")
-
-    parser.add_option(
-        '--system-site-packages',
-        dest='system_site_packages',
-        action='store_true',
-        help="Give access to the global site-packages dir to the "
-             "virtual environment")
-
-    parser.add_option(
-        '--unzip-setuptools',
-        dest='unzip_setuptools',
-        action='store_true',
-        help="Unzip Setuptools or Distribute when installing it")
-
-    parser.add_option(
-        '--relocatable',
-        dest='relocatable',
-        action='store_true',
-        help='Make an EXISTING virtualenv environment relocatable.  '
-        'This fixes up scripts and makes all .pth files relative')
-
-    parser.add_option(
-        '--distribute', '--use-distribute',  # the second option is for legacy reasons here. Hi Kenneth!
-        dest='use_distribute',
-        action='store_true',
-        help='Use Distribute instead of Setuptools. Set environ variable '
-        'VIRTUALENV_DISTRIBUTE to make it the default ')
-
-    parser.add_option(
-        '--no-setuptools',
-        dest='no_setuptools',
-        action='store_true',
-        help='Do not install distribute/setuptools (or pip) '
-        'in the new virtualenv.')
-
-    parser.add_option(
-        '--no-pip',
-        dest='no_pip',
-        action='store_true',
-        help='Do not install pip in the new virtualenv.')
-
-    parser.add_option(
-        '--setuptools',
-        dest='use_distribute',
-        action='store_false',
-        help='Use Setuptools instead of Distribute.  Set environ variable '
-        'VIRTUALENV_SETUPTOOLS to make it the default ')
-
-    # Set this to True to use distribute by default, even in Python 2.
-    parser.set_defaults(use_distribute=False)
-
-    default_search_dirs = file_search_dirs()
-    parser.add_option(
-        '--extra-search-dir',
-        dest="search_dirs",
-        action="append",
-        default=default_search_dirs,
-        help="Directory to look for setuptools/distribute/pip distributions in. "
-        "You can add any number of additional --extra-search-dir paths.")
-
-    parser.add_option(
-        '--never-download',
-        dest="never_download",
-        action="store_true",
-        help="Never download anything from the network.  Instead, virtualenv will fail "
-        "if local distributions of setuptools/distribute/pip are not present.")
-
-    parser.add_option(
-        '--prompt',
-        dest='prompt',
-        help='Provides an alternative prompt prefix for this environment')
-
-    if 'extend_parser' in globals():
-        extend_parser(parser)
-
-    options, args = parser.parse_args()
-
-    global logger
-
-    if 'adjust_options' in globals():
-        adjust_options(options, args)
-
-    verbosity = options.verbose - options.quiet
-    logger = Logger([(Logger.level_for_integer(2 - verbosity), sys.stdout)])
-
-    if options.python and not os.environ.get('VIRTUALENV_INTERPRETER_RUNNING'):
-        env = os.environ.copy()
-        interpreter = resolve_interpreter(options.python)
-        if interpreter == sys.executable:
-            logger.warn('Already using interpreter %s' % interpreter)
-        else:
-            logger.notify('Running virtualenv with interpreter %s' % interpreter)
-            env['VIRTUALENV_INTERPRETER_RUNNING'] = 'true'
-            file = __file__
-            if file.endswith('.pyc'):
-                file = file[:-1]
-            popen = subprocess.Popen([interpreter, file] + sys.argv[1:], env=env)
-            raise SystemExit(popen.wait())
-
-    # Force --distribute on Python 3, since setuptools is not available.
-    if majver > 2:
-        options.use_distribute = True
-
-    if os.environ.get('PYTHONDONTWRITEBYTECODE') and not options.use_distribute:
-        print(
-            "The PYTHONDONTWRITEBYTECODE environment variable is "
-            "not compatible with setuptools. Either use --distribute "
-            "or unset PYTHONDONTWRITEBYTECODE.")
-        sys.exit(2)
-    if not args:
-        print('You must provide a DEST_DIR')
-        parser.print_help()
-        sys.exit(2)
-    if len(args) > 1:
-        print('There must be only one argument: DEST_DIR (you gave %s)' % (
-            ' '.join(args)))
-        parser.print_help()
-        sys.exit(2)
-
-    home_dir = args[0]
-
-    if os.environ.get('WORKING_ENV'):
-        logger.fatal('ERROR: you cannot run virtualenv while in a workingenv')
-        logger.fatal('Please deactivate your workingenv, then re-run this script')
-        sys.exit(3)
-
-    if 'PYTHONHOME' in os.environ:
-        logger.warn('PYTHONHOME is set.  You *must* activate the virtualenv before using it')
-        del os.environ['PYTHONHOME']
-
-    if options.relocatable:
-        make_environment_relocatable(home_dir)
-        return
-
-    create_environment(home_dir,
-                       site_packages=options.system_site_packages,
-                       clear=options.clear,
-                       unzip_setuptools=options.unzip_setuptools,
-                       use_distribute=options.use_distribute,
-                       prompt=options.prompt,
-                       search_dirs=options.search_dirs,
-                       never_download=options.never_download,
-                       no_setuptools=options.no_setuptools,
-                       no_pip=options.no_pip)
-    if 'after_install' in globals():
-        after_install(options, home_dir)
-
-def call_subprocess(cmd, show_stdout=True,
-                    filter_stdout=None, cwd=None,
-                    raise_on_returncode=True, extra_env=None,
-                    remove_from_env=None):
-    cmd_parts = []
-    for part in cmd:
-        if len(part) > 45:
-            part = part[:20]+"..."+part[-20:]
-        if ' ' in part or '\n' in part or '"' in part or "'" in part:
-            part = '"%s"' % part.replace('"', '\\"')
-        if hasattr(part, 'decode'):
-            try:
-                part = part.decode(sys.getdefaultencoding())
-            except UnicodeDecodeError:
-                part = part.decode(sys.getfilesystemencoding())
-        cmd_parts.append(part)
-    cmd_desc = ' '.join(cmd_parts)
-    if show_stdout:
-        stdout = None
-    else:
-        stdout = subprocess.PIPE
-    logger.debug("Running command %s" % cmd_desc)
-    if extra_env or remove_from_env:
-        env = os.environ.copy()
-        if extra_env:
-            env.update(extra_env)
-        if remove_from_env:
-            for varname in remove_from_env:
-                env.pop(varname, None)
-    else:
-        env = None
-    try:
-        proc = subprocess.Popen(
-            cmd, stderr=subprocess.STDOUT, stdin=None, stdout=stdout,
-            cwd=cwd, env=env)
-    except Exception:
-        e = sys.exc_info()[1]
-        logger.fatal(
-            "Error %s while executing command %s" % (e, cmd_desc))
-        raise
-    all_output = []
-    if stdout is not None:
-        stdout = proc.stdout
-        encoding = sys.getdefaultencoding()
-        fs_encoding = sys.getfilesystemencoding()
-        while 1:
-            line = stdout.readline()
-            try:
-                line = line.decode(encoding)
-            except UnicodeDecodeError:
-                line = line.decode(fs_encoding)
-            if not line:
-                break
-            line = line.rstrip()
-            all_output.append(line)
-            if filter_stdout:
-                level = filter_stdout(line)
-                if isinstance(level, tuple):
-                    level, line = level
-                logger.log(level, line)
-                if not logger.stdout_level_matches(level):
-                    logger.show_progress()
-            else:
-                logger.info(line)
-    else:
-        proc.communicate()
-    proc.wait()
-    if proc.returncode:
-        if raise_on_returncode:
-            if all_output:
-                logger.notify('Complete output from command %s:' % cmd_desc)
-                logger.notify('\n'.join(all_output) + '\n----------------------------------------')
-            raise OSError(
-                "Command %s failed with error code %s"
-                % (cmd_desc, proc.returncode))
-        else:
-            logger.warn(
-                "Command %s had error code %s"
-                % (cmd_desc, proc.returncode))
-
-
-def create_environment(home_dir, site_packages=False, clear=False,
-                       unzip_setuptools=False, use_distribute=False,
-                       prompt=None, search_dirs=None, never_download=False,
-                       no_setuptools=False, no_pip=False):
-    """
-    Creates a new environment in ``home_dir``.
-
-    If ``site_packages`` is true, then the global ``site-packages/``
-    directory will be on the path.
-
-    If ``clear`` is true (default False) then the environment will
-    first be cleared.
-    """
-    home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
-
-    py_executable = os.path.abspath(install_python(
-        home_dir, lib_dir, inc_dir, bin_dir,
-        site_packages=site_packages, clear=clear))
-
-    install_distutils(home_dir)
-
-    if not no_setuptools:
-        if use_distribute:
-            install_distribute(py_executable, unzip=unzip_setuptools,
-                               search_dirs=search_dirs, never_download=never_download)
-        else:
-            install_setuptools(py_executable, unzip=unzip_setuptools,
-                               search_dirs=search_dirs, never_download=never_download)
-
-        if not no_pip:
-            install_pip(py_executable, search_dirs=search_dirs, never_download=never_download)
-
-    install_activate(home_dir, bin_dir, prompt)
-
-def is_executable_file(fpath):
-    return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
-
-def path_locations(home_dir):
-    """Return the path locations for the environment (where libraries are,
-    where scripts go, etc)"""
-    # XXX: We'd use distutils.sysconfig.get_python_inc/lib but its
-    # prefix arg is broken: http://bugs.python.org/issue3386
-    if is_win:
-        # Windows has lots of problems with executables with spaces in
-        # the name; this function will remove them (using the ~1
-        # format):
-        mkdir(home_dir)
-        if ' ' in home_dir:
-            import ctypes
-            GetShortPathName = ctypes.windll.kernel32.GetShortPathNameW
-            size = max(len(home_dir)+1, 256)
-            buf = ctypes.create_unicode_buffer(size)
-            try:
-                u = unicode
-            except NameError:
-                u = str
-            ret = GetShortPathName(u(home_dir), buf, size)
-            if not ret:
-                print('Error: the path "%s" has a space in it' % home_dir)
-                print('We could not determine the short pathname for it.')
-                print('Exiting.')
-                sys.exit(3)
-            home_dir = str(buf.value)
-        lib_dir = join(home_dir, 'Lib')
-        inc_dir = join(home_dir, 'Include')
-        bin_dir = join(home_dir, 'Scripts')
-    if is_jython:
-        lib_dir = join(home_dir, 'Lib')
-        inc_dir = join(home_dir, 'Include')
-        bin_dir = join(home_dir, 'bin')
-    elif is_pypy:
-        lib_dir = home_dir
-        inc_dir = join(home_dir, 'include')
-        bin_dir = join(home_dir, 'bin')
-    elif not is_win:
-        lib_dir = join(home_dir, 'lib', py_version)
-        multiarch_exec = '/usr/bin/multiarch-platform'
-        if is_executable_file(multiarch_exec):
-            # In Mageia (2) and Mandriva distros the include dir must be like:
-            # virtualenv/include/multiarch-x86_64-linux/python2.7
-            # instead of being virtualenv/include/python2.7
-            p = subprocess.Popen(multiarch_exec, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-            stdout, stderr = p.communicate()
-            # stdout.strip is needed to remove newline character
-            inc_dir = join(home_dir, 'include', stdout.strip(), py_version + abiflags)
-        else:
-            inc_dir = join(home_dir, 'include', py_version + abiflags)
-        bin_dir = join(home_dir, 'bin')
-    return home_dir, lib_dir, inc_dir, bin_dir
-
-
-def change_prefix(filename, dst_prefix):
-    prefixes = [sys.prefix]
-
-    if is_darwin:
-        prefixes.extend((
-            os.path.join("/Library/Python", sys.version[:3], "site-packages"),
-            os.path.join(sys.prefix, "Extras", "lib", "python"),
-            os.path.join("~", "Library", "Python", sys.version[:3], "site-packages"),
-            # Python 2.6 no-frameworks
-            os.path.join("~", ".local", "lib","python", sys.version[:3], "site-packages"),
-            # System Python 2.7 on OSX Mountain Lion
-            os.path.join("~", "Library", "Python", sys.version[:3], "lib", "python", "site-packages")))
-
-    if hasattr(sys, 'real_prefix'):
-        prefixes.append(sys.real_prefix)
-    if hasattr(sys, 'base_prefix'):
-        prefixes.append(sys.base_prefix)
-    prefixes = list(map(os.path.expanduser, prefixes))
-    prefixes = list(map(os.path.abspath, prefixes))
-    # Check longer prefixes first so we don't split in the middle of a filename
-    prefixes = sorted(prefixes, key=len, reverse=True)
-    filename = os.path.abspath(filename)
-    for src_prefix in prefixes:
-        if filename.startswith(src_prefix):
-            _, relpath = filename.split(src_prefix, 1)
-            if src_prefix != os.sep: # sys.prefix == "/"
-                assert relpath[0] == os.sep
-                relpath = relpath[1:]
-            return join(dst_prefix, relpath)
-    assert False, "Filename %s does not start with any of these prefixes: %s" % \
-        (filename, prefixes)
-
-def copy_required_modules(dst_prefix):
-    import imp
-    # If we are running under -p, we need to remove the current
-    # directory from sys.path temporarily here, so that we
-    # definitely get the modules from the site directory of
-    # the interpreter we are running under, not the one
-    # virtualenv.py is installed under (which might lead to py2/py3
-    # incompatibility issues)
-    _prev_sys_path = sys.path
-    if os.environ.get('VIRTUALENV_INTERPRETER_RUNNING'):
-        sys.path = sys.path[1:]
-    try:
-        for modname in REQUIRED_MODULES:
-            if modname in sys.builtin_module_names:
-                logger.info("Ignoring built-in bootstrap module: %s" % modname)
-                continue
-            try:
-                f, filename, _ = imp.find_module(modname)
-            except ImportError:
-                logger.info("Cannot import bootstrap module: %s" % modname)
-            else:
-                if f is not None:
-                    f.close()
-                # special-case custom readline.so on OS X, but not for pypy:
-                if modname == 'readline' and sys.platform == 'darwin' and not (
-                        is_pypy or filename.endswith(join('lib-dynload', 'readline.so'))):
-                    dst_filename = join(dst_prefix, 'lib', 'python%s' % sys.version[:3], 'readline.so')
-                else:
-                    dst_filename = change_prefix(filename, dst_prefix)
-                copyfile(filename, dst_filename)
-                if filename.endswith('.pyc'):
-                    pyfile = filename[:-1]
-                    if os.path.exists(pyfile):
-                        copyfile(pyfile, dst_filename[:-1])
-    finally:
-        sys.path = _prev_sys_path
-
-
-def subst_path(prefix_path, prefix, home_dir):
-    prefix_path = os.path.normpath(prefix_path)
-    prefix = os.path.normpath(prefix)
-    home_dir = os.path.normpath(home_dir)
-    if not prefix_path.startswith(prefix):
-        logger.warn('Path not in prefix %r %r', prefix_path, prefix)
-        return
-    return prefix_path.replace(prefix, home_dir, 1)
-
-
-def install_python(home_dir, lib_dir, inc_dir, bin_dir, site_packages, clear):
-    """Install just the base environment, no distutils patches etc"""
-    if sys.executable.startswith(bin_dir):
-        print('Please use the *system* python to run this script')
-        return
-
-    if clear:
-        rmtree(lib_dir)
-        ## FIXME: why not delete it?
-        ## Maybe it should delete everything with #!/path/to/venv/python in it
-        logger.notify('Not deleting %s', bin_dir)
-
-    if hasattr(sys, 'real_prefix'):
-        logger.notify('Using real prefix %r' % sys.real_prefix)
-        prefix = sys.real_prefix
-    elif hasattr(sys, 'base_prefix'):
-        logger.notify('Using base prefix %r' % sys.base_prefix)
-        prefix = sys.base_prefix
-    else:
-        prefix = sys.prefix
-    mkdir(lib_dir)
-    fix_lib64(lib_dir)
-    stdlib_dirs = [os.path.dirname(os.__file__)]
-    if is_win:
-        stdlib_dirs.append(join(os.path.dirname(stdlib_dirs[0]), 'DLLs'))
-    elif is_darwin:
-        stdlib_dirs.append(join(stdlib_dirs[0], 'site-packages'))
-    if hasattr(os, 'symlink'):
-        logger.info('Symlinking Python bootstrap modules')
-    else:
-        logger.info('Copying Python bootstrap modules')
-    logger.indent += 2
-    try:
-        # copy required files...
-        for stdlib_dir in stdlib_dirs:
-            if not os.path.isdir(stdlib_dir):
-                continue
-            for fn in os.listdir(stdlib_dir):
-                bn = os.path.splitext(fn)[0]
-                if fn != 'site-packages' and bn in REQUIRED_FILES:
-                    copyfile(join(stdlib_dir, fn), join(lib_dir, fn))
-        # ...and modules
-        copy_required_modules(home_dir)
-    finally:
-        logger.indent -= 2
-    mkdir(join(lib_dir, 'site-packages'))
-    import site
-    site_filename = site.__file__
-    if site_filename.endswith('.pyc'):
-        site_filename = site_filename[:-1]
-    elif site_filename.endswith('$py.class'):
-        site_filename = site_filename.replace('$py.class', '.py')
-    site_filename_dst = change_prefix(site_filename, home_dir)
-    site_dir = os.path.dirname(site_filename_dst)
-    writefile(site_filename_dst, SITE_PY)
-    writefile(join(site_dir, 'orig-prefix.txt'), prefix)
-    site_packages_filename = join(site_dir, 'no-global-site-packages.txt')
-    if not site_packages:
-        writefile(site_packages_filename, '')
-
-    if is_pypy or is_win:
-        stdinc_dir = join(prefix, 'include')
-    else:
-        stdinc_dir = join(prefix, 'include', py_version + abiflags)
-    if os.path.exists(stdinc_dir):
-        copyfile(stdinc_dir, inc_dir)
-    else:
-        logger.debug('No include dir %s' % stdinc_dir)
-
-    platinc_dir = distutils.sysconfig.get_python_inc(plat_specific=1)
-    if platinc_dir != stdinc_dir:
-        platinc_dest = distutils.sysconfig.get_python_inc(
-            plat_specific=1, prefix=home_dir)
-        if platinc_dir == platinc_dest:
-            # Do platinc_dest manually due to a CPython bug;
-            # not http://bugs.python.org/issue3386 but a close cousin
-            platinc_dest = subst_path(platinc_dir, prefix, home_dir)
-        if platinc_dest:
-            # PyPy's stdinc_dir and prefix are relative to the original binary
-            # (traversing virtualenvs), whereas the platinc_dir is relative to
-            # the inner virtualenv and ignores the prefix argument.
-            # This seems more evolved than designed.
-            copyfile(platinc_dir, platinc_dest)
-
-    # pypy never uses exec_prefix, just ignore it
-    if sys.exec_prefix != prefix and not is_pypy:
-        if is_win:
-            exec_dir = join(sys.exec_prefix, 'lib')
-        elif is_jython:
-            exec_dir = join(sys.exec_prefix, 'Lib')
-        else:
-            exec_dir = join(sys.exec_prefix, 'lib', py_version)
-        for fn in os.listdir(exec_dir):
-            copyfile(join(exec_dir, fn), join(lib_dir, fn))
-
-    if is_jython:
-        # Jython has either jython-dev.jar and javalib/ dir, or just
-        # jython.jar
-        for name in 'jython-dev.jar', 'javalib', 'jython.jar':
-            src = join(prefix, name)
-            if os.path.exists(src):
-                copyfile(src, join(home_dir, name))
-        # XXX: registry should always exist after Jython 2.5rc1
-        src = join(prefix, 'registry')
-        if os.path.exists(src):
-            copyfile(src, join(home_dir, 'registry'), symlink=False)
-        copyfile(join(prefix, 'cachedir'), join(home_dir, 'cachedir'),
-                 symlink=False)
-
-    mkdir(bin_dir)
-    py_executable = join(bin_dir, os.path.basename(sys.executable))
-    if 'Python.framework' in prefix:
-        # OS X framework builds cause validation to break
-        # https://github.com/pypa/virtualenv/issues/322
-        if os.environ.get('__PYVENV_LAUNCHER__'):
-          os.unsetenv('__PYVENV_LAUNCHER__')
-        if re.search(r'/Python(?:-32|-64)*$', py_executable):
-            # The name of the python executable is not quite what
-            # we want, rename it.
-            py_executable = os.path.join(
-                    os.path.dirname(py_executable), 'python')
-
-    logger.notify('New %s executable in %s', expected_exe, py_executable)
-    pcbuild_dir = os.path.dirname(sys.executable)
-    pyd_pth = os.path.join(lib_dir, 'site-packages', 'virtualenv_builddir_pyd.pth')
-    if is_win and os.path.exists(os.path.join(pcbuild_dir, 'build.bat')):
-        logger.notify('Detected python running from build directory %s', pcbuild_dir)
-        logger.notify('Writing .pth file linking to build directory for *.pyd files')
-        writefile(pyd_pth, pcbuild_dir)
-    else:
-        pcbuild_dir = None
-        if os.path.exists(pyd_pth):
-            logger.info('Deleting %s (not Windows env or not build directory python)' % pyd_pth)
-            os.unlink(pyd_pth)
-
-    if sys.executable != py_executable:
-        ## FIXME: could I just hard link?
-        executable = sys.executable
-        shutil.copyfile(executable, py_executable)
-        make_exe(py_executable)
-        if is_win or is_cygwin:
-            pythonw = os.path.join(os.path.dirname(sys.executable), 'pythonw.exe')
-            if os.path.exists(pythonw):
-                logger.info('Also created pythonw.exe')
-                shutil.copyfile(pythonw, os.path.join(os.path.dirname(py_executable), 'pythonw.exe'))
-            python_d = os.path.join(os.path.dirname(sys.executable), 'python_d.exe')
-            python_d_dest = os.path.join(os.path.dirname(py_executable), 'python_d.exe')
-            if os.path.exists(python_d):
-                logger.info('Also created python_d.exe')
-                shutil.copyfile(python_d, python_d_dest)
-            elif os.path.exists(python_d_dest):
-                logger.info('Removed python_d.exe as it is no longer at the source')
-                os.unlink(python_d_dest)
-            # we need to copy the DLL to enforce that windows will load the correct one.
-            # may not exist if we are cygwin.
-            py_executable_dll = 'python%s%s.dll' % (
-                sys.version_info[0], sys.version_info[1])
-            py_executable_dll_d = 'python%s%s_d.dll' % (
-                sys.version_info[0], sys.version_info[1])
-            pythondll = os.path.join(os.path.dirname(sys.executable), py_executable_dll)
-            pythondll_d = os.path.join(os.path.dirname(sys.executable), py_executable_dll_d)
-            pythondll_d_dest = os.path.join(os.path.dirname(py_executable), py_executable_dll_d)
-            if os.path.exists(pythondll):
-                logger.info('Also created %s' % py_executable_dll)
-                shutil.copyfile(pythondll, os.path.join(os.path.dirname(py_executable), py_executable_dll))
-            if os.path.exists(pythondll_d):
-                logger.info('Also created %s' % py_executable_dll_d)
-                shutil.copyfile(pythondll_d, pythondll_d_dest)
-            elif os.path.exists(pythondll_d_dest):
-                logger.info('Removed %s as the source does not exist' % pythondll_d_dest)
-                os.unlink(pythondll_d_dest)
-        if is_pypy:
-            # make a symlink python --> pypy-c
-            python_executable = os.path.join(os.path.dirname(py_executable), 'python')
-            if sys.platform in ('win32', 'cygwin'):
-                python_executable += '.exe'
-            logger.info('Also created executable %s' % python_executable)
-            copyfile(py_executable, python_executable)
-
-            if is_win:
-                for name in 'libexpat.dll', 'libpypy.dll', 'libpypy-c.dll', 'libeay32.dll', 'ssleay32.dll', 'sqlite.dll':
-                    src = join(prefix, name)
-                    if os.path.exists(src):
-                        copyfile(src, join(bin_dir, name))
-
-    if os.path.splitext(os.path.basename(py_executable))[0] != expected_exe:
-        secondary_exe = os.path.join(os.path.dirname(py_executable),
-                                     expected_exe)
-        py_executable_ext = os.path.splitext(py_executable)[1]
-        if py_executable_ext == '.exe':
-            # python2.4 gives an extension of '.4' :P
-            secondary_exe += py_executable_ext
-        if os.path.exists(secondary_exe):
-            logger.warn('Not overwriting existing %s script %s (you must use %s)'
-                        % (expected_exe, secondary_exe, py_executable))
-        else:
-            logger.notify('Also creating executable in %s' % secondary_exe)
-            shutil.copyfile(sys.executable, secondary_exe)
-            make_exe(secondary_exe)
-
-    if '.framework' in prefix:
-        if 'Python.framework' in prefix:
-            logger.debug('MacOSX Python framework detected')
-            # Make sure we use the the embedded interpreter inside
-            # the framework, even if sys.executable points to
-            # the stub executable in ${sys.prefix}/bin
-            # See http://groups.google.com/group/python-virtualenv/
-            #                              browse_thread/thread/17cab2f85da75951
-            original_python = os.path.join(
-                prefix, 'Resources/Python.app/Contents/MacOS/Python')
-        if 'EPD' in prefix:
-            logger.debug('EPD framework detected')
-            original_python = os.path.join(prefix, 'bin/python')
-        shutil.copy(original_python, py_executable)
-
-        # Copy the framework's dylib into the virtual
-        # environment
-        virtual_lib = os.path.join(home_dir, '.Python')
-
-        if os.path.exists(virtual_lib):
-            os.unlink(virtual_lib)
-        copyfile(
-            os.path.join(prefix, 'Python'),
-            virtual_lib)
-
-        # And then change the install_name of the copied python executable
-        try:
-            mach_o_change(py_executable,
-                          os.path.join(prefix, 'Python'),
-                          '@executable_path/../.Python')
-        except:
-            e = sys.exc_info()[1]
-            logger.warn("Could not call mach_o_change: %s. "
-                        "Trying to call install_name_tool instead." % e)
-            try:
-                call_subprocess(
-                    ["install_name_tool", "-change",
-                     os.path.join(prefix, 'Python'),
-                     '@executable_path/../.Python',
-                     py_executable])
-            except:
-                logger.fatal("Could not call install_name_tool -- you must "
-                             "have Apple's development tools installed")
-                raise
-
-    if not is_win:
-        # Ensure that 'python', 'pythonX' and 'pythonX.Y' all exist
-        py_exe_version_major = 'python%s' % sys.version_info[0]
-        py_exe_version_major_minor = 'python%s.%s' % (
-            sys.version_info[0], sys.version_info[1])
-        py_exe_no_version = 'python'
-        required_symlinks = [ py_exe_no_version, py_exe_version_major,
-                         py_exe_version_major_minor ]
-
-        py_executable_base = os.path.basename(py_executable)
-
-        if py_executable_base in required_symlinks:
-            # Don't try to symlink to yourself.
-            required_symlinks.remove(py_executable_base)
-
-        for pth in required_symlinks:
-            full_pth = join(bin_dir, pth)
-            if os.path.exists(full_pth):
-                os.unlink(full_pth)
-            os.symlink(py_executable_base, full_pth)
-
-    if is_win and ' ' in py_executable:
-        # There's a bug with subprocess on Windows when using a first
-        # argument that has a space in it.  Instead we have to quote
-        # the value:
-        py_executable = '"%s"' % py_executable
-    # NOTE: keep this check as one line, cmd.exe doesn't cope with line breaks
-    cmd = [py_executable, '-c', 'import sys;out=sys.stdout;'
-        'getattr(out, "buffer", out).write(sys.prefix.encode("utf-8"))']
-    logger.info('Testing executable with %s %s "%s"' % tuple(cmd))
-    try:
-        proc = subprocess.Popen(cmd,
-                            stdout=subprocess.PIPE)
-        proc_stdout, proc_stderr = proc.communicate()
-    except OSError:
-        e = sys.exc_info()[1]
-        if e.errno == errno.EACCES:
-            logger.fatal('ERROR: The executable %s could not be run: %s' % (py_executable, e))
-            sys.exit(100)
-        else:
-            raise e
-
-    proc_stdout = proc_stdout.strip().decode("utf-8")
-    proc_stdout = os.path.normcase(os.path.abspath(proc_stdout))
-    norm_home_dir = os.path.normcase(os.path.abspath(home_dir))
-    if hasattr(norm_home_dir, 'decode'):
-        norm_home_dir = norm_home_dir.decode(sys.getfilesystemencoding())
-    if proc_stdout != norm_home_dir:
-        logger.fatal(
-            'ERROR: The executable %s is not functioning' % py_executable)
-        logger.fatal(
-            'ERROR: It thinks sys.prefix is %r (should be %r)'
-            % (proc_stdout, norm_home_dir))
-        logger.fatal(
-            'ERROR: virtualenv is not compatible with this system or executable')
-        if is_win:
-            logger.fatal(
-                'Note: some Windows users have reported this error when they '
-                'installed Python for "Only this user" or have multiple '
-                'versions of Python installed. Copying the appropriate '
-                'PythonXX.dll to the virtualenv Scripts/ directory may fix '
-                'this problem.')
-        sys.exit(100)
-    else:
-        logger.info('Got sys.prefix result: %r' % proc_stdout)
-
-    pydistutils = os.path.expanduser('~/.pydistutils.cfg')
-    if os.path.exists(pydistutils):
-        logger.notify('Please make sure you remove any previous custom paths from '
-                      'your %s file.' % pydistutils)
-    ## FIXME: really this should be calculated earlier
-
-    fix_local_scheme(home_dir)
-
-    if site_packages:
-        if os.path.exists(site_packages_filename):
-            logger.info('Deleting %s' % site_packages_filename)
-            os.unlink(site_packages_filename)
-
-    return py_executable
-
-
-def install_activate(home_dir, bin_dir, prompt=None):
-    home_dir = os.path.abspath(home_dir)
-    if is_win or is_jython and os._name == 'nt':
-        files = {
-            'activate.bat': ACTIVATE_BAT,
-            'deactivate.bat': DEACTIVATE_BAT,
-            'activate.ps1': ACTIVATE_PS,
-        }
-
-        # MSYS needs paths of the form /c/path/to/file
-        drive, tail = os.path.splitdrive(home_dir.replace(os.sep, '/'))
-        home_dir_msys = (drive and "/%s%s" or "%s%s") % (drive[:1], tail)
-
-        # Run-time conditional enables (basic) Cygwin compatibility
-        home_dir_sh = ("""$(if [ "$OSTYPE" "==" "cygwin" ]; then cygpath -u '%s'; else echo '%s'; fi;)""" %
-                       (home_dir, home_dir_msys))
-        files['activate'] = ACTIVATE_SH.replace('__VIRTUAL_ENV__', home_dir_sh)
-
-    else:
-        files = {'activate': ACTIVATE_SH}
-
-        # suppling activate.fish in addition to, not instead of, the
-        # bash script support.
-        files['activate.fish'] = ACTIVATE_FISH
-
-        # same for csh/tcsh support...
-        files['activate.csh'] = ACTIVATE_CSH
-
-    files['activate_this.py'] = ACTIVATE_THIS
-    if hasattr(home_dir, 'decode'):
-        home_dir = home_dir.decode(sys.getfilesystemencoding())
-    vname = os.path.basename(home_dir)
-    for name, content in files.items():
-        content = content.replace('__VIRTUAL_PROMPT__', prompt or '')
-        content = content.replace('__VIRTUAL_WINPROMPT__', prompt or '(%s)' % vname)
-        content = content.replace('__VIRTUAL_ENV__', home_dir)
-        content = content.replace('__VIRTUAL_NAME__', vname)
-        content = content.replace('__BIN_NAME__', os.path.basename(bin_dir))
-        writefile(os.path.join(bin_dir, name), content)
-
-def install_distutils(home_dir):
-    distutils_path = change_prefix(distutils.__path__[0], home_dir)
-    mkdir(distutils_path)
-    ## FIXME: maybe this prefix setting should only be put in place if
-    ## there's a local distutils.cfg with a prefix setting?
-    home_dir = os.path.abspath(home_dir)
-    ## FIXME: this is breaking things, removing for now:
-    #distutils_cfg = DISTUTILS_CFG + "\n[install]\nprefix=%s\n" % home_dir
-    writefile(os.path.join(distutils_path, '__init__.py'), DISTUTILS_INIT)
-    writefile(os.path.join(distutils_path, 'distutils.cfg'), DISTUTILS_CFG, overwrite=False)
-
-def fix_local_scheme(home_dir):
-    """
-    Platforms that use the "posix_local" install scheme (like Ubuntu with
-    Python 2.7) need to be given an additional "local" location, sigh.
-    """
-    try:
-        import sysconfig
-    except ImportError:
-        pass
-    else:
-        if sysconfig._get_default_scheme() == 'posix_local':
-            local_path = os.path.join(home_dir, 'local')
-            if not os.path.exists(local_path):
-                os.mkdir(local_path)
-                for subdir_name in os.listdir(home_dir):
-                    if subdir_name == 'local':
-                        continue
-                    os.symlink(os.path.abspath(os.path.join(home_dir, subdir_name)), \
-                                                            os.path.join(local_path, subdir_name))
-
-def fix_lib64(lib_dir):
-    """
-    Some platforms (particularly Gentoo on x64) put things in lib64/pythonX.Y
-    instead of lib/pythonX.Y.  If this is such a platform we'll just create a
-    symlink so lib64 points to lib
-    """
-    if [p for p in distutils.sysconfig.get_config_vars().values()
-        if isinstance(p, basestring) and 'lib64' in p]:
-        logger.debug('This system uses lib64; symlinking lib64 to lib')
-        assert os.path.basename(lib_dir) == 'python%s' % sys.version[:3], (
-            "Unexpected python lib dir: %r" % lib_dir)
-        lib_parent = os.path.dirname(lib_dir)
-        top_level = os.path.dirname(lib_parent)
-        lib_dir = os.path.join(top_level, 'lib')
-        lib64_link = os.path.join(top_level, 'lib64')
-        assert os.path.basename(lib_parent) == 'lib', (
-            "Unexpected parent dir: %r" % lib_parent)
-        if os.path.lexists(lib64_link):
-            return
-        os.symlink('lib', lib64_link)
-
-def resolve_interpreter(exe):
-    """
-    If the executable given isn't an absolute path, search $PATH for the interpreter
-    """
-    if os.path.abspath(exe) != exe:
-        paths = os.environ.get('PATH', '').split(os.pathsep)
-        for path in paths:
-            if os.path.exists(os.path.join(path, exe)):
-                exe = os.path.join(path, exe)
-                break
-    if not os.path.exists(exe):
-        logger.fatal('The executable %s (from --python=%s) does not exist' % (exe, exe))
-        raise SystemExit(3)
-    if not is_executable(exe):
-        logger.fatal('The executable %s (from --python=%s) is not executable' % (exe, exe))
-        raise SystemExit(3)
-    return exe
-
-def is_executable(exe):
-    """Checks a file is executable"""
-    return os.access(exe, os.X_OK)
-
-############################################################
-## Relocating the environment:
-
-def make_environment_relocatable(home_dir):
-    """
-    Makes the already-existing environment use relative paths, and takes out
-    the #!-based environment selection in scripts.
-    """
-    home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
-    activate_this = os.path.join(bin_dir, 'activate_this.py')
-    if not os.path.exists(activate_this):
-        logger.fatal(
-            'The environment doesn\'t have a file %s -- please re-run virtualenv '
-            'on this environment to update it' % activate_this)
-    fixup_scripts(home_dir)
-    fixup_pth_and_egg_link(home_dir)
-    ## FIXME: need to fix up distutils.cfg
-
-OK_ABS_SCRIPTS = ['python', 'python%s' % sys.version[:3],
-                  'activate', 'activate.bat', 'activate_this.py']
-
-def fixup_scripts(home_dir):
-    # This is what we expect at the top of scripts:
-    shebang = '#!%s/bin/python' % os.path.normcase(os.path.abspath(home_dir))
-    # This is what we'll put:
-    new_shebang = '#!/usr/bin/env python%s' % sys.version[:3]
-    if is_win:
-        bin_suffix = 'Scripts'
-    else:
-        bin_suffix = 'bin'
-    bin_dir = os.path.join(home_dir, bin_suffix)
-    home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
-    for filename in os.listdir(bin_dir):
-        filename = os.path.join(bin_dir, filename)
-        if not os.path.isfile(filename):
-            # ignore subdirs, e.g. .svn ones.
-            continue
-        f = open(filename, 'rb')
-        try:
-            try:
-                lines = f.read().decode('utf-8').splitlines()
-            except UnicodeDecodeError:
-                # This is probably a binary program instead
-                # of a script, so just ignore it.
-                continue
-        finally:
-            f.close()
-        if not lines:
-            logger.warn('Script %s is an empty file' % filename)
-            continue
-        if not lines[0].strip().startswith(shebang):
-            if os.path.basename(filename) in OK_ABS_SCRIPTS:
-                logger.debug('Cannot make script %s relative' % filename)
-            elif lines[0].strip() == new_shebang:
-                logger.info('Script %s has already been made relative' % filename)
-            else:
-                logger.warn('Script %s cannot be made relative (it\'s not a normal script that starts with %s)'
-                            % (filename, shebang))
-            continue
-        logger.notify('Making script %s relative' % filename)
-        script = relative_script([new_shebang] + lines[1:])
-        f = open(filename, 'wb')
-        f.write('\n'.join(script).encode('utf-8'))
-        f.close()
-
-def relative_script(lines):
-    "Return a script that'll work in a relocatable environment."
-    activate = "import os; activate_this=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'activate_this.py'); execfile(activate_this, dict(__file__=activate_this)); del os, activate_this"
-    # Find the last future statement in the script. If we insert the activation
-    # line before a future statement, Python will raise a SyntaxError.
-    activate_at = None
-    for idx, line in reversed(list(enumerate(lines))):
-        if line.split()[:3] == ['from', '__future__', 'import']:
-            activate_at = idx + 1
-            break
-    if activate_at is None:
-        # Activate after the shebang.
-        activate_at = 1
-    return lines[:activate_at] + ['', activate, ''] + lines[activate_at:]
-
-def fixup_pth_and_egg_link(home_dir, sys_path=None):
-    """Makes .pth and .egg-link files use relative paths"""
-    home_dir = os.path.normcase(os.path.abspath(home_dir))
-    if sys_path is None:
-        sys_path = sys.path
-    for path in sys_path:
-        if not path:
-            path = '.'
-        if not os.path.isdir(path):
-            continue
-        path = os.path.normcase(os.path.abspath(path))
-        if not path.startswith(home_dir):
-            logger.debug('Skipping system (non-environment) directory %s' % path)
-            continue
-        for filename in os.listdir(path):
-            filename = os.path.join(path, filename)
-            if filename.endswith('.pth'):
-                if not os.access(filename, os.W_OK):
-                    logger.warn('Cannot write .pth file %s, skipping' % filename)
-                else:
-                    fixup_pth_file(filename)
-            if filename.endswith('.egg-link'):
-                if not os.access(filename, os.W_OK):
-                    logger.warn('Cannot write .egg-link file %s, skipping' % filename)
-                else:
-                    fixup_egg_link(filename)
-
-def fixup_pth_file(filename):
-    lines = []
-    prev_lines = []
-    f = open(filename)
-    prev_lines = f.readlines()
-    f.close()
-    for line in prev_lines:
-        line = line.strip()
-        if (not line or line.startswith('#') or line.startswith('import ')
-            or os.path.abspath(line) != line):
-            lines.append(line)
-        else:
-            new_value = make_relative_path(filename, line)
-            if line != new_value:
-                logger.debug('Rewriting path %s as %s (in %s)' % (line, new_value, filename))
-            lines.append(new_value)
-    if lines == prev_lines:
-        logger.info('No changes to .pth file %s' % filename)
-        return
-    logger.notify('Making paths in .pth file %s relative' % filename)
-    f = open(filename, 'w')
-    f.write('\n'.join(lines) + '\n')
-    f.close()
-
-def fixup_egg_link(filename):
-    f = open(filename)
-    link = f.readline().strip()
-    f.close()
-    if os.path.abspath(link) != link:
-        logger.debug('Link in %s already relative' % filename)
-        return
-    new_link = make_relative_path(filename, link)
-    logger.notify('Rewriting link %s in %s as %s' % (link, filename, new_link))
-    f = open(filename, 'w')
-    f.write(new_link)
-    f.close()
-
-def make_relative_path(source, dest, dest_is_directory=True):
-    """
-    Make a filename relative, where the filename is dest, and it is
-    being referred to from the filename source.
-
-        >>> make_relative_path('/usr/share/something/a-file.pth',
-        ...                    '/usr/share/another-place/src/Directory')
-        '../another-place/src/Directory'
-        >>> make_relative_path('/usr/share/something/a-file.pth',
-        ...                    '/home/user/src/Directory')
-        '../../../home/user/src/Directory'
-        >>> make_relative_path('/usr/share/a-file.pth', '/usr/share/')
-        './'
-    """
-    source = os.path.dirname(source)
-    if not dest_is_directory:
-        dest_filename = os.path.basename(dest)
-        dest = os.path.dirname(dest)
-    dest = os.path.normpath(os.path.abspath(dest))
-    source = os.path.normpath(os.path.abspath(source))
-    dest_parts = dest.strip(os.path.sep).split(os.path.sep)
-    source_parts = source.strip(os.path.sep).split(os.path.sep)
-    while dest_parts and source_parts and dest_parts[0] == source_parts[0]:
-        dest_parts.pop(0)
-        source_parts.pop(0)
-    full_parts = ['..']*len(source_parts) + dest_parts
-    if not dest_is_directory:
-        full_parts.append(dest_filename)
-    if not full_parts:
-        # Special case for the current directory (otherwise it'd be '')
-        return './'
-    return os.path.sep.join(full_parts)
-
-
-
-############################################################
-## Bootstrap script creation:
-
-def create_bootstrap_script(extra_text, python_version=''):
-    """
-    Creates a bootstrap script, which is like this script but with
-    extend_parser, adjust_options, and after_install hooks.
-
-    This returns a string that (written to disk of course) can be used
-    as a bootstrap script with your own customizations.  The script
-    will be the standard virtualenv.py script, with your extra text
-    added (your extra text should be Python code).
-
-    If you include these functions, they will be called:
-
-    ``extend_parser(optparse_parser)``:
-        You can add or remove options from the parser here.
-
-    ``adjust_options(options, args)``:
-        You can change options here, or change the args (if you accept
-        different kinds of arguments, be sure you modify ``args`` so it is
-        only ``[DEST_DIR]``).
-
-    ``after_install(options, home_dir)``:
-
-        After everything is installed, this function is called.  This
-        is probably the function you are most likely to use.  An
-        example would be::
-
-            def after_install(options, home_dir):
-                subprocess.call([join(home_dir, 'bin', 'easy_install'),
-                                 'MyPackage'])
-                subprocess.call([join(home_dir, 'bin', 'my-package-script'),
-                                 'setup', home_dir])
-
-        This example immediately installs a package, and runs a setup
-        script from that package.
-
-    If you provide something like ``python_version='2.5'`` then the
-    script will start with ``#!/usr/bin/env python2.5`` instead of
-    ``#!/usr/bin/env python``.  You can use this when the script must
-    be run with a particular Python version.
-    """
-    filename = __file__
-    if filename.endswith('.pyc'):
-        filename = filename[:-1]
-    f = codecs.open(filename, 'r', encoding='utf-8')
-    content = f.read()
-    f.close()
-    py_exe = 'python%s' % python_version
-    content = (('#!/usr/bin/env %s\n' % py_exe)
-               + '## WARNING: This file is generated\n'
-               + content)
-    return content.replace('##EXT' 'END##', extra_text)
-
-
-
-default_target_dir = 'venv'
-
-pip_install_packages = filter(len, open('requirements.txt').readlines())
-
-import os
-import subprocess
-import sys
-
-def adjust_options(options, args):
-  if len(args)==0:
-    os.chdir(os.path.dirname(__file__))
-    args.append(default_target_dir)
-
-def after_install(options, home_dir):
-  from os.path import join
-  pip = join(home_dir, 'bin/pip')
-  if not os.path.exists(pip):
-    # on windows
-    pip = join(home_dir, 'Scripts/pip.exe')
-  if not os.path.exists(pip):
-    print "error", pip, "is missing"
-  if sys.version_info < (2, 7):
-    subprocess.call([pip, 'install', 'importlib'])
-  for prog in pip_install_packages:
-    subprocess.call([pip, 'install', prog])
-
-
-
-def convert(s):
-    b = base64.b64decode(s.encode('ascii'))
-    return zlib.decompress(b).decode('utf-8')
-
-##file site.py
-SITE_PY = convert("""
-eJzFPf1z2zaWv/OvwMqToZTIdOK0vR2nzo2TOK3v3MTbpLO5dT1aSoIs1hTJEqRl7c3d337vAwAB
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-AU1oWSsgZVLJfVXIWbJIZrbhOq/TuSjSeCbF3//OU6OmYRiofCXXS1lKkQEyAFMCrALxgK9JKWb5
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-WxgOIAJJg75x5omq7Dg0O5EDgBLXsQIpWSkxXMVJBsz6UzwjtP+aZPN8rUZEAVgtJX6rVeXOf9hD
-AGjtEGAc4GKZ1ayzNLmR6WYECHwG7Eup6rRCgZgnpZxVeZlIRQAAtY2Qd4D0WMSl1CRkzjRyOyb6
-E02SDBcWBQwFHl8iSRbJdV2ShIlFApwLXPH+48/i3embs5MPmscMMJbZ6xXgDFBooR2cYABxUKvy
-IM1BoKPgHP+IeD5HIbvG8QGvpsHBvSsdDGHuRdTu4yw4kF0vrh4G5liBMqGxAur339BlrJZAn/+5
-Z72D4GQbVWji/G29zEEms3glxTJm/kLOCL7XcF5HRbV8BdygEE4FpFK4OIhggvCAJC7NhnkmRQEs
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-""")
-
-##file ez_setup.py
-EZ_SETUP_PY = convert("""
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-""")
-
-##file distribute_from_egg.py
-DISTRIBUTE_FROM_EGG_PY = convert("""
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-hLLdWkDbi/DeEpCjNb3u/zccT2Ob8gtnwVyI
-""")
-
-##file distribute_setup.py
-DISTRIBUTE_SETUP_PY = convert("""
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-kXvcQGDu2uCbeoB0zQQhg6vrQKjiAHyEyWpHAfp4mQTTXBBR4JuX4v4N8FOQLFqfGg+eLSj7gOi0
-2pMNaxWucOZfSlGJX1LVe/c7VH1QW6h7lpKh8gq/BlCMt5cxXQ6APtyZjEOLZZBp6AGM+vl6Yuoc
-WEl4WohVCsQr09Ww6vz3PN6JJsyjR90RauiaoVRZ76aEhYxoDeVuGqo1fCep6VoKbkX46ygg3tHD
-XtGPP/6XTIuSrAD5ifoMCDz7z7MzJ/vL15GSvUYqtd+kK9cM3QEjDbLfpdm1b7eZSf6bhK/m5EeH
-RWhkOJ/xEDCczxHPq9loXZIUtYCJsCUhASN7LtfnGyINJeZxAC6pD8dOXQaIHth+qTUwwhsUoL9I
-c4AEBDNMxAU2eSNbMwiSQnF5BnAZEzZmi7or5IFZYp95Pa1zxj0ixfnnaBNFS9xn0OA6gpBysgXi
-rIwV3tkQsBPnqs8ATLawsyOAuvnqmOz/4iqxVFGcnAP3cyi4z4fFtrio3Svkx65+CGRxutqEoIRT
-5VvwlUW8RMZ670G5L4aF6k1pGwLE31/MSyL2bVfwpoF6uVbHLGK6NZV+e8gUY6o89r2js7L0aooZ
-iooIK35Nn+elDhjjT4cytKnsHui71g35qF8L/glDNOSjjPeuZ8lL8Tf7pmXFJcbWcydpcgjXTk03
-KLymggtomrVgWpLZPS5/xBEZS+WhE0Sakjkdp8YDF4jELUb1Lnj0QUAJNFy5AgkU0TSNJQ5b72qC
-8WJr0y4Dl9nwkIo7PcugabH114IrEJBr2uWqPLd3Z7csr5c6PUIbF8wWL5wruZPwGOtnwXOo1Rfz
-FnjX0ZDt3YAMMJNp6SPly+mn63dTS6KmfPTur6Rf/3MDmNTgjVgRmNXN1speCxxXbLUDJai5ztzU
-jlyh60S2Av6onMMYFcUu6qYEjqeuGmnxCw0qKDjGAzedrUZdHft3CoTPvqTNXkFpldL/TsLSV1PZ
-/zn6ipR/wVrbr/fUM4zhy8vHvBF4rExcM8RaLRbtwDhGPsSxepHeZMCCOzDhfwBqDMd7
-""")
-
-##file activate.sh
-ACTIVATE_SH = convert("""
-eJytVVFvokAQfudXTLEPtTlLeo9tvMSmJpq02hSvl7u2wRUG2QR2DSxSe7n/frOACEVNLlceRHa+
-nfl25pvZDswCnoDPQ4QoTRQsENIEPci4CsBMZBq7CAsuLOYqvmYKTTj3YxnBgiXBudGBjUzBZUJI
-BXEqgCvweIyuCjeG4eF2F5x14bcB9KQiQQWrjSddI1/oQIx6SYYeoFjzWIoIhYI1izlbhJjkKO7D
-M/QEmKfO9O7WeRo/zr4P7pyHwWxkwitcgwpQ5Ej96OX+PmiFwLeVjFUOrNYKaq1Nud3nR2n8nI2m
-k9H0friPTGVsUdptaxGrTEfpNVFEskxpXtUkkCkl1UNF9cgLBkx48J4EXyALuBtAwNYIjF5kcmUU
-abMKmMq1ULoiRbgsDEkTSsKSGFCJ6Z8vY/2xYiSacmtyAfCDdCNTVZoVF8vSTQOoEwSnOrngBkws
-MYGMBMg8/bMBLSYKS7pYEXP0PqT+ZmBT0Xuy+Pplj5yn4aM9nk72JD8/Wi+Gr98sD9eWSMOwkapD
-BbUv91XSvmyVkICt2tmXR4tWmrcUCsjWOpw87YidEC8i0gdTSOFhouJUNxR+4NYBG0MftoCTD9F7
-2rTtxG3oPwY1b2HncYwhrlmj6Wq924xtGDWqfdNxap+OYxplEurnMVo9RWks+rH8qKEtx7kZT5zJ
-4H7oOFclrN6uFe+d+nW2aIUsSgs/42EIPuOhXq+jEo3S6tX6w2ilNkDnIpHCWdEQhFgwj9pkk7FN
-l/y5eQvRSIQ5+TrL05lewxWpt/Lbhes5cJF3mLET1MGhcKCF+40tNWnUulxrpojwDo2sObdje3Bz
-N3QeHqf3D7OjEXMVV8LN3ZlvuzoWHqiUcNKHtwNd0IbvPGKYYM31nPKCgkUILw3KL+Y8l7aO1ArS
-Ad37nIU0fCj5NE5gQCuC5sOSu+UdI2NeXg/lFkQIlFpdWVaWZRfvqGiirC9o6liJ9FXGYrSY9mI1
-D/Ncozgn13vJvsznr7DnkJWXsyMH7e42ljdJ+aqNDF1bFnKWFLdj31xtaJYK6EXFgqmV/ymD/ROG
-+n8O9H8f5vsGOWXsL1+1k3g=
-""")
-
-##file activate.fish
-ACTIVATE_FISH = convert("""
-eJyVVWFv2jAQ/c6vuBoqQVWC9nVSNVGVCaS2VC2rNLWVZZILWAs2s52wVvvxsyEJDrjbmgpK7PP5
-3bt3d22YLbmGlGcIq1wbmCPkGhPYcLMEEsGciwGLDS+YwSjlekngLFVyBe73GXSXxqw/DwbuTS8x
-yyKpFr1WG15lDjETQhpQuQBuIOEKY5O9tlppLqxHKSDByjVAPwEy+mXtCq5MzjIUBTCRgEKTKwFG
-gpBqxTLYXgN2myspVigMaYF92tZSowGZJf4mFExxNs9Qb614CgZtmH0BpEOn11f0cXI/+za8pnfD
-2ZjA1sg9zlV/8QvcMhxbNu0QwgYokn/d+n02nt6Opzcjcnx1vXcIoN74O4ymWQXmHURfJw9jenc/
-vbmb0enj6P5+cuVhqlKm3S0u2XRtRbA2QQAhV7VhBF0rsgUX9Ur1rBUXJgVSy8O751k8mzY5OrKH
-RW3eaQhYGTr8hrXO59ALhxQ83mCsDLAid3T72CCSdJhaFE+fXgicXAARUiR2WeVO37gH3oYHzFKo
-9k7CaPZ1UeNwH1tWuXA4uFKYYcEa8vaKqXl7q1UpygMPhFLvlVKyNzsSM3S2km7UBOl4xweUXk5u
-6e3wZmQ9leY1XE/Ili670tr9g/5POBBpGIJXCCF79L1siarl/dbESa8mD8PL61GpzqpzuMS7tqeB
-1YkALrRBloBMbR9yLcVx7frQAgUqR7NZIuzkEu110gbNit1enNs82Rx5utq7Z3prU78HFRgulqNC
-OTwbqJa9vkJFclQgZSjbKeBgSsUtCtt9D8OwAbIVJuewQdfvQRaoFE9wd1TmCuRG7OgJ1bVXGHc7
-z5WDL/WW36v2oi37CyVBak61+yPBA9C1qqGxzKQqZ0oPuocU9hpud0PIp8sDHkXR1HKkNlzjuUWA
-a0enFUyzOWZA4yXGP+ZMI3Tdt2OuqU/SO4q64526cPE0A7ZyW2PMbWZiZ5HamIZ2RcCKLXhcDl2b
-vXL+eccQoRzem80mekPDEiyiWK4GWqZmwxQOmPM0eIfgp1P9cqrBsewR2p/DPMtt+pfcYM+Ls2uh
-hALufTAdmGl8B1H3VPd2af8fQAc4PgqjlIBL9cGQqNpXaAwe3LrtVn8AkZTUxg==
-""")
-
-##file activate.csh
-ACTIVATE_CSH = convert("""
-eJx9VG1P2zAQ/u5fcYQKNgTNPtN1WxlIQ4KCUEGaxuQ6yYVYSuzKdhqVX7+zk3bpy5YPUXL3PPfc
-ne98DLNCWshliVDV1kGCUFvMoJGugMjq2qQIiVSxSJ1cCofD1BYRnOVGV0CfZ0N2DD91DalQSjsw
-tQLpIJMGU1euvPe7QeJlkKzgWixlhnAt4aoUVsLnLBiy5NtbJWQ5THX1ZciYKKWwkOFaE04dUm6D
-r/zh7pq/3D7Nnid3/HEy+wFHY/gEJydg0aFaQrBFgz1c5DG1IhTs+UZgsBC2GMFBlaeH+8dZXwcW
-VPvCjXdlAvCfQsE7al0+07XjZvrSCUevR5dnkVeKlFYZmUztG4BdzL2u9KyLVabTU0bdfg7a0hgs
-cSmUg6UwUiQl2iHrcbcVGNvPCiLOe7+cRwG13z9qRGgx2z6DHjfm/Op2yqeT+xvOLzs0PTKHDz2V
-tkckFHoQfQRXoGJAj9el0FyJCmEMhzgMS4sB7KPOE2ExoLcSieYwDvR+cP8cg11gKkVJc2wRcm1g
-QhYFlXiTaTfO2ki0fQoiFM4tLuO4aZrhOzqR4dIPcWx17hphMBY+Srwh7RTyN83XOWkcSPh1Pg/k
-TXX/jbJTbMtUmcxZ+/bbqOsy82suFQg/BhdSOTRhMNBHlUarCpU7JzBhmkKmRejKOQzayQe6MWoa
-n1wqWmuh6LZAaHxcdeqIlVLhIBJdO9/kbl0It2oEXQj+eGjJOuvOIR/YGRqvFhttUB2XTvLXYN2H
-37CBdbW2W7j2r2+VsCn0doVWcFG1/4y1VwBjfwAyoZhD
-""")
-
-##file activate.bat
-ACTIVATE_BAT = convert("""
-eJx9UdEKgjAUfW6wfxjiIH+hEDKUFHSKLCMI7kNOEkIf9P9pTJ3OLJ/03HPPPed4Es9XS9qqwqgT
-PbGKKOdXL4aAFS7A4gvAwgijuiKlqOpGlATS2NeMLE+TjJM9RkQ+SmqAXLrBo1LLIeLdiWlD6jZt
-r7VNubWkndkXaxg5GO3UaOOKS6drO3luDDiO5my3iA0YAKGzPRV1ack8cOdhysI0CYzIPzjSiH5X
-0QcvC8Lfaj0emsVKYF2rhL5L3fCkVjV76kShi59NHwDniAHzkgDgqBcwOgTMx+gDQQqXCw==
-""")
-
-##file deactivate.bat
-DEACTIVATE_BAT = convert("""
-eJxzSE3OyFfIT0vj4ipOLVEI8wwKCXX0iXf1C7Pl4spMU0hJTcvMS01RiPf3cYmHyQYE+fsGhCho
-cCkAAUibEkTEVhWLMlUlLk6QGixStlyaeCyJDPHw9/Pw93VFsQguim4ZXAJoIUw5DhX47XUM8UCx
-EchHtwsohN1bILUgw61c/Vy4AJYPYm4=
-""")
-
-##file activate.ps1
-ACTIVATE_PS = convert("""
-eJylWdmS40Z2fVeE/oHT6rCloNUEAXDThB6wAyQAEjsB29GBjdgXYiWgmC/zgz/Jv+AEWNVd3S2N
-xuOKYEUxM+/Jmzfvcm7W//zXf/+wUMOoXtyi1F9kbd0sHH/hFc2iLtrK9b3FrSqyxaVQwr8uhqJd
-uHaeg9mqzRdR8/13Pyy8qPLdJh0+LMhi0QCoXxYfFh9WtttEnd34H8p6/f1300KauwrULws39e18
-0ZaLNm9rgN/ZVf3h++/e124Vlc0vKsspHy+Yyi5+XbzPhijvCtduoiL/kA1ukWV27n0o7Sb8LIFj
-CvWR5GQgUJdp1Pw8TS9+rPy6SDv/+e3d+0+4qw8f3v20+PliV37efEYBAB9FTKC+RHn/Cfxn3rdv
-00Fube5O+iyCtHDs9BfPfz3q4sfFv9d91Ljhfy7ei0VO+nVTtdOkv/jpt0l2AX6iG1jXgKnnDuD4
-ke2k/i8fzzz5UedkVcP4pwF+Wvz2FJl+3vt598urXf5Y6LNA5WcFOP7r0sW7b9a+W/xcu0Xpv5zk
-Kfq3P9Dz9di/fCxS72MXVU1rpx9L4Bxl85Wmn5a+zP76Zuh3pL9ROWr87PN+//GHIl+oOtvn9XSU
-qH+p0gQBFnx1uV+JLH5O5zv+PXW+WepXVVHZT0+oQezkIATcIm+ivPV/z5J/+cYj3ir4w0Lx09vC
-e5n/y5/Y5LPPfdrqb88ga/PabxZRVfmp39l588m/6u+/e+OpP+dF7n1WZpJ9//Z4v372fDDz9eHB
-7Juvs/BLMHzrxL9+9twXpJfhd1/DrpQ5Euu/vlss3wp9HXC/54C/Ld69m6zwdx3tC0d8daSv0V8B
-n4b9YYF53sJelJV/ix6LZspw/sJtqyl5LJ5r/23htA1Imfm/gt9R7dqVB1LjhydAX4Gb+zksQF59
-9+P7H//U+376afFuvh2/T6P85Xr/5c8C6OXyFY4BGuN+EE0+GeR201b+wkkLN5mmBY5TfMw8ngqL
-CztXxCSXKMCYrRIElWkEJlEPYsSOeKBVZCAQTKBhApMwRFQzmCThE0YQu2CdEhgjbgmk9GluHpfR
-/hhwJCZhGI5jt5FsAkOrObVyE6g2y1snyhMGFlDY1x+BoHpCMulTj5JYWNAYJmnKpvLxXgmQ8az1
-4fUGxxcitMbbhDFcsiAItg04E+OSBIHTUYD1HI4FHH4kMREPknuYRMyhh3AARWMkfhCketqD1CWJ
-mTCo/nhUScoQcInB1hpFhIKoIXLo5jLpwFCgsnLCx1QlEMlz/iFEGqzH3vWYcpRcThgWnEKm0QcS
-rA8ek2a2IYYeowUanOZOlrbWSJUC4c7y2EMI3uJPMnMF/SSXdk6E495VLhzkWHps0rOhKwqk+xBI
-DhJirhdUCTamMfXz2Hy303hM4DFJ8QL21BcPBULR+gcdYxoeiDqOFSqpi5B5PUISfGg46gFZBPo4
-jdh8lueaWuVSMTURfbAUnLINr/QYuuYoMQV6l1aWxuZVTjlaLC14UzqZ+ziTGDzJzhiYoPLrt3uI
-tXkVR47kAo09lo5BD76CH51cTt1snVpMOttLhY93yxChCQPI4OBecS7++h4p4Bdn4H97bJongtPk
-s9gQnXku1vzsjjmX4/o4YUDkXkjHwDg5FXozU0fW4y5kyeYW0uJWlh536BKr0kMGjtzTkng6Ep62
-uTWnQtiIqKnEsx7e1hLtzlXs7Upw9TwEnp0t9yzCGgUJIZConx9OHJArLkRYW0dW42G9OeR5Nzwk
-yk1mX7du5RGHT7dka7N3AznmSif7y6tuKe2N1Al/1TUPRqH6E2GLVc27h9IptMLkCKQYRqPQJgzV
-2m6WLsSipS3v3b1/WmXEYY1meLEVIU/arOGVkyie7ZsH05ZKpjFW4cpY0YkjySpSExNG2TS8nnJx
-nrQmWh2WY3cP1eISP9wbaVK35ZXc60yC3VN/j9n7UFoK6zvjSTE2+Pvz6Mx322rnftfP8Y0XKIdv
-Qd7AfK0nexBTMqRiErvCMa3Hegpfjdh58glW2oNMsKeAX8x6YJLZs9K8/ozjJkWL+JmECMvhQ54x
-9rsTHwcoGrDi6Y4I+H7yY4/rJVPAbYymUH7C2D3uiUS3KQ1nrCAUkE1dJMneDQIJMQQx5SONxoEO
-OEn1/Ig1eBBUeEDRuOT2WGGGE4bNypBLFh2PeIg3bEbg44PHiqNDbGIQm50LW6MJU62JHCGBrmc9
-2F7WBJrrj1ssnTAK4sxwRgh5LLblhwNAclv3Gd+jC/etCfyfR8TMhcWQz8TBIbG8IIyAQ81w2n/C
-mHWAwRzxd3WoBY7BZnsqGOWrOCKwGkMMNfO0Kci/joZgEocLjNnzgcmdehPHJY0FudXgsr+v44TB
-I3jnMGnsK5veAhgi9iXGifkHMOC09Rh9cAw9sQ0asl6wKMk8mpzFYaaDSgG4F0wisQDDBRpjCINg
-FIxhlhQ31xdSkkk6odXZFpTYOQpOOgw9ugM2cDQ+2MYa7JsEirGBrOuxsQy5nPMRdYjsTJ/j1iNw
-FeSt1jY2+dd5yx1/pzZMOQXUIDcXeAzR7QlDRM8AMkUldXOmGmvYXPABjxqkYKO7VAY6JRU7kpXr
-+Epu2BU3qFFXClFi27784LrDZsJwbNlDw0JzhZ6M0SMXE4iBHehCpHVkrQhpTFn2dsvsZYkiPEEB
-GSEAwdiur9LS1U6P2U9JhGp4hnFpJo4FfkdJHcwV6Q5dV1Q9uNeeu7rV8PAjwdFg9RLtroifOr0k
-uOiRTo/obNPhQIf42Fr4mtThWoSjitEdAmFW66UCe8WFjPk1YVNpL9srFbond7jrLg8tqAasIMpy
-zkH0SY/6zVAwJrEc14zt14YRXdY+fcJ4qOd2XKB0/Kghw1ovd11t2o+zjt+txndo1ZDZ2T+uMVHT
-VSXhedBAHoJIID9xm6wPQI3cXY+HR7vxtrJuCKh6kbXaW5KkVeJsdsjqsYsOwYSh0w5sMbu7LF8J
-5T7U6LJdiTx+ca7RKlulGgS5Z1JSU2Llt32cHFipkaurtBrvNX5UtvNZjkufZ/r1/XyLl6yOpytL
-Km8Fn+y4wkhlqZP5db0rooqy7xdL4wxzFVTX+6HaxuQJK5E5B1neSSovZ9ALB8091dDbbjVxhWNY
-Ve5hn1VnI9OF0wpvaRm7SZuC1IRczwC7GnkhPt3muHV1YxUJfo+uh1sYnJy+vI0ZwuPV2uqWJYUH
-bmBsi1zmFSxHrqwA+WIzLrHkwW4r+bad7xbOzJCnKIa3S3YvrzEBK1Dc0emzJW+SqysQfdEDorQG
-9ZJlbQzEHQV8naPaF440YXzJk/7vHGK2xwuP+Gc5xITxyiP+WQ4x18oXHjFzCBy9kir1EFTAm0Zq
-LYwS8MpiGhtfxiBRDXpxDWxk9g9Q2fzPPAhS6VFDAc/aiNGatUkPtZIStZFQ1qD0IlJa/5ZPAi5J
-ySp1ETDomZMnvgiysZSBfMikrSDte/K5lqV6iwC5q7YN9I1dBZXUytDJNqU74MJsUyNNLAPopWK3
-tzmLkCiDyl7WQnj9sm7Kd5kzgpoccdNeMw/6zPVB3pUwMgi4C7hj4AMFAf4G27oXH8NNT9zll/sK
-S6wVlQwazjxWKWy20ZzXb9ne8ngGalPBWSUSj9xkc1drsXkZ8oOyvYT3e0rnYsGwx85xZB9wKeKg
-cJKZnamYwiaMymZvzk6wtDUkxmdUg0mPad0YHtvzpjEfp2iMxvORhnx0kCVLf5Qa43WJsVoyfEyI
-pzmf8ruM6xBr7dnBgzyxpqXuUPYaKahOaz1LrxNkS/Q3Ae5AC+xl6NbxAqXXlzghZBZHmOrM6Y6Y
-ctAkltwlF7SKEsShjVh7QHuxMU0a08/eiu3x3M+07OijMcKFFltByXrpk8w+JNnZpnp3CfgjV1Ax
-gUYCnWwYow42I5wHCcTzLXK0hMZN2DrPM/zCSqe9jRSlJnr70BPE4+zrwbk/xVIDHy2FAQyHoomT
-Tt5jiM68nBQut35Y0qLclLiQrutxt/c0OlSqXAC8VrxW97lGoRWzhOnifE2zbF05W4xuyhg7JTUL
-aqJ7SWDywhjlal0b+NLTpERBgnPW0+Nw99X2Ws72gOL27iER9jgzj7Uu09JaZ3n+hmCjjvZpjNst
-vOWWTbuLrg+/1ltX8WpPauEDEvcunIgTxuMEHweWKCx2KQ9DU/UKdO/3za4Szm2iHYL+ss9AAttm
-gZHq2pkUXFbV+FiJCKrpBms18zH75vax5jSo7FNunrVWY3Chvd8KKnHdaTt/6ealwaA1x17yTlft
-8VBle3nAE+7R0MScC3MJofNCCkA9PGKBgGMYEwfB2QO5j8zUqa8F/EkWKCzGQJ5EZ05HTly1B01E
-z813G5BY++RZ2sxbQS8ZveGPJNabp5kXAeoign6Tlt5+L8i5ZquY9+S+KEUHkmYMRFBxRrHnbl2X
-rVemKnG+oB1yd9+zT+4c43jQ0wWmQRR6mTCkY1q3VG05Y120ZzKOMBe6Vy7I5Vz4ygPB3yY4G0FP
-8RxiMx985YJPXsgRU58EuHj75gygTzejP+W/zKGe78UQN3yOJ1aMQV9hFH+GAfLRsza84WlPLAI/
-9G/5JdcHftEfH+Y3/fHUG7/o8bv98dzzy3e8S+XCvgqB+VUf7sH0yDHpONdbRE8tAg9NWOzcTJ7q
-TuAxe/AJ07c1Rs9okJvl1/0G60qvbdDzz5zO0FuPFQIHNp9y9Bd1CufYVx7dB26mAxwa8GMNrN/U
-oGbNZ3EQ7inLzHy5tRg9AXJrN8cB59cCUBeCiVO7zKM0jU0MamhnRThkg/NMmBOGb6StNeD9tDfA
-7czsAWopDdnGoXUHtA+s/k0vNPkBcxEI13jVd/axp85va3LpwGggXXWw12Gwr/JGAH0b8CPboiZd
-QO1l0mk/UHukud4C+w5uRoNzpCmoW6GbgbMyaQNkga2pQINB18lOXOCJzSWPFOhZcwzdgrsQnne7
-nvjBi+7cP2BbtBeDOW5uOLGf3z94FasKIguOqJl+8ss/6Kumns4cuWbqq5592TN/RNIbn5Qo6qbi
-O4F0P9txxPAwagqPlftztO8cWBzdN/jz3b7GD6JHYP/Zp4ToAMaA74M+EGSft3hEGMuf8EwjnTk/
-nz/P7SLipB/ogQ6xNX0fDqNncMCfHqGLCMM0ZzFa+6lPJYQ5p81vW4HkCvidYf6kb+P/oB965g8K
-C6uR0rdjX1DNKc5pOSTquI8uQ6KXxYaKBn+30/09tK4kMpJPgUIQkbENEPbuezNPPje2Um83SgyX
-GTCJb6MnGVIpgncdQg1qz2bvPfxYD9fewCXDomx9S+HQJuX6W3VAL+v5WZMudRQZk9ZdOk6GIUtC
-PqEb/uwSIrtR7/edzqgEdtpEwq7p2J5OQV+RLrmtTvFwFpf03M/VrRyTZ73qVod7v7Jh2Dwe5J25
-JqFOU2qEu1sP+CRotklediycKfLjeIZzjJQsvKmiGSNQhxuJpKa+hoWUizaE1PuIRGzJqropwgVB
-oo1hr870MZLgnXF5ZIpr6mF0L8aSy2gVnTAuoB4WEd4d5NPVC9TMotYXERKlTcwQ2KiB/C48AEfH
-Qbyq4CN8xTFnTvf/ebOc3isnjD95s0QF0nx9s+y+zMmz782xL0SgEmRpA3x1w1Ff9/74xcxKEPdS
-IEFTz6GgU0+BK/UZ5Gwbl4gZwycxEw+Kqa5QmMkh4OzgzEVPnDAiAOGBFaBW4wkDmj1G4RyElKgj
-NlLCq8zsp085MNh/+R4t1Q8yxoSv8PUpTt7izZwf2BTHZZ3pIZpUIpuLkL1nNL6sYcHqcKm237wp
-T2+RCjgXweXd2Zp7ZM8W6dG5bZsqo0nrJBTx8EC0+CQQdzEGnabTnkzofu1pYkWl4E7XSniECdxy
-vLYavPMcL9LW5SToJFNnos+uqweOHriUZ1ntIYZUonc7ltEQ6oTRtwOHNwez2sVREskHN+bqG3ua
-eaEbJ8XpyO8CeD9QJc8nbLP2C2R3A437ISUNyt5Yd0TbDNcl11/DSsOzdbi/VhCC0KE6v1vqVNkq
-45ZnG6fiV2NwzInxCNth3BwL0+8814jE6+1W1EeWtpWbSZJOJNYXmWRXa7vLnAljE692eHjZ4y5u
-y1u63De0IzKca7As48Z3XshVF+3XiLNz0JIMh/JOpbiNLlMi672uO0wYzOCZjRxcxj3D+gVenGIE
-MvFUGGXuRps2RzMcgWIRolHXpGUP6sMsQt1hspUBnVKUn/WQj2u6j3SXd9Xz0QtEzoM7qTu5y7gR
-q9gNNsrlEMLdikBt9bFvBnfbUIh6voTw7eDsyTmPKUvF0bHqWLbHe3VRHyRZnNeSGKsB73q66Vsk
-taxWYmwz1tYVFG/vOQhlM0gUkyvIab3nv2caJ1udU1F3pDMty7stubTE4OJqm0i0ECfrJIkLtraC
-HwRWKzlqpfhEIqYH09eT9WrOhQyt8YEoyBlnXtAT37WHIQ03TIuEHbnRxZDdLun0iok9PUC79prU
-m5beZzfQUelEXnhzb/pIROKx3F7qCttYIFGh5dXNzFzID7u8vKykA8Uejf7XXz//S4nKvW//ofS/
-QastYw==
-""")
-
-##file distutils-init.py
-DISTUTILS_INIT = convert("""
-eJytV1uL4zYUfvevOE0ottuMW9q3gVDa3aUMXXbLMlDKMBiNrSTqOJKRlMxkf33PkXyRbGe7Dw2E
-UXTu37lpxLFV2oIyifAncxmOL0xLIfcG+gv80x9VW6maw7o/CANSWWBwFtqeWMPlGY6qPjV8A0bB
-C4eKSTgZ5LRgFeyErMEeOBhbN+Ipgeizhjtnhkn7DdyjuNLPoCS0l/ayQTG0djwZC08cLXozeMss
-aG5EzQ0IScpnWtHSTXuxByV/QCmxE7y+eS0uxWeoheaVVfqSJHiU7Mhhi6gULbOHorshkrEnKxpT
-0n3A8Y8SMpuwZx6aoix3ouFlmW8gHRSkeSJ2g7hU+kiHLDaQw3bmRDaTGfTnty7gPm0FHbIBg9U9
-oh1kZzAFLaue2R6htPCtAda2nGlDSUJ4PZBgCJBGVcwKTAMz/vJiLD+Oin5Z5QlvDPdulC6EsiyE
-NFzb7McNTKJzbJqzphx92VKRFY1idenzmq3K0emRcbWBD0ryqc4NZGmKOOOX9Pz5x+/l27tP797c
-f/z0d+4NruGNai8uAM0bfsYaw8itFk8ny41jsfpyO+BWlpqfhcG4yxLdi/0tQqoT4a8Vby382mt8
-p7XSo7aWGdPBc+b6utaBmCQ7rQKQoWtAuthQCiold2KfJIPTT8xwg9blPumc+YDZC/wYGdAyHpJk
-vUbHbHWAp5No6pK/WhhLEWrFjUwtPEv1Agf8YmnsuXUQYkeZoHm8ogP16gt2uHoxcEMdf2C6pmbw
-hUMsWGhanboh4IzzmsIpWs134jVPqD/c74bZHdY69UKKSn/+KfVhxLgUlToemayLMYQOqfEC61bh
-cbhwaqoGUzIyZRFHPmau5juaWqwRn3mpWmoEA5nhzS5gog/5jbcFQqOZvmBasZtwYlG93k5GEiyw
-buHhMWLjDarEGpMGB2LFs5nIJkhp/nUmZneFaRth++lieJtHepIvKgx6PJqIlD9X2j6pG1i9x3pZ
-5bHuCPFiirGHeO7McvoXkz786GaKVzC9DSpnOxJdc4xm6NSVq7lNEnKdVlnpu9BNYoKX2Iq3wvgh
-gGEUM66kK6j4NiyoneuPLSwaCWDxczgaolEWpiMyDVDb7dNuLAbriL8ig8mmeju31oNvQdpnvEPC
-1vAXbWacGRVrGt/uXN/gU0CDDwgooKRrHfTBb1/s9lYZ8ZqOBU0yLvpuP6+K9hLFsvIjeNhBi0KL
-MlOuWRn3FRwx5oHXjl0YImUx0+gLzjGchrgzca026ETmYJzPD+IpuKzNi8AFn048Thd63OdD86M6
-84zE8yQm0VqXdbbgvub2pKVnS76icBGdeTHHXTKspUmr4NYo/furFLKiMdQzFjHJNcdAnMhltBJK
-0/IKX3DVFqvPJ2dLE7bDBkH0l/PJ29074+F0CsGYOxsb7U3myTUncYfXqnLLfa6sJybX4g+hmcjO
-kMRBfA1JellfRRKJcyRpxdS4rIl6FdmQCWjo/o9Qz7yKffoP4JHjOvABcRn4CZIT2RH4jnxmfpVG
-qgLaAvQBNfuO6X0/Ux02nb4FKx3vgP+XnkX0QW9pLy/NsXgdN24dD3LxO2Nwil7Zlc1dqtP3d7/h
-kzp1/+7hGBuY4pk0XD/0Ao/oTe/XGrfyM773aB7iUhgkpy+dwAMalxMP0DrBcsVw/6p25+/hobP9
-GBknrWExDhLJ1bwt1NcCNblaFbMKCyvmX0PeRaQ=
-""")
-
-##file distutils.cfg
-DISTUTILS_CFG = convert("""
-eJxNj00KwkAMhfc9xYNuxe4Ft57AjYiUtDO1wXSmNJnK3N5pdSEEAu8nH6lxHVlRhtDHMPATA4uH
-xJ4EFmGbvfJiicSHFRzUSISMY6hq3GLCRLnIvSTnEefN0FIjw5tF0Hkk9Q5dRunBsVoyFi24aaLg
-9FDOlL0FPGluf4QjcInLlxd6f6rqkgPu/5nHLg0cXCscXoozRrP51DRT3j9QNl99AP53T2Q=
-""")
-
-##file activate_this.py
-ACTIVATE_THIS = convert("""
-eJyNU01v2zAMvetXEB4K21jmDOstQA4dMGCHbeihlyEIDMWmG62yJEiKE//7kXKdpN2KzYBt8euR
-fKSyLPs8wiEo8wh4wqZTGou4V6Hm0wJa1cSiTkJdr8+GsoTRHuCotBayiWqQEYGtMCgfD1KjGYBe
-5a3p0cRKiAe2NtLADikftnDco0ko/SFEVgEZ8aRC5GLux7i3BpSJ6J1H+i7A2CjiHq9z7JRZuuQq
-siwTIvpxJYCeuWaBpwZdhB+yxy/eWz+ZvVSU8C4E9FFZkyxFsvCT/ZzL8gcz9aXVE14Yyp2M+2W0
-y7n5mp0qN+avKXvbsyyzUqjeWR8hjGE+2iCE1W1tQ82hsCZN9UzlJr+/e/iab8WfqsmPI6pWeUPd
-FrMsd4H/55poeO9n54COhUs+sZNEzNtg/wanpjpuqHJaxs76HtZryI/K3H7KJ/KDIhqcbJ7kI4ar
-XL+sMgXnX0D+Te2Iy5xdP8yueSlQB/x/ED2BTAtyE3K4SYUN6AMNfbO63f4lBW3bUJPbTL+mjSxS
-PyRfJkZRgj+VbFv+EzHFi5pKwUEepa4JslMnwkowSRCXI+m5XvEOvtuBrxHdhLalG0JofYBok6qj
-YdN2dEngUlbC4PG60M1WEN0piu7Nq7on0mgyyUw3iV1etLo6r/81biWdQ9MWHFaePWZYaq+nmp+t
-s3az+sj7eA0jfgPfeoN1
-""")
-
-MH_MAGIC = 0xfeedface
-MH_CIGAM = 0xcefaedfe
-MH_MAGIC_64 = 0xfeedfacf
-MH_CIGAM_64 = 0xcffaedfe
-FAT_MAGIC = 0xcafebabe
-BIG_ENDIAN = '>'
-LITTLE_ENDIAN = '<'
-LC_LOAD_DYLIB = 0xc
-maxint = majver == 3 and getattr(sys, 'maxsize') or getattr(sys, 'maxint')
-
-
-class fileview(object):
-    """
-    A proxy for file-like objects that exposes a given view of a file.
-    Modified from macholib.
-    """
-
-    def __init__(self, fileobj, start=0, size=maxint):
-        if isinstance(fileobj, fileview):
-            self._fileobj = fileobj._fileobj
-        else:
-            self._fileobj = fileobj
-        self._start = start
-        self._end = start + size
-        self._pos = 0
-
-    def __repr__(self):
-        return '<fileview [%d, %d] %r>' % (
-            self._start, self._end, self._fileobj)
-
-    def tell(self):
-        return self._pos
-
-    def _checkwindow(self, seekto, op):
-        if not (self._start <= seekto <= self._end):
-            raise IOError("%s to offset %d is outside window [%d, %d]" % (
-                op, seekto, self._start, self._end))
-
-    def seek(self, offset, whence=0):
-        seekto = offset
-        if whence == os.SEEK_SET:
-            seekto += self._start
-        elif whence == os.SEEK_CUR:
-            seekto += self._start + self._pos
-        elif whence == os.SEEK_END:
-            seekto += self._end
-        else:
-            raise IOError("Invalid whence argument to seek: %r" % (whence,))
-        self._checkwindow(seekto, 'seek')
-        self._fileobj.seek(seekto)
-        self._pos = seekto - self._start
-
-    def write(self, bytes):
-        here = self._start + self._pos
-        self._checkwindow(here, 'write')
-        self._checkwindow(here + len(bytes), 'write')
-        self._fileobj.seek(here, os.SEEK_SET)
-        self._fileobj.write(bytes)
-        self._pos += len(bytes)
-
-    def read(self, size=maxint):
-        assert size >= 0
-        here = self._start + self._pos
-        self._checkwindow(here, 'read')
-        size = min(size, self._end - here)
-        self._fileobj.seek(here, os.SEEK_SET)
-        bytes = self._fileobj.read(size)
-        self._pos += len(bytes)
-        return bytes
-
-
-def read_data(file, endian, num=1):
-    """
-    Read a given number of 32-bits unsigned integers from the given file
-    with the given endianness.
-    """
-    res = struct.unpack(endian + 'L' * num, file.read(num * 4))
-    if len(res) == 1:
-        return res[0]
-    return res
-
-
-def mach_o_change(path, what, value):
-    """
-    Replace a given name (what) in any LC_LOAD_DYLIB command found in
-    the given binary with a new name (value), provided it's shorter.
-    """
-
-    def do_macho(file, bits, endian):
-        # Read Mach-O header (the magic number is assumed read by the caller)
-        cputype, cpusubtype, filetype, ncmds, sizeofcmds, flags = read_data(file, endian, 6)
-        # 64-bits header has one more field.
-        if bits == 64:
-            read_data(file, endian)
-        # The header is followed by ncmds commands
-        for n in range(ncmds):
-            where = file.tell()
-            # Read command header
-            cmd, cmdsize = read_data(file, endian, 2)
-            if cmd == LC_LOAD_DYLIB:
-                # The first data field in LC_LOAD_DYLIB commands is the
-                # offset of the name, starting from the beginning of the
-                # command.
-                name_offset = read_data(file, endian)
-                file.seek(where + name_offset, os.SEEK_SET)
-                # Read the NUL terminated string
-                load = file.read(cmdsize - name_offset).decode()
-                load = load[:load.index('\0')]
-                # If the string is what is being replaced, overwrite it.
-                if load == what:
-                    file.seek(where + name_offset, os.SEEK_SET)
-                    file.write(value.encode() + '\0'.encode())
-            # Seek to the next command
-            file.seek(where + cmdsize, os.SEEK_SET)
-
-    def do_file(file, offset=0, size=maxint):
-        file = fileview(file, offset, size)
-        # Read magic number
-        magic = read_data(file, BIG_ENDIAN)
-        if magic == FAT_MAGIC:
-            # Fat binaries contain nfat_arch Mach-O binaries
-            nfat_arch = read_data(file, BIG_ENDIAN)
-            for n in range(nfat_arch):
-                # Read arch header
-                cputype, cpusubtype, offset, size, align = read_data(file, BIG_ENDIAN, 5)
-                do_file(file, offset, size)
-        elif magic == MH_MAGIC:
-            do_macho(file, 32, BIG_ENDIAN)
-        elif magic == MH_CIGAM:
-            do_macho(file, 32, LITTLE_ENDIAN)
-        elif magic == MH_MAGIC_64:
-            do_macho(file, 64, BIG_ENDIAN)
-        elif magic == MH_CIGAM_64:
-            do_macho(file, 64, LITTLE_ENDIAN)
-
-    assert(len(what) >= len(value))
-    do_file(open(path, 'r+b'))
-
-
-if __name__ == '__main__':
-    main()
-
-## TODO:
-## Copy python.exe.manifest
-## Monkeypatch distutils.sysconfig
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/__init__.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/backup/benchmarks.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/backup/benchmarks.py
deleted file mode 100644
index 7a19da0684e4006c25dda36c5736a7e698090900..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/backup/benchmarks.py
+++ /dev/null
@@ -1,493 +0,0 @@
-
-# NOTES: Batch13 (Baseline) Batch14 - With ErrorSens (10, 25, 35)
-
-# Batch 9: No Error Sens. Min : P3
-# Batch 10: No Error Sens + More Runs for Loss1 and Loss2.  Min: P3
-# Batch 11: Error Sens: Skipping 30% elems in each : Min: P3. More runs in Loss1 (4000) and Loss2 (2000)
-# Batch 12: Error Sens: 10, 25, 35, for Loss1, 2, 3, respectively, Min: P3. 1000 Runs for All
-# Batch 13: No Error Sens: Equal Runs (1000) for all. Min: P1
-# Batch 14: Reruning Batch12 with bugFix!
-# Batch 15: MAJOR CHANGE: 3 different skip levels for each Loss1,Loss2,Loss3
-
-# Batch 18: Batch13 (Basline) + ParetoCurve (1500 Runs) - BUGGY IGNORE!!!
-
-# Batch 19: (Basline) + ParetoCurve + 2 runs in Tuning Phase (1500 Runs)
-
-# Batch 20: 3-Skip levels + + 2 runs + 1500 Runs + EnergyBandSize now % of Max (Compare against Batch19
-
-
-# Batch 200: AlgoTuner - 1000 images - 1500 runs (IGNORE)
-# Batch 201: AlgoTuner - 2000 images - 1500 runs
-# Batch 202: AlgoTuner - 2000 images - 500 runs
-# Batch 203: AlgoTuner - 2000 images - 3000 runs
-
-
-#---- CHANGES: i) Reshufled inputs ii) 3K images for tuning
-# Batch 210: 3K images, 1000 runs (1500 resnet), no FP32 used in tuning
-# Batch 211: Same as Batch-210 + uses tensorConvPerfCuda*Half*
-
-
-#batch_id = "batch210"
-#batch_id = "batch211"
-#batch_id = "batch210"
-
-
-batch_id = "batch310"
-
-
-class Benchmark:
-  def __init__(self):
-    self.tuner_binary = ""
-    self.promise_binary = ""
-    self.tuner_accuracy = 0
-    self.promise_accuracy = 0
-    self.num_flags = 0
-    self.num_layers = 0
-    self.autotuner_runs = 0
-    self.error_range_1 = 0
-    self.error_range_2 = 0
-    self.result_dir_1 = ""
-    self.result_dir_2 = ""
-    self.promise_result_dir_1 = ""
-    self.promise_result_dir_2 = ""
-
-    
-
-bench_tuner_data = {}
-
-# FIXIT: Fix Variable Names below
-Alexnet1 = Benchmark()
-Alexnet1.tuner_binary = "alexnet_cifar10_tuner"
-Alexnet1.fp16_binary = "alexnet_half"
-Alexnet1.promise_binary = "alexnet_promise"
-Alexnet1.validation_binary = "alexnet_valid"
-Alexnet1.num_flags = 21
-Alexnet1.num_layers = 6
-Alexnet1.error_range_1 = 10
-Alexnet1.error_range_2 = 13
-Alexnet1.start_promise_range = 1
-Alexnet1.skip_layers = 0
-#Alexnet1.skip_layer_str = "0"
-Alexnet1.skip_layer_str = "5_0"
-
-Alexnet1.base_dir = "../build_tuner/tuner_results/alexnet_cifar10/"
-Alexnet1.result_dir_1 = "../build_tuner/tuner_results/alexnet_cifar10/loss_1/" + batch_id
-Alexnet1.result_dir_2 = "../build_tuner/tuner_results/alexnet_cifar10/loss_2/" + batch_id
-Alexnet1.result_dir_3 = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/" + batch_id
-
-Alexnet1.tensor_desc_file = "tuner_results/alexnet_cifar10/alexnet_tensors.txt"
-Alexnet1.layer_file = "tuner_results/alexnet_cifar10/alexnet_layers.txt"
-Alexnet1.cost_file = "../build_tuner/tuner_results/alexnet_cifar10/op_cost.txt"
-Alexnet1.layer_knobs = "../opentuner/data/alexnet/knobs.txt"
-
-#Alexnet1.loss1_result_file = "tuner_results/alexnet2_cifar10/alexnet_layers.txt"
-Alexnet1.loss1_result_file = "tuner_results/alexnet_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet1.loss2_result_file = "tuner_results/alexnet_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet1.autotuner_runs = 1000
-Alexnet1.tuner_accuracy = 79.9
-#Alexnet1.promise_accuracy = 79.9
-Alexnet1.promise_accuracy = 78.86
-Alexnet1.validation_accuracy = 79.19
-
-bench_tuner_data["alexnet_cifar10"] = Alexnet1
-
-
-Alexnet2 = Benchmark()
-Alexnet2.tuner_binary = "alexnet2_cifar10_tuner"
-Alexnet2.fp16_binary = "alexnet2_half"
-Alexnet2.promise_binary = "alexnet2_promise"
-Alexnet2.validation_binary = "alexnet2_valid"
-Alexnet2.num_flags = 23
-Alexnet2.num_layers = 7
-Alexnet2.error_range_1 = 10
-Alexnet2.error_range_2 = 13
-Alexnet2.start_promise_range = 1
-#Alexnet2.skip_layer_str = "0"
-Alexnet2.skip_layer_str = "6_1_0"
-
-Alexnet2.base_dir = "../build_tuner/tuner_results/alexnet2_cifar10/"
-Alexnet2.result_dir_1 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_1/" + batch_id
-Alexnet2.result_dir_2 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_2/" + batch_id
-Alexnet2.result_dir_3 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_3/" + batch_id
-Alexnet2.tensor_desc_file = "tuner_results/alexnet2_cifar10/alexnet2_tensors.txt"
-Alexnet2.layer_file = "tuner_results/alexnet2_cifar10/alexnet2_layers.txt"
-Alexnet2.cost_file = "../build_tuner/tuner_results/alexnet2_cifar10/op_cost.txt"
-Alexnet2.layer_knobs = "../opentuner/data/alexnet2/knobs.txt"
-#Alexnet2.loss1_result_file = "tuner_results/alexnet2_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-#Alexnet2.loss2_result_file = "tuner_results/alexnet2_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet2.autotuner_runs = 1000
-Alexnet2.tuner_accuracy = 84.19
-#Alexnet2.promise_accuracy = 84.19
-Alexnet2.promise_accuracy = 84.7
-Alexnet2.validation_accuracy = 85.15
-
-bench_tuner_data["alexnet2_cifar10"] = Alexnet2
-
-
-
-Alexnet3 = Benchmark()
-Alexnet3.tuner_binary = "vgg16_cifar10_tuner"
-Alexnet3.fp16_binary = "vgg16_cifar10_half"
-Alexnet3.promise_binary = "./vgg16_cifar10_promise"
-Alexnet3.validation_binary = "vgg16_cifar10_valid"
-Alexnet3.num_flags = 50
-Alexnet3.num_layers = 15
-Alexnet3.error_range_1 = 9
-Alexnet3.error_range_2 = 11
-Alexnet3.start_promise_range = 1
-#Alexnet3.skip_layer_str = "0"
-Alexnet3.skip_layer_str = "14_3_4_1_6"
-
-Alexnet3.base_dir = "../build_tuner/tuner_results/vgg16_cifar10/"
-Alexnet3.result_dir_1 = "../build_tuner/tuner_results/vgg16_cifar10/loss_1/" + batch_id
-Alexnet3.result_dir_2 = "../build_tuner/tuner_results/vgg16_cifar10/loss_2/" + batch_id
-Alexnet3.result_dir_3 = "../build_tuner/tuner_results/vgg16_cifar10/loss_3/" + batch_id
-
-Alexnet3.tensor_desc_file = "tuner_results/vgg16_cifar10/vgg16_tensors.txt"
-Alexnet3.layer_file = "tuner_results/vgg16_cifar10/vgg16_layers.txt"
-Alexnet3.cost_file = "../build_tuner/tuner_results/vgg16_cifar10/op_cost.txt"
-Alexnet3.layer_knobs = "../opentuner/data/vgg16_cifar10/knobs.txt"
-
-Alexnet3.loss1_result_file = "tuner_results/vgg16_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet3.loss2_result_file = "tuner_results/vgg16_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet3.autotuner_runs = 1000
-Alexnet3.tuner_accuracy = 90.19
-#Alexnet3.promise_accuracy = 90.19
-Alexnet3.promise_accuracy = 88.53
-Alexnet3.validation_accuracy = 89.05
-
-bench_tuner_data["vgg16_cifar10"] = Alexnet3
-
-
-
-Alexnet4 = Benchmark()
-Alexnet4.tuner_binary = "resnet18_cifar10_tuner"
-Alexnet4.fp16_binary = "resnet18_half"
-Alexnet4.promise_binary = "resnet18_promise"
-Alexnet4.validation_binary = "resnet18_valid"
-Alexnet4.num_flags = 73
-Alexnet4.num_layers = 22
-Alexnet4.error_range_1 = 7
-Alexnet4.error_range_2 = 9
-Alexnet4.start_promise_range = 1
-#Alexnet4.skip_layer_str = "0"
-Alexnet4.skip_layer_str = "0_1_2_14_15_17_18_21"
-Alexnet4.base_dir = "../build_tuner/tuner_results/resnet18_cifar10/"
-Alexnet4.result_dir_1 = "../build_tuner/tuner_results/resnet18_cifar10/loss_1/" + batch_id
-Alexnet4.result_dir_2 = "../build_tuner/tuner_results/resnet18_cifar10/loss_2/" + batch_id
-Alexnet4.result_dir_3 = "../build_tuner/tuner_results/resnet18_cifar10/loss_3/" + batch_id
-Alexnet4.tensor_desc_file = "tuner_results/resnet18_cifar10/resnet_tensors.txt"
-Alexnet4.layer_file = "tuner_results/resnet18_cifar10/resnet_layers.txt"
-Alexnet4.cost_file = "../build_tuner/tuner_results/resnet18_cifar10/op_cost.txt"
-Alexnet4.layer_knobs = "../opentuner/data/resnet/knobs.txt"
-
-Alexnet4.loss1_result_file = "tuner_results/resnet18_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet4.loss2_result_file = "tuner_results/resnet18_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet4.autotuner_runs = 1500
-Alexnet4.tuner_accuracy = 89.6
-#Alexnet4.promise_accuracy = 89.59  - 1000 images
-Alexnet4.promise_accuracy = 89.5
-Alexnet4.validation_accuracy = 89.65
-
-bench_tuner_data["resnet18_cifar10"] = Alexnet4
-
-
-
-
-
-Alexnet5 = Benchmark()
-Alexnet5.tuner_binary = "vgg16_cifar100_tuner"
-Alexnet5.fp16_binary = "vgg16_cifar100_half"
-Alexnet5.promise_binary = "vgg16_cifar100_promise"
-Alexnet5.validation_binary = "vgg16_cifar100_valid"
-Alexnet5.num_flags = 50
-Alexnet5.num_layers = 15
-Alexnet5.error_range_1 = 9
-Alexnet5.error_range_2 = 11
-Alexnet5.start_promise_range = 1
-Alexnet5.skip_layer_str = "0_1_2_3_4"
-Alexnet5.base_dir = "../build_tuner/tuner_results/vgg16_cifar100/"
-Alexnet5.result_dir_1 = "../build_tuner/tuner_results/vgg16_cifar100/loss_1/" + batch_id
-Alexnet5.result_dir_2 = "../build_tuner/tuner_results/vgg16_cifar100/loss_2/" + batch_id
-Alexnet5.result_dir_3 = "../build_tuner/tuner_results/vgg16_cifar100/loss_3/" + batch_id
-
-Alexnet5.tensor_desc_file = "../build_tuner/tuner_results/vgg16_cifar100/vgg16_tensors.txt"
-Alexnet5.layer_file = "../build_tuner/tuner_results/vgg16_cifar100/vgg16_layers.txt"
-Alexnet5.cost_file = "../build_tuner/tuner_results/vgg16_cifar100/op_cost.txt"
-Alexnet5.layer_knobs = "../opentuner/data/vgg16_cifar100/knobs.txt"
-
-Alexnet5.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet5.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet5.autotuner_runs = 1000
-Alexnet5.tuner_accuracy = 67.95
-#Alexnet5.promise_accuracy = 66.8
-Alexnet5.promise_accuracy = 67.86
-Alexnet5.validation_accuracy = 68.65
-
-bench_tuner_data["vgg16_cifar100"] = Alexnet5
-
-
-
-Alexnet6 = Benchmark()
-Alexnet6.tuner_binary = "lenet_keras"
-Alexnet6.fp16_binary = "lenet_half"
-Alexnet6.promise_binary = "lenet_promise"
-Alexnet6.validation_binary = "lenet_promise"
-
-Alexnet6.num_flags = 14
-Alexnet6.num_layers = 4
-Alexnet6.error_range_1 = 16
-Alexnet6.error_range_2 = 20
-Alexnet6.start_promise_range = 1
-Alexnet6.skip_layer_str = "0"
-
-Alexnet6.base_dir = "../build_tuner/tuner_results/lenet_keras/"
-Alexnet6.result_dir = "../build_tuner/tuner_results/lenet_keras/loss_123/" + batch_id
-Alexnet6.result_dir_1 = "../build_tuner/tuner_results/lenet_keras/loss_1/" + batch_id
-Alexnet6.result_dir_2 = "../build_tuner/tuner_results/lenet_keras/loss_2/" + batch_id
-Alexnet6.result_dir_3 = "../build_tuner/tuner_results/lenet_keras/loss_3/" + batch_id
-
-Alexnet6.tensor_desc_file = "tuner_results/lenet_keras/lenet_tensors.txt"
-Alexnet6.layer_file = "tuner_results/lenet_keras/lenet_layers.txt"
-Alexnet6.cost_file = "../build_tuner/tuner_results/lenet_keras/op_cost.txt"
-Alexnet6.layer_knobs = "../autotuner/data/lenet/knobs.txt"
-
-#Alexnet6.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Alexnet6.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet6.autotuner_runs = 1000
-Alexnet6.tuner_accuracy = 98.9
-Alexnet6.promise_accuracy = 99.7
-Alexnet6.validation_accuracy = 99
-
-bench_tuner_data["lenet_keras"] = Alexnet6
-
-
-
-
-Alexnet7 = Benchmark()
-Alexnet7.tuner_binary = "mobilenet_cifar10"
-Alexnet7.fp16_binary = "mobilenet_half"
-Alexnet7.promise_binary = "mobilenet_promise"
-Alexnet7.validation_binary = "mobilenet_valid"
-Alexnet7.num_flags = 85
-Alexnet7.num_layers = 15
-Alexnet7.error_range_1 = 7
-Alexnet7.error_range_2 = 8
-Alexnet7.start_promise_range = 1
-#Alexnet7.skip_layer_str = "0"
-Alexnet7.skip_layer_str = "1_14_0_6_2"
-Alexnet7.base_dir = "../build_tuner/tuner_results/mobilenet/"
-Alexnet7.result_dir_1 = "../build_tuner/tuner_results/mobilenet/loss_1/" + batch_id
-Alexnet7.result_dir_2 = "../build_tuner/tuner_results/mobilenet/loss_2/" + batch_id
-Alexnet7.result_dir_3 = "../build_tuner/tuner_results/mobilenet/loss_3/" + batch_id
-
-Alexnet7.tensor_desc_file = "tuner_results/mobilenet/mobilenet_ops.txt"
-Alexnet7.layer_file = "tuner_results/mobilenet/mobilenet_layer_comp.txt"
-Alexnet7.cost_file = "../build_tuner/tuner_results/mobilenet/op_cost.txt"
-Alexnet7.layer_knobs = "../opentuner/data/mobilenet/knobs.txt"
-
-#--- Files below needed for VALIDATION experiment
-Alexnet7.loss1_result_file = "tuner_results/mobilenet/loss_1/batch1/promise_tuner/high_confidence/promise_confs.txt"
-Alexnet7.loss2_result_file = "tuner_results/mobilenet/loss_2/batch1/promise_tuner/high_confidence/promise_confs.txt"
-Alexnet7.autotuner_runs = 1000
-Alexnet7.tuner_accuracy = 84.8
-#Alexnet7.promise_accuracy = 84.8
-Alexnet7.promise_accuracy = 83.73
-Alexnet7.validation_accuracy = 84.4
-
-bench_tuner_data["mobilenet_cifar10"] = Alexnet7
-
-
-
-Alexnet8 = Benchmark()
-Alexnet8.tuner_binary = "mobilenet_cifar10_shallow"
-Alexnet8.fp16_binary = "mobilenet_shallow_half"
-Alexnet8.promise_binary = "mobilenet_shallow_promise"
-Alexnet8.validation_binary = "mobilenet_shallow_valid"
-Alexnet8.num_flags = 42
-Alexnet8.num_layers = 8
-Alexnet8.error_range_1 = 10
-Alexnet8.error_range_2 = 12
-Alexnet8.start_promise_range = 1
-#Alexnet8.skip_layer_str = "0"
-Alexnet8.skip_layer_str = "7_0_1"
-Alexnet8.base_dir = "../build_tuner/tuner_results/mobilenet_shallow/"
-Alexnet8.result_dir_1 = "../build_tuner/tuner_results/mobilenet_shallow/loss_1/" + batch_id
-Alexnet8.result_dir_2 = "../build_tuner/tuner_results/mobilenet_shallow/loss_2/" + batch_id
-Alexnet8.result_dir_3 = "../build_tuner/tuner_results/mobilenet_shallow/loss_3/" + batch_id
-
-Alexnet8.tensor_desc_file = "../build_tuner/tuner_results/mobilenet_shallow/mobilenet_shallow_ops.txt"
-Alexnet8.layer_file = "../build_tuner/tuner_results/mobilenet_shallow/mobilenet_shallow_layer_comp.txt"
-Alexnet8.cost_file = "../build_tuner/tuner_results/mobilenet_shallow/op_cost.txt"
-Alexnet8.layer_knobs = "../opentuner/data/mobilenet_shallow/knobs.txt"
-
-Alexnet8.loss1_result_file = "../build_tuner/tuner_results/mobilenet_shallow/loss_1/batch2/promise_tuner/high_confidence/promise_selected_confs.txt"
-Alexnet8.loss2_result_file = "../build_tuner/tuner_results/mobilenet_shallow/loss_2/batch2/promise_tuner/high_confidence/promise_selected_confs.txt"
-
-Alexnet8.autotuner_runs = 1000
-Alexnet8.tuner_accuracy = 87.6
-#Alexnet8.promise_accuracy = 87.59
-Alexnet8.promise_accuracy = 87.76
-Alexnet8.validation_accuracy = 88.5
-
-bench_tuner_data["mobilenet_shallow"] = Alexnet8
-
-
-
-"""
-Alexnet9 = Benchmark()
-Alexnet9.tuner_binary = "fc4_clipped"
-Alexnet9.promise_binary = ""
-Alexnet9.validation_binary = ""
-Alexnet9.num_flags = 12
-Alexnet9.num_layers = 4
-Alexnet9.error_range_1 = 12
-Alexnet9.error_range_2 = 16 
-Alexnet9.start_promise_range = 3
-Alexnet9.skip_layer_str = "0"
-Alexnet9.base_dir = "../build_tuner/tuner_results/fc4/"
-Alexnet9.result_dir_1 = "../build_tuner/tuner_results/fc4/loss1/batch1"
-Alexnet9.result_dir_2 = "../build_tuner/tuner_results/fc4/loss2/batch1"
-Alexnet9.tensor_desc_file = ""
-Alexnet9.layer_file = ""
-
-Alexnet9.loss1_result_file = ""
-Alexnet9.loss2_result_file = ""
-
-Alexnet9.autotuner_runs = 1000
-Alexnet9.tuner_accuracy = 93.8
-Alexnet9.promise_accuracy = 0.0
-Alexnet9.validation_accuracy = 0.0
-
-bench_tuner_data["fc4"] = Alexnet9
-
-
-
-
-Pipeline1 = Benchmark()
-Pipeline1.tuner_binary = "pipeline_GEOM"
-Pipeline1.promise_binary = "pipeline_GEOM_promise"
-Pipeline1.validation_binary = "pipeline_GEOM_valid"
-Pipeline1.num_flags = 9
-Pipeline1.num_layers = 4
-Pipeline1.error_range_1 = 10
-Pipeline1.error_range_2 = 15
-Pipeline1.start_promise_range = 2
-Pipeline1.skip_layer_str = "1_2"
-Pipeline1.result_dir_1 = "tuner_results/pipeline_GEOM/loss_30/batch1"
-Pipeline1.result_dir_2 = "tuner_results/pipeline_GEOM/loss_20/batch1"
-Pipeline1.tensor_desc_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_tensors.txt"
-Pipeline1.layer_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline1.autotuner_runs = 300
-Pipeline1.tuner_accuracy = 95
-Pipeline1.promise_accuracy = 95
-Pipeline1.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEOM"] = Pipeline1
-
-
-Pipeline2 = Benchmark()
-Pipeline2.tuner_binary = "pipeline_GEMO"
-Pipeline2.promise_binary = "pipeline_GEMO_promise"
-Pipeline2.validation_binary = "pipeline_GEMO_valid"
-Pipeline2.num_flags = 9
-Pipeline2.num_layers = 4
-Pipeline2.error_range_1 = 10
-Pipeline2.error_range_2 = 15
-Pipeline2.start_promise_range = 2
-Pipeline2.skip_layer_str = "1_3"
-Pipeline2.result_dir_1 = "tuner_results/pipeline_GEMO/loss_30/batch1"
-Pipeline2.result_dir_2 = "tuner_results/pipeline_GEMO/loss_20/batch1"
-Pipeline2.tensor_desc_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_tensors.txt"
-Pipeline2.layer_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline2.autotuner_runs = 300
-Pipeline2.tuner_accuracy = 95
-Pipeline2.promise_accuracy = 95
-Pipeline2.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEMO"] = Pipeline2
-
-
-
-
-Pipeline3 = Benchmark()
-Pipeline3.tuner_binary = "pipeline_GSME"
-Pipeline3.promise_binary = "pipeline_GSME_promise"
-Pipeline3.validation_binary = "pipeline_GSME_valid"
-Pipeline3.num_flags = 9
-Pipeline3.num_layers = 4
-Pipeline3.error_range_1 = 10
-Pipeline3.error_range_2 = 15
-Pipeline3.start_promise_range = 2
-Pipeline3.skip_layer_str = "1_3"
-Pipeline3.result_dir_1 = "tuner_results/pipeline_GSME/loss_30/batch1"
-Pipeline3.result_dir_2 = "tuner_results/pipeline_GSME/loss_20/batch1"
-Pipeline3.tensor_desc_file = "tuner_results/pipeline_GSME/pipeline_GSME_tensors.txt"
-Pipeline3.layer_file = "tuner_results/pipeline_GSME/pipeline_GSME_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline3.autotuner_runs = 300
-Pipeline3.tuner_accuracy = 95
-Pipeline3.promise_accuracy = 95
-Pipeline3.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSME"] = Pipeline3
-
-
-Pipeline4 = Benchmark()
-Pipeline4.tuner_binary = "pipeline_GEO"
-Pipeline4.promise_binary = "pipeline_GEO_promise"
-Pipeline4.validation_binary = "pipeline_GEO_valid"
-Pipeline4.num_flags = 7
-Pipeline4.num_layers = 3
-Pipeline4.error_range_1 = 10
-Pipeline4.error_range_2 = 15
-Pipeline4.start_promise_range = 2
-Pipeline4.skip_layer_str = "1_2"
-Pipeline4.result_dir_1 = "tuner_results/pipeline_GEO/loss_30/batch1"
-Pipeline4.result_dir_2 = "tuner_results/pipeline_GEO/loss_20/batch1"
-Pipeline4.tensor_desc_file = "tuner_results/pipeline_GEO/pipeline_GEO_tensors.txt"
-Pipeline4.layer_file = "tuner_results/pipeline_GEO/pipeline_GEO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline4.autotuner_runs = 300
-Pipeline4.tuner_accuracy = 95
-Pipeline4.promise_accuracy = 95
-Pipeline4.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEO"] = Pipeline4
-
-
-Pipeline5 = Benchmark()
-Pipeline5.tuner_binary = "pipeline_GSM"
-Pipeline5.promise_binary = "pipeline_GSM_promise"
-Pipeline5.validation_binary = "pipeline_GSM_valid"
-Pipeline5.num_flags = 6
-Pipeline5.num_layers = 3
-Pipeline5.error_range_1 = 10
-Pipeline5.error_range_2 = 15
-Pipeline5.start_promise_range = 2
-Pipeline5.skip_layer_str = "1_1"
-Pipeline5.result_dir_1 = "tuner_results/pipeline_GSM/loss_30/batch1"
-Pipeline5.result_dir_2 = "tuner_results/pipeline_GSM/loss_20/batch1"
-Pipeline5.tensor_desc_file = "tuner_results/pipeline_GSM/pipeline_GSM_tensors.txt"
-Pipeline5.layer_file = "tuner_results/pipeline_GSM/pipeline_GSM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline5.autotuner_runs = 300
-Pipeline5.tuner_accuracy = 95
-Pipeline5.promise_accuracy = 95
-Pipeline5.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSM"] = Pipeline5
-
-"""
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/benchmarks.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/benchmarks.py
deleted file mode 100644
index 0662ddaa76e359c3d3b1d911d17d01394aaab654..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/benchmarks.py
+++ /dev/null
@@ -1,599 +0,0 @@
-
-# NOTES: Batch13 (Baseline) Batch14 - With ErrorSens (10, 25, 35)
-
-# Batch 9: No Error Sens. Min : P3
-# Batch 10: No Error Sens + More Runs for Loss1 and Loss2.  Min: P3
-# Batch 11: Error Sens: Skipping 30% elems in each : Min: P3. More runs in Loss1 (4000) and Loss2 (2000)
-# Batch 12: Error Sens: 10, 25, 35, for Loss1, 2, 3, respectively, Min: P3. 1000 Runs for All
-# Batch 13: No Error Sens: Equal Runs (1000) for all. Min: P1
-# Batch 14: Reruning Batch12 with bugFix!
-# Batch 15: MAJOR CHANGE: 3 different skip levels for each Loss1,Loss2,Loss3
-
-# Batch 18: Batch13 (Basline) + ParetoCurve (1500 Runs) - BUGGY IGNORE!!!
-
-# Batch 19: (Basline) + ParetoCurve + 2 runs in Tuning Phase (1500 Runs)
-
-# Batch 20: 3-Skip levels + + 2 runs + 1500 Runs + EnergyBandSize now % of Max (Compare against Batch19
-
-
-# Batch 200: AlgoTuner - 1000 images - 1500 runs (IGNORE)
-# Batch 201: AlgoTuner - 2000 images - 1500 runs
-# Batch 202: AlgoTuner - 2000 images - 500 runs
-# Batch 203: AlgoTuner - 2000 images - 3000 runs
-
-
-#---- CHANGES: i) Reshufled inputs ii) 3K images for tuning
-# Batch 210: 3K images, 1000 runs (1500 resnet), no FP32 used in tuning
-# Batch 211: Same as Batch-210 + uses tensorConvPerfCuda*Half*
-
-
-#batch_id = "batch210"
-#batch_id = "batch211"
-#batch_id = "batch210"
-
-# NOTE: Testing new devtuner script
-#batch_id = "batch311"
-
-# NOTE: batch with 3K runs each - new devtuner script
-#batch_id = "batch312"
-
-
-# NOTE: Trying out piped execution
-#batch_id = "batch313"
-
-# NOTE: Trying out piped execution with 3K each - to measure time is the goal
-#batch_id = "batch314"
-
-# NOTE: Trying out VGG16 Imagenet with new error slack approach
-# batch_id = "batch_315"
-
-# NOTE: Using Batch with 2K images in VGG16_imagenet
-#-- batch_id = "batch316"
-
-# Running all non imagenet DNNs for Yifan - ENDED UP TESTING RUN
-#-- batch_id = "batch321"
-
-
-# Running all non imagenet DNNs for Yifan - Long Running
-#--- batch_id = "batch322"
-
-
-# Re-Running VGG16_imagenet after issues with python setup 
-#-- batch_id = "batch323"
-
-
-# Re-Running all CIFAR-10 benchmarks after using AUTOMATIC KNOBS 
-#-- batch_id = "batch324"
-
-# After Fixing Yasmin's code first batch of runs on CIFAR-10 DNNs
-# NOTE: First batch with 33% sampling - 2K runs for each threshold
-#-- batch_id = "batch325"
-
-
-# After Fixing Yasmin's code second batch of runs on CIFAR-10 DNNs
-# NOTE: Second batch with 33% sampling - 5K runs for each threshold
-# NOTE: First batch with dumping CPU runtime configs
-#-- batch_id = "batch327"
-
-
-# IMP: Increased SAMPLING Knobs ---- Adding interpolation-based Knobs - 8K iterations
-#---- batch_id = "batch328"
-
-
-# IMP: Increased SAMPLING Knobs ---- Adding interpolation-based Knobs --  12K 
-#-- batch_id = "batch329"
-
-# IMP: Increased SAMPLING Knobs ---- Adding interpolation-based Knobs --  12K - NEW: 5K images calibration set 
-#-- batch_id = "batch330"
-
-
-# IMP: Increased SAMPLING Knobs -- 20K iterations - NEW: 5K images calibration set -- fixed bugs 
-#-- batch_id = "batch331"
-
-# testing install-time tuner 
-#batch_id = "batch340"
-
-# First run of install time tuner
-batch_id = "batch341"
-
-# Install Timer Tuner with FP32 SEEDING
-batch_id = "batch342"
-
-# Install Timer Tuner with FP32 SEEDING
-# FIRST time reducing sampling knobs 239 above
-# Fixed bugs --- added FP32 to search space
-batch_id = "batch343"
-
-
-# testing pareto-only validation
-batch_id = "batch344"
-
-
-# First Install-time tuning run with different iterations per DNN benchmark
-batch_id = "batch345"
-
-
-# First Install-time tuning run with 10K iterations per DNN benchmark
-batch_id = "batch346"
-
-
-
-class Benchmark:
-  def __init__(self):
-    self.tuner_binary = ""
-    self.promise_binary = ""
-    self.tuner_accuracy = 0
-    self.promise_accuracy = 0
-    self.num_flags = 0
-    self.num_layers = 0
-    self.autotuner_runs = 0
-    self.error_range_1 = 0
-    self.error_range_2 = 0
-    self.result_dir_1 = ""
-    self.result_dir_2 = ""
-    self.promise_result_dir_1 = ""
-    self.promise_result_dir_2 = ""
-
-    
-
-bench_tuner_data = {}
-
-
-LeNet = Benchmark()
-LeNet.tuner_binary = "lenet_keras"
-LeNet.fp16_binary = "lenet_half"
-LeNet.promise_binary = "lenet_promise"
-LeNet.piped_binary = "lenet_piped"
-LeNet.validation_binary = "lenet_promise"
-
-LeNet.num_flags = 14
-LeNet.num_layers = 4
-LeNet.error_range_1 = 16
-LeNet.error_range_2 = 20
-LeNet.start_promise_range = 1
-LeNet.skip_layer_str = "0"
-
-LeNet.base_dir = "tuner_results/lenet_keras/"
-
-LeNet.tensor_desc_file = "autotuner/data/lenet/lenet_tensors.txt"
-LeNet.layer_file = "autotuner/data/lenet/lenet_layers.txt"
-LeNet.cost_file = "autotuner/data/lenet/op_cost.txt"
-LeNet.layer_knobs = "autotuner/data/lenet/dev_knobs.txt"
-
-LeNet.autotuner_runs = 2000
-LeNet.tuner_accuracy = 98.9
-LeNet.promise_accuracy = 99.7
-LeNet.validation_accuracy = 99
-
-bench_tuner_data["lenet_keras"] = LeNet
-
-
-
-
-
-
-# FIXIT: Fix Variable Names below
-Alexnet = Benchmark()
-Alexnet.tuner_binary = "alexnet_cifar10_tuner"
-Alexnet.fp16_binary = "alexnet_half"
-Alexnet.promise_binary = "alexnet_promise"
-Alexnet.piped_binary = "alexnet_piped"
-Alexnet.validation_binary = "alexnet_valid"
-Alexnet.num_flags = 21
-Alexnet.num_layers = 6
-Alexnet.error_range_1 = 10
-Alexnet.error_range_2 = 13
-Alexnet.start_promise_range = 1
-Alexnet.skip_layers = 0
-Alexnet.skip_layer_str = "5_0"
-
-Alexnet.base_dir = "tuner_results/alexnet_cifar10/"
-
-Alexnet.tensor_desc_file = "autotuner/data/alexnet/alexnet_tensors.txt"
-Alexnet.layer_file = "autotuner/data/alexnet/alexnet_layers.txt"
-Alexnet.cost_file = "autotuner/data/alexnet/op_cost.txt"
-Alexnet.layer_knobs = "autotuner/data/alexnet/dev_knobs.txt"
-
-Alexnet.autotuner_runs = 4000
-Alexnet.tuner_accuracy = 79.9
-Alexnet.promise_accuracy = 78.86
-Alexnet.validation_accuracy = 79.19
-
-bench_tuner_data["alexnet_cifar10"] = Alexnet
-
-
-Alexnet2 = Benchmark()
-Alexnet2.tuner_binary = "alexnet2_cifar10_tuner"
-Alexnet2.fp16_binary = "alexnet2_half"
-Alexnet2.promise_binary = "alexnet2_promise"
-Alexnet2.piped_binary = "alexnet2_piped"
-Alexnet2.validation_binary = "alexnet2_valid"
-Alexnet2.num_flags = 23
-Alexnet2.num_layers = 7
-Alexnet2.error_range_1 = 10
-Alexnet2.error_range_2 = 13
-Alexnet2.start_promise_range = 1
-Alexnet2.skip_layer_str = "6_1_0"
-
-Alexnet2.base_dir = "tuner_results/alexnet2_cifar10/"
-
-Alexnet2.tensor_desc_file = "autotuner/data/alexnet2/alexnet2_tensors.txt"
-Alexnet2.layer_file = "autotuner/data/alexnet2/alexnet2_layers.txt"
-Alexnet2.cost_file = "autotuner/data/alexnet2/op_cost.txt"
-Alexnet2.layer_knobs = "autotuner/data/alexnet2/dev_knobs.txt"
-Alexnet2.autotuner_runs = 4000
-Alexnet2.tuner_accuracy = 84.19
-Alexnet2.promise_accuracy = 84.7
-Alexnet2.validation_accuracy = 85.15
-
-bench_tuner_data["alexnet2_cifar10"] = Alexnet2
-
-
-
-VGG16_10 = Benchmark()
-VGG16_10.tuner_binary = "vgg16_cifar10_tuner"
-VGG16_10.fp16_binary = "vgg16_cifar10_half"
-VGG16_10.promise_binary = "./vgg16_cifar10_promise"
-VGG16_10.piped_binary = "./vgg16_cifar10_piped"
-VGG16_10.validation_binary = "vgg16_cifar10_valid"
-VGG16_10.num_flags = 50
-VGG16_10.num_layers = 15
-VGG16_10.error_range_1 = 9
-VGG16_10.error_range_2 = 11
-VGG16_10.start_promise_range = 1
-VGG16_10.skip_layer_str = "14_3_4_1_6"
-
-VGG16_10.base_dir = "tuner_results/vgg16_cifar10/"
-
-VGG16_10.tensor_desc_file = "autotuner/data/vgg16_cifar10/vgg16_tensors.txt"
-VGG16_10.layer_file = "autotuner/data/vgg16_cifar10/vgg16_layers.txt"
-VGG16_10.cost_file = "autotuner/data/vgg16_cifar10/op_cost.txt"
-VGG16_10.layer_knobs = "autotuner/data/vgg16_cifar10/dev_knobs.txt"
-
-VGG16_10.autotuner_runs = 8000
-VGG16_10.tuner_accuracy = 90.19
-
-VGG16_10.promise_accuracy = 88.53
-VGG16_10.validation_accuracy = 89.05
-
-bench_tuner_data["vgg16_cifar10"] = VGG16_10
-
-
-
-
-VGG16_100 = Benchmark()
-VGG16_100.tuner_binary = "vgg16_cifar100_tuner"
-VGG16_100.fp16_binary = "vgg16_cifar100_half"
-VGG16_100.promise_binary = "vgg16_cifar100_promise"
-VGG16_100.piped_binary = "vgg16_cifar100_piped"
-VGG16_100.validation_binary = "vgg16_cifar100_valid"
-VGG16_100.num_flags = 50
-VGG16_100.num_layers = 15
-VGG16_100.error_range_1 = 9
-VGG16_100.error_range_2 = 11
-VGG16_100.start_promise_range = 1
-VGG16_100.skip_layer_str = "0_1_2_3_4"
-
-VGG16_100.base_dir = "tuner_results/vgg16_cifar100/"
-
-VGG16_100.tensor_desc_file = "autotuner/data/vgg16_cifar100/vgg16_tensors.txt"
-VGG16_100.layer_file = "autotuner/data/vgg16_cifar100/vgg16_layers.txt"
-VGG16_100.cost_file = "autotuner/data/vgg16_cifar100/op_cost.txt"
-VGG16_100.layer_knobs = "autotuner/data/vgg16_cifar100/dev_knobs.txt"
-
-VGG16_100.autotuner_runs = 5000
-VGG16_100.tuner_accuracy = 67.95
-
-VGG16_100.promise_accuracy = 67.86
-VGG16_100.validation_accuracy = 68.65
-
-bench_tuner_data["vgg16_cifar100"] = VGG16_100
-
-
-
-
-VGG16_imagenet = Benchmark()
-VGG16_imagenet.tuner_binary = ""
-VGG16_imagenet.fp16_binary = ""
-VGG16_imagenet.promise_binary = "vgg16_imagenet_promise"
-VGG16_imagenet.piped_binary = "vgg16_imagenet_piped"
-VGG16_imagenet.validation_binary = "vgg16_imagenet_promise"
-VGG16_imagenet.num_flags = 53
-VGG16_imagenet.num_layers = 16
-
-VGG16_imagenet.base_dir = "tuner_results/vgg16_imagenet/"
-VGG16_imagenet.tensor_desc_file = "autotuner/data/vgg16_imagenet/vgg16_tensors.txt"
-VGG16_imagenet.layer_file = "autotuner/data/vgg16_imagenet/vgg16_layers.txt"
-VGG16_imagenet.cost_file = "autotuner/data/vgg16_imagenet/op_cost.txt"
-VGG16_imagenet.layer_knobs = "autotuner/data/vgg16_imagenet/dev_knobs.txt"
-
-VGG16_imagenet.autotuner_runs = 5000
-VGG16_imagenet.tuner_accuracy = 0.0
-VGG16_imagenet.promise_accuracy = 69.62
-VGG16_imagenet.validation_accuracy = 69.62
-
-#-- bench_tuner_data["vgg16_imagenet"] = VGG16_imagenet
-
-
-
-
-ResNet = Benchmark()
-ResNet.tuner_binary = "resnet18_cifar10_tuner"
-ResNet.fp16_binary = "resnet18_half"
-ResNet.promise_binary = "resnet18_promise"
-ResNet.piped_binary = "resnet18_piped"
-ResNet.validation_binary = "resnet18_valid"
-ResNet.num_flags = 73
-ResNet.num_layers = 22
-ResNet.error_range_1 = 7
-ResNet.error_range_2 = 9
-ResNet.start_promise_range = 1
-
-ResNet.skip_layer_str = "0_1_2_14_15_17_18_21"
-ResNet.base_dir = "tuner_results/resnet18_cifar10/"
-
-ResNet.tensor_desc_file = "autotuner/data/resnet/resnet_tensors.txt"
-ResNet.layer_file = "autotuner/data/resnet/resnet_layers.txt"
-ResNet.cost_file = "autotuner/data/resnet/op_cost.txt"
-ResNet.layer_knobs = "autotuner/data/resnet/dev_knobs.txt"
-
-ResNet.autotuner_runs = 8000
-ResNet.tuner_accuracy = 89.6
-
-ResNet.promise_accuracy = 89.5
-ResNet.validation_accuracy = 89.65
-
-bench_tuner_data["resnet18_cifar10"] = ResNet
-
-
-
-
-
-
-ResNet50 = Benchmark()
-ResNet50.tuner_binary = ""
-ResNet50.fp16_binary = ""
-ResNet50.promise_binary = "resnet50_imagenet_promise"
-ResNet50.piped_binary = "resnet50_imagenet_piped"
-ResNet50.validation_binary = "resnet50_valid"
-ResNet50.num_flags = 1 # FIXIT
-ResNet50.num_layers = 54
-
-ResNet50.base_dir = "tuner_results/resnet50_imagenet/"
-
-ResNet50.tensor_desc_file = "autotuner/data/resnet50_imagenet/resnet50_tensors.txt"
-ResNet50.layer_file = "autotuner/data/resnet50_imagenet/resnet50_layers.txt"
-ResNet50.cost_file = "autotuner/data/resnet50_imagenet/op_cost.txt"
-ResNet50.layer_knobs = "autotuner/data/resnet50_imagenet/dev_knobs.txt"
-
-ResNet50.autotuner_runs = 5000
-ResNet50.tuner_accuracy = 89.6
-
-ResNet50.promise_accuracy = 77
-ResNet50.validation_accuracy = 20 # FIXIT
-
-#--- bench_tuner_data["resnet50_imagenet"] = ResNet50
-
-
-
-
-
-
-
-
-
-MobileNet = Benchmark()
-MobileNet.tuner_binary = "mobilenet_cifar10"
-MobileNet.fp16_binary = "mobilenet_half"
-MobileNet.promise_binary = "mobilenet_promise"
-MobileNet.piped_binary = "mobilenet_piped"
-MobileNet.validation_binary = "mobilenet_valid"
-MobileNet.num_flags = 85
-MobileNet.num_layers = 15
-MobileNet.error_range_1 = 7
-MobileNet.error_range_2 = 8
-MobileNet.start_promise_range = 1
-
-MobileNet.skip_layer_str = "1_14_0_6_2"
-MobileNet.base_dir = "tuner_results/mobilenet/"
-
-MobileNet.tensor_desc_file = "autotuner/data/mobilenet/mobilenet_ops.txt"
-MobileNet.layer_file = "autotuner/data/mobilenet/mobilenet_layer_comp.txt"
-MobileNet.cost_file = "autotuner/data/mobilenet/op_cost.txt"
-MobileNet.layer_knobs = "autotuner/data/mobilenet/dev_knobs.txt"
-
-MobileNet.autotuner_runs = 8000
-MobileNet.tuner_accuracy = 84.8
-
-MobileNet.promise_accuracy = 83.73
-MobileNet.validation_accuracy = 84.4
-
-bench_tuner_data["mobilenet_cifar10"] = MobileNet
-
-
-
-MobileNet_SH = Benchmark()
-MobileNet_SH.tuner_binary = "mobilenet_cifar10_shallow"
-MobileNet_SH.fp16_binary = "mobilenet_shallow_half"
-MobileNet_SH.promise_binary = "mobilenet_shallow_promise"
-MobileNet_SH.piped_binary = "mobilenet_shallow_piped"
-MobileNet_SH.validation_binary = "mobilenet_shallow_valid"
-MobileNet_SH.num_flags = 42
-MobileNet_SH.num_layers = 8
-MobileNet_SH.error_range_1 = 10
-MobileNet_SH.error_range_2 = 12
-MobileNet_SH.start_promise_range = 1
-
-MobileNet_SH.skip_layer_str = "7_0_1"
-MobileNet_SH.base_dir = "tuner_results/mobilenet_shallow/"
-
-MobileNet_SH.tensor_desc_file = "autotuner/data/mobilenet_shallow/mobilenet_shallow_ops.txt"
-MobileNet_SH.layer_file = "autotuner/data/mobilenet_shallow/mobilenet_shallow_layer_comp.txt"
-MobileNet_SH.cost_file = "autotuner/data/mobilenet_shallow/op_cost.txt"
-MobileNet_SH.layer_knobs = "autotuner/data/mobilenet_shallow/dev_knobs.txt"
-
-
-MobileNet_SH.autotuner_runs = 1000
-MobileNet_SH.tuner_accuracy = 87.6
-
-MobileNet_SH.promise_accuracy = 87.76
-MobileNet_SH.validation_accuracy = 88.5
-
-#-- bench_tuner_data["mobilenet_shallow"] = MobileNet_SH
-
-
-
-"""
-Alexnet9 = Benchmark()
-FC4.tuner_binary = "fc4_clipped"
-FC4.promise_binary = ""
-FC4.validation_binary = ""
-FC4.num_flags = 12
-FC4.num_layers = 4
-FC4.error_range_1 = 12
-FC4.error_range_2 = 16 
-FC4.start_promise_range = 3
-FC4.skip_layer_str = "0"
-FC4.base_dir = "../build_tuner/tuner_results/fc4/"
-FC4.result_dir_1 = "../build_tuner/tuner_results/fc4/loss1/batch1"
-FC4.result_dir_2 = "../build_tuner/tuner_results/fc4/loss2/batch1"
-FC4.tensor_desc_file = ""
-FC4.layer_file = ""
-
-FC4.loss1_result_file = ""
-FC4.loss2_result_file = ""
-
-FC4.autotuner_runs = 1000
-FC4.tuner_accuracy = 93.8
-FC4.promise_accuracy = 0.0
-FC4.validation_accuracy = 0.0
-
-bench_tuner_data["fc4"] = FC4
-
-
-
-
-Pipeline1 = Benchmark()
-Pipeline1.tuner_binary = "pipeline_GEOM"
-Pipeline1.promise_binary = "pipeline_GEOM_promise"
-Pipeline1.validation_binary = "pipeline_GEOM_valid"
-Pipeline1.num_flags = 9
-Pipeline1.num_layers = 4
-Pipeline1.error_range_1 = 10
-Pipeline1.error_range_2 = 15
-Pipeline1.start_promise_range = 2
-Pipeline1.skip_layer_str = "1_2"
-Pipeline1.result_dir_1 = "tuner_results/pipeline_GEOM/loss_30/batch1"
-Pipeline1.result_dir_2 = "tuner_results/pipeline_GEOM/loss_20/batch1"
-Pipeline1.tensor_desc_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_tensors.txt"
-Pipeline1.layer_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline1.autotuner_runs = 300
-Pipeline1.tuner_accuracy = 95
-Pipeline1.promise_accuracy = 95
-Pipeline1.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEOM"] = Pipeline1
-
-
-Pipeline2 = Benchmark()
-Pipeline2.tuner_binary = "pipeline_GEMO"
-Pipeline2.promise_binary = "pipeline_GEMO_promise"
-Pipeline2.validation_binary = "pipeline_GEMO_valid"
-Pipeline2.num_flags = 9
-Pipeline2.num_layers = 4
-Pipeline2.error_range_1 = 10
-Pipeline2.error_range_2 = 15
-Pipeline2.start_promise_range = 2
-Pipeline2.skip_layer_str = "1_3"
-Pipeline2.result_dir_1 = "tuner_results/pipeline_GEMO/loss_30/batch1"
-Pipeline2.result_dir_2 = "tuner_results/pipeline_GEMO/loss_20/batch1"
-Pipeline2.tensor_desc_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_tensors.txt"
-Pipeline2.layer_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline2.autotuner_runs = 300
-Pipeline2.tuner_accuracy = 95
-Pipeline2.promise_accuracy = 95
-Pipeline2.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEMO"] = Pipeline2
-
-
-
-
-Pipeline3 = Benchmark()
-Pipeline3.tuner_binary = "pipeline_GSME"
-Pipeline3.promise_binary = "pipeline_GSME_promise"
-Pipeline3.validation_binary = "pipeline_GSME_valid"
-Pipeline3.num_flags = 9
-Pipeline3.num_layers = 4
-Pipeline3.error_range_1 = 10
-Pipeline3.error_range_2 = 15
-Pipeline3.start_promise_range = 2
-Pipeline3.skip_layer_str = "1_3"
-Pipeline3.result_dir_1 = "tuner_results/pipeline_GSME/loss_30/batch1"
-Pipeline3.result_dir_2 = "tuner_results/pipeline_GSME/loss_20/batch1"
-Pipeline3.tensor_desc_file = "tuner_results/pipeline_GSME/pipeline_GSME_tensors.txt"
-Pipeline3.layer_file = "tuner_results/pipeline_GSME/pipeline_GSME_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline3.autotuner_runs = 300
-Pipeline3.tuner_accuracy = 95
-Pipeline3.promise_accuracy = 95
-Pipeline3.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSME"] = Pipeline3
-
-
-Pipeline4 = Benchmark()
-Pipeline4.tuner_binary = "pipeline_GEO"
-Pipeline4.promise_binary = "pipeline_GEO_promise"
-Pipeline4.validation_binary = "pipeline_GEO_valid"
-Pipeline4.num_flags = 7
-Pipeline4.num_layers = 3
-Pipeline4.error_range_1 = 10
-Pipeline4.error_range_2 = 15
-Pipeline4.start_promise_range = 2
-Pipeline4.skip_layer_str = "1_2"
-Pipeline4.result_dir_1 = "tuner_results/pipeline_GEO/loss_30/batch1"
-Pipeline4.result_dir_2 = "tuner_results/pipeline_GEO/loss_20/batch1"
-Pipeline4.tensor_desc_file = "tuner_results/pipeline_GEO/pipeline_GEO_tensors.txt"
-Pipeline4.layer_file = "tuner_results/pipeline_GEO/pipeline_GEO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline4.autotuner_runs = 300
-Pipeline4.tuner_accuracy = 95
-Pipeline4.promise_accuracy = 95
-Pipeline4.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEO"] = Pipeline4
-
-
-Pipeline5 = Benchmark()
-Pipeline5.tuner_binary = "pipeline_GSM"
-Pipeline5.promise_binary = "pipeline_GSM_promise"
-Pipeline5.validation_binary = "pipeline_GSM_valid"
-Pipeline5.num_flags = 6
-Pipeline5.num_layers = 3
-Pipeline5.error_range_1 = 10
-Pipeline5.error_range_2 = 15
-Pipeline5.start_promise_range = 2
-Pipeline5.skip_layer_str = "1_1"
-Pipeline5.result_dir_1 = "tuner_results/pipeline_GSM/loss_30/batch1"
-Pipeline5.result_dir_2 = "tuner_results/pipeline_GSM/loss_20/batch1"
-Pipeline5.tensor_desc_file = "tuner_results/pipeline_GSM/pipeline_GSM_tensors.txt"
-Pipeline5.layer_file = "tuner_results/pipeline_GSM/pipeline_GSM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline5.autotuner_runs = 300
-Pipeline5.tuner_accuracy = 95
-Pipeline5.promise_accuracy = 95
-Pipeline5.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSM"] = Pipeline5
-
-"""
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/buildRtConfig.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/buildRtConfig.py
deleted file mode 100644
index 5a6a9e0f03a27cac1190a4bf1e93dfd48810ffd9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/buildRtConfig.py
+++ /dev/null
@@ -1,583 +0,0 @@
-
-
-import os
-import sys
-import utils
-from benchmarks import bench_tuner_data
-from swing_selection import loadLayerDesc
-from benchmarks import batch_id
-
-
-op_mapping = {}
-op_mapping["conv"] = "conv"
-op_mapping["depthwise_conv"] = "group_conv"
-op_mapping["dense"] = "mul"
-op_mapping["batchnorm"] = "batchnorm"
-op_mapping["pool"] = "pool_max"
-op_mapping["pool_mean"] = "pool_mean"
-op_mapping["activation"] = "relu"
-op_mapping["tanh"] = "tanh"
-op_mapping["add"] = "add"
-
-
-approx_map = {}
-
-
-def initializeApproxMap(knobs_file_path):
-
-  f = open(knobs_file_path, "r")
-
-  for x in f:
-    toks = x.split("\t")
-    approx_type = toks[0].split(",")[0]
-    knob_id = toks[0].split(",")[1]
-    approx_str = approx_type + " " + knob_id
-    approx_map[knob_id] = approx_str
-
-
-  print (approx_map)
-
-    
-
-
-
-
-class Config:
-  def __init__(self):
-    self.avg_accuracy = 0
-    self.avg_loss = 0
-    self.speedup = 1
-    self.fname = ""
-    self.flags = []
-
-
-
-
-def isLayer(layer_comp):
-  if layer_comp[0] == "dense" or layer_comp[0] == "conv":
-    return True
-  else:
-    return False
-
-
-  
-def getOpMapping(op_name):
-
-  if op_name not in op_mapping:
-    print ("ERROR: OP not found!! = ", op_name, "\n")
-    sys.exit(0)
-
-  return op_mapping[op_name]
-
-
-
-def getApproxMapping(flag, layer_comp):
-
-  flag_str = str(flag)
-  if flag_str not in approx_map:
-    print ("ERROR: OP not found!! = ", flag_str, "\n")
-    sys.exit(0)
-
-  if "dense" in layer_comp and flag > 7:
-    if flag == 12:
-      return "fp16 1"
-    else:
-      return "fp32 1"
-    
-
-  return approx_map[flag_str]
-
-
-
-def skipFile(fname):
-
-  skip_files = {}
-  skip_files["confidence_summary.txt"] = 1
-  skip_files["promise_confs.txt"] = 1
-
-  if "accuracy" in fname: # *_accuracy files should be skipped
-    return True
-
-  if "norms" in fname: # *_accuracy files should be skipped
-    return True
-
-  if ".#" in fname: # *_accuracy files should be skipped
-    return True
-
-  #if "_promise" in fname: # *_accuracy files should be skipped
-  #  return True
-
-  if not fname[-1].isdigit():
-    return True
-  
-  if fname in skip_files:
-    return True
-  else:
-    return False
-    
-
-
-def parseTopLine(x):
-
-  toks = x.split()
-
-  speedup = 1.0
-  accuracy = 0.0
-  for tok in toks:
-    if "avg_accuracy" in tok:
-      avg_accuracy = float(tok.split("=")[1])
-    if "speedup" in tok:
-      speedup = float(tok.split("=")[1])
-    
-
-  return avg_accuracy, speedup
-
-
-
-def loadConfigData(result_dir, baseline_accuracy, sub_dir = "high_confidence"):
-
-  config_arr = []
-  
-  #result_dir += "/promise_tuner/high_confidence/"
-  #result_dir += "/algo_tuner/high_confidence/"
-  result_dir += "/algo_tuner/" + sub_dir + "/"
-  file_names = os.listdir(result_dir)
-
-  
-  for fname in file_names:
-    if not skipFile(fname):
-
-      fpath = result_dir + fname  
-      config = Config()
-      f = open(fpath, "r")
-
-      it = 0
-      for x in f:
-        if x.strip == "":
-            continue       
-        if it == 0:
-          avg_accuracy, speedup = parseTopLine(x)
-          config.avg_accuracy = avg_accuracy
-          config.avg_loss = baseline_accuracy - avg_accuracy 
-          config.speedup = speedup
-          config.fname = fname
-          #print ("acc = " + str(avg_accuracy) + "\n")
-        else:
-          flag = int(x.strip())
-          config.flags.append(flag)
-        it += 1
-  
-      config_arr.append(config)
-        
-
-  return config_arr      
-      
-
-
-
-def loadConfigsFromDir(result_dir, baseline_accuracy):
-
-  config_arr = []
-  file_names = os.listdir(result_dir)
-  
-  for fname in file_names:
-    if not skipFile(fname):
-
-      fpath = result_dir + '/' + fname
-      config = Config()
-      f = open(fpath, "r")
-
-      it = 0
-      for x in f:
-        if x.strip == "":
-            continue       
-        if it == 0:
-          avg_accuracy, speedup = parseTopLine(x)
-          config.avg_accuracy = avg_accuracy
-          config.avg_loss = baseline_accuracy - avg_accuracy 
-          config.speedup = speedup
-          config.fname = fname
-          #print ("acc = " + str(avg_accuracy) + "\n")
-        else:
-          flag = int(x.strip())
-          config.flags.append(flag)
-        it += 1
-  
-      config_arr.append(config)
-        
-
-  return config_arr      
-      
-
-
-
-
-
-
-def loadPromiseConfigs(result_dir, baseline_accuracy, sub_dir = "promise_test"):
-
-  config_arr = []  
-  result_dir += "/algo_tuner/" + sub_dir + "/"
-  file_names = os.listdir(result_dir)
-  
-  for fname in file_names:
-    if "_promise" in fname:
-
-      fpath = result_dir + fname  
-      config = Config()
-      f = open(fpath, "r")
-
-      it = 0
-      for x in f:
-        if x.strip == "":
-            continue
-          
-        if it == 0:
-          avg_accuracy, speedup = parseTopLine(x)
-          config.avg_accuracy = avg_accuracy
-          config.avg_loss = baseline_accuracy - avg_accuracy 
-          config.speedup = speedup
-          config.fname = fname
-          #print ("acc = " + str(avg_accuracy) + "\n")
-        else:
-          flag = int(x.strip())
-          config.flags.append(flag)
-        
-        it += 1
-  
-      config_arr.append(config)
-        
-
-  return config_arr      
-      
-
-
-
-
-def getFP(flag):
-
-  if flag < 11:
-    return "fp16"
-  else:
-    return "fp32"
-
-
-
-def getHardwareTarget(flag):
-
-  if flag <= 7:
-    return "promise"
-  else:
-    return "gpu"
-
-  return "gpu"
-
-
-def handlePromiseConfs(flag, layer_comp):
-
-  approx_tech = getApproxMapping(flag, layer_comp)      
-  config_str = ""
-  if flag <= 7:
-    config_str += approx_tech + " "  
-
-  return config_str
-
-
-def handleGPUApproxs(flag, layer_comp):
-
-  approx_tech = getApproxMapping(flag, layer_comp)
-  config_str = ""
-  if flag > 7:
-    utils.debug_print ("flag = " +  str(flag))
-    config_str += getOpMapping(layer_comp[0]) + " " + approx_tech + " "
-    for op in layer_comp[1:]:
-      utils.debug_print (layer_comp[1:])
-      utils.debug_print (op)
-      
-      op_name = getOpMapping(op)
-      config_str += str(op_name) + " " + getFP(flag) + " 1 "
-
-  return config_str
-
-
-def generateBaselineConfig(layer_comp):
-
-  config_str = ""
-  config_str += "gpu "
-  for op in layer_comp:
-    op_name = getOpMapping(op)
-    config_str += str(op_name) + " fp16 1 "
-
-  return config_str
-
-
-
-
-
-def buildConfigStr(config, layer_desc, hardware_target):
-
-  index = 1
-  it = 0
-  flags = config.flags
-  config_str = ""
-  
-  for layer_comp in layer_desc:
-    config_str += str(index) + " "
-    #-- print ("laye_comp = ", layer_comp)
-    
-    if isLayer(layer_comp):
-      flag = flags[it]
-      it += 1
-
-      utils.debug_print ("flag* = " + str(flag)) 
-      # Add Target Target - GPU, PROMISE
-      #config_str += getHardwareTarget(flag) + " "
-
-      config_str += hardware_target + " "
-
-      utils.debug_print ("config_str = " +  str(config_str))
-      
-      config_str += handlePromiseConfs(flag, layer_comp)
-      config_str += handleGPUApproxs(flag, layer_comp)
-      
-    else: # if a non-Layer Operation
-      config_str += generateBaselineConfig(layer_comp)
-      
-    
-    config_str += "\n"    
-    index += 1
-
-
-  config_str += str(index) + " " + hardware_target + " softmax fp32 1\n"  
-    
-  return config_str
-
-
-
-# Adjusts for expected loss on unseen dataset
-def adjustDevTimeLoss(loss):
-
-  # Adjusts for negative and low loss values
-  if loss < 0.3:
-    loss += 0.4
-  else:
-    loss = loss * 1.33  # 33% extra error for unseen data
-
-  if loss < 0.0:
-    loss = 0.1
-    
-  return loss
-    
-
-
-def adjustConfigLosses(configurations):
-
-  for config in configurations:
-    config.avg_loss = adjustDevTimeLoss(config.avg_loss)
-
-  
-
-
-def dumpConfig(layer_desc, config_arrs, result_dir):
-
-  f = open(result_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "w+")
-  it = 1
-  for config in config_arrs:
-    f.write("+++++\n")
-    f.write("conf" + str(it) + " " + str(config.speedup) + " 0 " + \
-            str(config.avg_accuracy) + " " + str(config.avg_loss) + "\n")
-
-    config_str = buildConfigStr(config, layer_desc)
-
-    f.write(config_str)
-    f.write("-----\n")
-          
-    it += 1
-
-
-def dumpBaseLineConfig(conf_id, perf_improv, energy_red, \
-                       baseline_acc, hardware_target, bench_layer_composition, f_out):
-  
-    f_out.write("+++++\n")
-    f_out.write("conf" + str(conf_id) + " " + str(perf_improv) + " " + str(energy_red) + " " + \
-               str(baseline_acc) + " " + str(0) + "\n")
-
-    config_str = genFP32Config(bench_layer_composition, hardware_target)
- 
-    f_out.write(config_str)    
-    f_out.write("-----\n")
-
-    
-
-def genFP32Config(layer_comp, hardware_target):
-
-  it = 1
-  config_str = ""
-  for layer in layer_comp:
-    config_str += str(it) + " "
-    config_str += hardware_target + " "
-  
-    for op in layer:
-      op_name = getOpMapping(op)
-      config_str += str(op_name) + " fp32 1 "
-
-    config_str += "\n"
-
-    it += 1
-    
-  config_str += str(it) + " " + hardware_target + " softmax fp32 1\n" 
-  
-  return config_str
-
-
-
-# ***** Exported Interface --- Generates file used by HPVM RT controller ******/
-def dumpDevConfigsToRTFile(configurations, config_out_path,  \
-                           bench_layer_composition, baseline_acc, hardware_target):
-
-  f = open(config_out_path, "w+")
-  
-  dumpBaseLineConfig(1, 1.0, 0, baseline_acc, hardware_target, bench_layer_composition, f) 
-  
-  it = 2
-  for config in configurations:
-    f.write("+++++\n")
-    f.write("conf" + str(it) + " " + str(config.speedup) + " 0 " +  \
-            str(config.avg_accuracy) + " " + str(config.avg_loss) + "\n")
-
-    config_str = buildConfigStr(config, bench_layer_composition, hardware_target)
-
-    f.write(config_str)    
-    f.write("-----\n")
-          
-    it += 1
-
-    
-
-def prependBaseline(Bench):
-
-  f1 = open(Bench.base_dir + "/tuner_confs_base.txt", "r")
-  baseline_str = f1.read()
-  f1.close()
-
-  f2 = open(Bench.base_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "r")
-  config_str = f2.read()
-  f2.close()
-
-  f3 = open(Bench.base_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "w+")
-  f3.write(baseline_str)
-  f3.write(config_str)
-  f3.close()
-
-
-    
-def generateConf(Bench):
-
-  layer_desc = loadLayerDesc(Bench.layer_file)
-
-  utils.debug_print ("layer_desc = ", layer_desc)
-  
-  #config_arr1 = loadConfigData(Bench.result_dir_1, Bench.promise_accuracy)
-  #config_arr2 = loadConfigData(Bench.result_dir_2, Bench.promise_accuracy)
-  #config_arr3 = loadConfigData(Bench.result_dir_3, Bench.promise_accuracy)
-
-  result_dir1 = Bench.result_dir_1 + "/algo_tuner/pareto/"
-  result_dir2 = Bench.result_dir_2 + "/algo_tuner/pareto/"
-  result_dir3 = Bench.result_dir_3 + "/algo_tuner/pareto/"
- 
-  config_arr1 = loadConfigsFromDir(result_dir1, Bench.promise_accuracy)
-  config_arr2 = loadConfigsFromDir(result_dir2, Bench.promise_accuracy)
-  config_arr3 = loadConfigsFromDir(result_dir3, Bench.promise_accuracy)
-
-  config_arrs = config_arr1 + config_arr2 + config_arr3
-  
-  dumpConfig(layer_desc, config_arrs, Bench.base_dir) 
-
-  prependBaseline(Bench)
-   
-  
-
-
-def dumpBaselineConfs(Bench):
-
-  layer_desc = loadLayerDesc(Bench.layer_file)
-
-  #-- print("layer_desc = ", layer_desc)
-  
-  f = open(Bench.base_dir + "/tuner_confs_base.txt", "w+")
- 
-  f.write("+++++\n")
-  f.write("conf" + str(1) + " " + str(1) + " 0 " + str(Bench.promise_accuracy) + " " + str(0) + "\n")
-
-  config = Config()
-  flags = []
-  for i in range(Bench.num_layers):
-    flags.append(11)
-    
-  config.flags = flags
-  config_str = buildConfigStr(config, layer_desc)
-
-  f.write(config_str)  
-  f.write("-----\n")
-          
-
-  f.close()
-  
-  
-  #f.write("+++++\n")
-  #f.write("conf" + str(2) + " " + str(1.5) + " 0 " + str(Bench.promise_accuracy) + " " + str(0) + "\n")
-
-  #config = Config()
-  #flags = []
-  #for i in range(Bench.num_layers):
-  #  flags.append(12)
-    
-  #config.flags = flags
-  #config_str = buildConfigStr(config, layer_desc)
-
-  #f.write(config_str)    
-  #f.write("-----\n")
-
-
-
-  
-
-
-if __name__ == "__main__":
-
-  Bench = bench_tuner_data["alexnet_cifar10"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["alexnet2_cifar10"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["vgg16_cifar10"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["vgg16_cifar100"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["resnet18_cifar10"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["lenet_keras"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-  
-  Bench = bench_tuner_data["mobilenet_cifar10"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-    
-  Bench = bench_tuner_data["mobilenet_shallow"]
-  generateConf(Bench)
-  dumpBaselineConfs(Bench)
-
-
-
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compareResults.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compareResults.py
deleted file mode 100644
index 6ee7466242d47299d5aa7622f15aef7d35832a2a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compareResults.py
+++ /dev/null
@@ -1,66 +0,0 @@
-
-
-
-import os
-from benchmarks import bench_tuner_data
-from buildRtConfig import loadConfigData
-from buildRtConfig import loadConfigsFromDir
-
-
-
-def compareBench(batch_ids, Bench):
-
-  losses = ["1", "2", "3"]
-  for loss in losses:
-    print ("\n Loss = ", loss, " % \n")
-    for id in batch_ids:
-      result_dir = Bench.base_dir + "/loss_" + loss + "/batch" + id 
-      #config_arr = loadConfigData(result_dir, Bench.promise_accuracy, "high_confidence")
-
-      #result_dir += "/algo_tuner/high_confidence/"
-      result_dir += "/promise_tuner3/high_confidence/"
-      
-      config_arr = loadConfigsFromDir(result_dir, Bench.promise_accuracy)
-      
-      count = len(config_arr)
-      if len(config_arr) > 0:
-        max_speedup = max(config.speedup for config in config_arr)
-      else:
-        max_speedup = 1.0  
-      print ("Bench = ", Bench.promise_binary, " BatchID = ", id, " Loss = ", loss, " Count = ", count, " MaxS = ", max_speedup)
-  
-
-
-
-if __name__ == "__main__":
-
-
-  batch_ids = []
-
-  #batch_ids.append("13") # No Error Sens - baseline
-  #batch_ids.append("14") # Ops Skipped 10% for Loss1, 25% Loss2, 40% Loss3
-  #batch_ids.append("15") # 3 differnet levels for each of Loss1, Loss2, Loss3
-  #batch_ids.append("19") # Baseline + Pareto
-  #batch_ids.append("20") # Batch18 + Pareto
-  
-  #batch_ids.append("101") # Algo-specific tuning
-
-  #batch_ids.append("201") # Algo-specific tuning
-
-  #---- batch_ids.append("202") # Algo-specific tuning
-  #batch_ids.append("212") # Algo-specific tuning
-  #batch_ids.append("211") # Algo-specific tuning
-
-
-  batch_ids.append("220") # Algo-specific tuning
- 
-  
-  compareBench(batch_ids, bench_tuner_data["lenet_keras"])
-  compareBench(batch_ids, bench_tuner_data["alexnet_cifar10"])
-  compareBench(batch_ids, bench_tuner_data["mobilenet_cifar10"])
- 
-  compareBench(batch_ids, bench_tuner_data["alexnet2_cifar10"])
-  compareBench(batch_ids, bench_tuner_data["vgg16_cifar10"])
-  compareBench(batch_ids, bench_tuner_data["vgg16_cifar100"])    
-  compareBench(batch_ids, bench_tuner_data["resnet18_cifar10"])
-  compareBench(batch_ids, bench_tuner_data["mobilenet_shallow"])
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compute_confs.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compute_confs.py
deleted file mode 100644
index f82c09095ceac24d8ee4a765f1d63be987b625a9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/compute_confs.py
+++ /dev/null
@@ -1,56 +0,0 @@
-
-
-from swing_selection import compute_swing_selection
-from swing_selection2 import compute_swing_selection2
-  
-
-def computeBenchSwings(Bench):
-
-  dir_prefix = "../build_tuner/"
-  
-  loss_confs = []
-  conf_ranks = []
-  # Swing selection for 1% and 2% results
-  #Bench = bench_tuner_data[bench_name]
-  tuned_result_dir = dir_prefix + Bench.result_dir_1 + "/high_confidence/" 
-  layer_file = Bench.layer_file
-  layer_swings, file_names = compute_swing_selection(tuned_result_dir, layer_file)
-  loss_confs.append(layer_swings)
-  conf_ranks.append(file_names)
-  print (file_names)
-  
-  tuned_result_dir = dir_prefix + Bench.result_dir_2 + "/high_confidence/" 
-  layer_swings, file_names = compute_swing_selection(tuned_result_dir, layer_file)
-  loss_confs.append(layer_swings)
-  conf_ranks.append(file_names)
-  print (file_names)
-  
-
-  return loss_confs, conf_ranks
-
-
-
-
-
-def computePSNRBenchSwings(Bench):
-
-  loss_confs = []
-  conf_ranks = []
-  # Swing selection for 1% and 2% results
-  #Bench = bench_tuner_data[bench_name]
-  tuned_result_dir = Bench.result_dir_1 + "/high_confidence/" 
-  layer_file = Bench.layer_file
-  layer_swings, file_names = compute_swing_selection2(tuned_result_dir, layer_file)
-  loss_confs.append(layer_swings)
-  conf_ranks.append(file_names)
-  print (file_names)
-  
-  tuned_result_dir = Bench.result_dir_2 + "/high_confidence/" 
-  layer_swings, file_names = compute_swing_selection2(tuned_result_dir, layer_file)
-  loss_confs.append(layer_swings)
-  conf_ranks.append(file_names)
-  print (file_names)
-  
-
-  return loss_confs, conf_ranks
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/error_sensitivity.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/error_sensitivity.py
deleted file mode 100644
index 186477164240694ebae63f019b7824dc1e12c83b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/error_sensitivity.py
+++ /dev/null
@@ -1,378 +0,0 @@
-
-
-import subprocess
-import os
-import operator
-from benchmarks import bench_tuner_data
-from swing_selection import loadLayerDesc
-import math
-
-
-def constructTunerFile(num_flags, tensor_id, error_level, default_error):
-
-  f = open("opentuner_flags", "w+")
-
-  for i in range(num_flags):
-    if i == tensor_id:
-      f.write(str(error_level) + "\n")
-    else:
-      f.write(str(default_error) + "\n")
-
-  f.close()
-    
-
-
-def runAndTestError(binary_name, gold_acc):
-
-  num_runs = 10
-
-  binary_name = "./" + binary_name
-  FNULL = open(os.devnull, 'wb')
-  p = subprocess.Popen([binary_name, str(num_runs)], stdout = FNULL)
-  p.wait()
-
-  f = open("run_accuracies.txt")
-
-  total_err = 0.0
-  for x in f:
-    acc = float(x.strip())    
-    total_err += (gold_acc - acc)
-
-  avg_err = total_err / num_runs
-
-  return avg_err
-    
-
-
-def roundDecimal(val):
-
-  new_val = int(val * 10000)
-  new_val = float(new_val) / 10000
-
-  return new_val
-
-
-
-
-def test_sensitivity(Bench):
-
-  tensor_errors = []
-  
-  error_levels = [6, 9, 12, 15]
-  num_flags = Bench.num_flags
-
-  for tensor_id in range(num_flags):
-    total_error = 0
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 0)
-      error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-      #print (tensor_id, error_level, error)
-      total_error += error
-
-    avg_error = total_error / len(error_levels)
-
-    tensor_errors.append([tensor_id, avg_error])
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/tensor_errors_multiple.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i) +  "\t" + str(tensor_errors[i][1]) + "\n")
-
-  f.close()
-
-  f_name = Bench.base_dir + "/tensor_errors_ranked_1000.txt"  
-  f2 = open(f_name, "w+")
-  tensor_errors.sort(key=operator.itemgetter(1))
-
-
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-
-    f2.write(str(tensor_errors[i][0]) +  "\t" + str(tensor_errors[i][1]) + "\n")
-    
-
-  f2.close()
-
-
-
-def test_sensitivity2(Bench):
-
-  num_flags = Bench.num_flags
-
-  constructTunerFile(num_flags, 0, 6, 6)
-  error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
-  ref_acc = Bench.tuner_accuracy - error
-  print ("*** Gold accuracy = ", Bench.tuner_accuracy, "  Ref accuracy = ", ref_acc, " *** \n\n")
-  
-  
-  tensor_errors = []
-  
-  error_levels = [6, 9, 12, 15]
-
-  for tensor_id in range(num_flags):
-    total_error = 0
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 6)
-      error = runAndTestError(Bench.tuner_binary, ref_acc)
-      print (tensor_id, error_level, error)
-      total_error += error
-
-    avg_error = total_error / len(error_levels)
-
-    tensor_errors.append([tensor_id, avg_error])
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/tensor_composite_errors.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i) +  "\t" + str(tensor_errors[i][1]) + "\n")
-
-  f.close()
-
-  f_name = Bench.base_dir + "/tensor_composite_errors_ranked.txt"  
-  f2 = open(f_name, "w+")
-  tensor_errors.sort(key=operator.itemgetter(1))
-
-
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-
-    f2.write(str(tensor_errors[i][0]) +  "\t" + str(tensor_errors[i][1]) + "\n")
-    
-
-  f2.close()
-
-
-
-def test_sensitivity3(Bench):
-
-  tensor_errors = []
-  
-  error_levels = [2, 5, 8, 11, 14, 17]
-  num_flags = Bench.num_flags
-
-  for tensor_id in range(num_flags):
-    total_error = 0
-    errors = []
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 0)
-      error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-      print (tensor_id, error_level, error)
-      errors.append(error)
-      
-    tensor_errors.append([tensor_id, errors])
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/tensor_errors_multiple.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i))
-    for j in range(len(tensor_errors[i][1])):
-      val = roundDecimal(tensor_errors[i][1][j])
-      f.write("\t" + str(val) )
-    f.write("\n")
-      
-  f.close()
-
-
-
-
-
-def test_sensitivity4(Bench):
-
-  num_flags = Bench.num_flags
-
-  constructTunerFile(num_flags, 0, 5, 5)
-  error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
-  ref_acc = Bench.tuner_accuracy - error
-  print ("*** Gold accuracy = ", Bench.tuner_accuracy, "  Ref accuracy = ", ref_acc, " *** \n\n")
-  
-  
-  tensor_errors = []  
-  error_levels = [4, 8, 11, 14, 16, 19]
-
-  for tensor_id in range(num_flags):
-    errors = []
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 5)
-      error = runAndTestError(Bench.tuner_binary, ref_acc)
-      print (tensor_id, error_level, error)
-      errors.append(error)
-
-    tensor_errors.append([tensor_id, errors])
-
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/composite_errors.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i))
-    for j in range(len(tensor_errors[i][1])):
-      val = roundDecimal(tensor_errors[i][1][j])
-      f.write("\t" + str(val) )
-    f.write("\n")
-      
-  f.close()
-
-  
-
-  
-
-def readTensorErrs(result_dir):
-
-  tensor_errs = []
-  f = open(result_dir + "/tensor_errors.txt")
-  
-  for x in f:
-    err = float(x.split()[1])
-    tensor_errs.append(err)
-    
-  return tensor_errs
-
-
-
-def readTensorErrs2(result_dir):
-
-  tensor_errs = []
-  f = open(result_dir + "/tensor_errors_multiple.txt")
-  
-  for x in f:
-    toks = x.split()
-    total_err = 0.0
-    for tok in toks[2:-1]:
-      err = float(tok)
-      total_err += err
-
-    avg_err = total_err / len(toks[2:-1])  
-    tensor_errs.append(avg_err)
-    
-  return tensor_errs
-
-
-def isSkipLayer(layer):
-
-  if "dense" in layer or "conv" in layer:
-    return False
-  else:
-    return True
-  
-
-def readLayerCosts(cost_file):
-  
-  f = open(cost_file)
-  layer_costs = []
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  return layer_costs
-  
-
-
-disable_skipping = False
-
-def select_skip_layers(Bench, percent_to_skip):
-
-  if disable_skipping:
-    return "0"
-  
-  result_dir = Bench.base_dir
-  layer_file = Bench.layer_file
-
-  tensor_errs = readTensorErrs2(result_dir)
-  layer_costs = readLayerCosts(Bench.cost_file)
-  layer_desc = loadLayerDesc(layer_file)
-
-  it = 0
-  index = 0
-  layer_errs = []
-  for layer in layer_desc:
-    layer_len = len(layer)
-    avg_err = tensor_errs[index]
-    index += layer_len
- 
-    if isSkipLayer(layer):
-      continue
-
-    cost = (math.sqrt(layer_costs[it])) / 100;
-    ERR_IMPACT = avg_err / cost
-    #print ("layer, ", it, " avg_err = ", avg_err, " cost = ", cost, " err_impact = ", ERR_IMPACT)
-
-    layer_errs.append((ERR_IMPACT, it))
-    it += 1
-
-  layer_errs.sort(key=operator.itemgetter(0), reverse=True)
-
-  to_skip = len(layer_errs)
-  to_skip = math.ceil((percent_to_skip / 100.0) * to_skip)
-
-  skip_str = ""
-  it = 0
-  for err in layer_errs:
-    if it >= to_skip:
-      break
-
-    skip_str += str(err[1])
-    if it < to_skip - 1:
-      skip_str += "_"
-      
-    it += 1
-    
-  return skip_str
-
-
-
-
-
-
-if __name__ == "__main__":
-
-
-  AlexNet = bench_tuner_data["alexnet_cifar10"]
-  skip_str = select_skip_layers(AlexNet, 10)
-  print ("AlexNet skip_str = ", skip_str)
-
-
-  AlexNet2 = bench_tuner_data["alexnet2_cifar10"]
-  skip_str = select_skip_layers(AlexNet2, 15)
-  print ("AlexNet2 skip_str = ", skip_str)
-
-
-  VGG16 = bench_tuner_data["vgg16_cifar10"]
-  skip_str = select_skip_layers(VGG16, 15)
-  print ("VGG16 skip_str = ", skip_str)
-
-
-  VGG16_100 = bench_tuner_data["vgg16_cifar100"]
-  skip_str = select_skip_layers(VGG16_100, 15)
-  print ("VGG16_100 skip_str = ", skip_str)
-
-  
-  ResNet = bench_tuner_data["resnet18_cifar10"]
-  skip_str = select_skip_layers(ResNet, 10)
-  print ("ResNet skip_str = ", skip_str)
-
-
-  MobileNet = bench_tuner_data["mobilenet_cifar10"]
-  skip_str = select_skip_layers(MobileNet, 15)
-  print ("MobileNet skip_str = ", skip_str)
-
-
-  MobileNet_SH = bench_tuner_data["mobilenet_shallow"]
-  skip_str = select_skip_layers(MobileNet_SH, 15)
-  print ("MobileNet_SH skip_str = ", skip_str)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/genPlots.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/genPlots.py
deleted file mode 100644
index df05ddc52be66a0073a76093b77f1de328706635..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/genPlots.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-
-import matplotlib.pyplot as plt
-import seaborn
-import numpy as np
-
-
-
-
-
-def genScatterPlot(accuracy_losses, speedups, file_path):
-
-    plt.scatter(accuracy_losses, speedups)
-    plt.xlabel("accuracy_loss")
-    plt.ylabel("speedup")
-    plt.savefig(file_path)
-    plt.close()
-
-
-
-def genScatterPlotFromConfigs(configurations, file_path):
-
-    accuracy_losses = []
-    speedups = []
-
-    for conf in configurations:
-        accuracy_losses.append(conf.avg_loss)
-        speedups.append(conf.speedup)
-
-    genScatterPlot(accuracy_losses, speedups, file_path)        
-    
-
-    
-if __name__ == "__main__":
-
-  x = np.array([1, 2, 3])
-  y = np.array([1, 2, 3])
-
-  print ("type = ", type(plt.scatter(x, y)))
-
-  plt.savefig("output.png")
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/global_paths.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/global_paths.py
deleted file mode 100644
index d93e96ef7cdca95239c625a6018fb4b2adb1ba45..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/global_paths.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-import os
-import sys
-
-
-
-if "LLVM_SRC_ROOT" not in os.environ:
-  print ("ERROR: LLVM_SRC_ROOT not set --- set $LLVM_SRC_ROOT to top of LLVM source tree ")
-  sys.exit(-1)
-
-opentuner_src_dir = os.environ["LLVM_SRC_ROOT"] + "/projects/hpvm-tensor-rt/autotuner/opentuner/autotuner/"
-tensorRT_dir = os.environ["LLVM_SRC_ROOT"] + "/projects/hpvm-tensor-rt/"
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/knob_pruning.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/knob_pruning.py
deleted file mode 100644
index dfcab4f36bf425615debad880a0e2a828867d7ba..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/knob_pruning.py
+++ /dev/null
@@ -1,86 +0,0 @@
-
-
-import utils
-import subprocess
-
-
-def createPromiseFile(l_knob, layer_ind, num_layers):
-
-    f = open("promise_flags", "w+")
-
-    for i in range(num_layers):
-      if i == layer_ind:  
-        f.write(str(l_knob) + "\n")
-      else:
-        f.write("11\n")
-
-    f.close()
-    
-
-
-def runBinary(binary_path):
-
-    run_cmd = "./" + binary_path
-    print (run_cmd)
-
-    p = subprocess.Popen(run_cmd, shell=True)
-    p.wait()
-    
-    return utils.readAccuracy("final_accuracy")
-
-
-
-    
-    
-def getPrunedKnobs(binary_path, layer_file, global_knobs_file, \
-                   baseline_acc, acc_slack):
-
-
-  knobs = utils.getInstallAndDevKnobs(layer_file, \
-                                      global_knobs_file)
-
-  pruned_knobs = []
-  num_layers = len(knobs)
-  layer_ind = 0
-  for layer_knobs in knobs:
-      pruned_layer_knobs = []
-      for l_knob in layer_knobs:
-          createPromiseFile(l_knob, layer_ind, num_layers)
-          accuracy = runBinary(binary_path)
-          acc_loss = baseline_acc - accuracy
-          if acc_loss <= acc_slack:
-              pruned_layer_knobs.append(l_knob)
-              print ("\n + l_knob = ", l_knob, " - layer_ind = ", layer_ind)
-              print ("- acc_loss = ", acc_loss, " **** SELECTED *** ")
-          else:
-              print ("\n -- l_knob = ", l_knob, " - layer_ind = ", layer_ind)
-              print ("- acc_loss = ", acc_loss, " --- REJECTED --- ")
-
-      pruned_knobs.append(pruned_layer_knobs)
-          
-      layer_ind += 1
-      
-  
-  print ("*** knobs = ", knobs)
-
-  return pruned_knobs
-
-
-
-if __name__ == "__main__":
-
-    
-  #pruned_knobs = getPrunedKnobs("alexnet2_promise",  "../autotuner/data/alexnet2/alexnet2_layers.txt", \
-  #                              "../autotuner/data/global_knobs.txt", 84.5, 3)
-
-  pruned_knobs = getPrunedKnobs("lenet_promise",  "../autotuner/data/lenet/lenet_layers.txt", \
-                                "../autotuner/data/global_knobs.txt", 99.7, 1)
-
-
-    
-  print ("*** pruned_knobs = ", pruned_knobs)
-  
-
-  utils.dumpKnobsFile(pruned_knobs, "install_knobs.txt")
-  
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/main_driver.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/main_driver.py
deleted file mode 100644
index c4a5e0fac038fbddee0025a3e0b75b8005d3be3e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/main_driver.py
+++ /dev/null
@@ -1,69 +0,0 @@
-
-import os
-import sys
-import subprocess
-import shutil
-
-
-from benchmarks import bench_tuner_data, batch_id
-from utils import createResultDirs
-from run_devtime_tuner import DevTimeTuner
-from run_install_tuner import InstallTimeTuner
-
-  
-  
-  
-# Invoke Dev-time Autotuner
-def runDevTimeBenchs():
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]
-  benchTuner = DevTimeTuner(Bench)
-  benchTuner.runDevTuner()
-
-  Bench = bench_tuner_data["resnet18_cifar10"]
-  benchTuner = DevTimeTuner(Bench)
-  benchTuner.runDevTuner()
-
-  Bench = bench_tuner_data["alexnet_cifar10"]
-  benchTuner = DevTimeTuner(Bench)
-  benchTuner.runDevTuner()
-
-
-  #for bench_id in bench_tuner_data:
-  #  Bench = bench_tuner_data[bench_id]
-  #  benchTuner = DevTimeTuner(Bench)
-  #  benchTuner.runDevTuner()
-
-
-
-
-
-  
-# Invoke Dev-time Autotuner
-def runInstallTimeBenchs():
-
-  Bench = bench_tuner_data["alexnet_cifar10"]
-  benchTuner = InstallTimeTuner(Bench)
-  benchTuner.runDevTuner()
-
-
-  Bench = bench_tuner_data["alexnet2_cifar10"]
-  benchTuner = InstallTimeTuner(Bench)
-  benchTuner.runDevTuner()
-
-
-
-
-
-  
-  
-if __name__ == "__main__":
-
-  createResultDirs(bench_tuner_data)
-
-  #runDevTimeBenchs()
-
-  runInstallTimeBenchs()
-
-  
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_curve.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_curve.py
deleted file mode 100644
index d90403be23fae547fde9e2ac4996f5cca3b0e5fb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_curve.py
+++ /dev/null
@@ -1,313 +0,0 @@
-
-
-from buildRtConfig import loadConfigData
-from benchmarks import bench_tuner_data
-import os
-import shutil
-
-
-AL_THRESHOLD = 0.1
-SPEEDUP_BAND_SIZE = 0.1
-ENERGY_BAND_SIZE = 10
-
-class Configuration:
-    def __init__(self, name, speedup, energy, accuracy, accuracy_loss, flags):
-        self.name = name
-        self.speedup = speedup
-        self.energy = energy
-        self.accuracy = accuracy
-        self.accuracy_loss = accuracy_loss
-        self.flags  = flags
-    def __repr__(self):
-        return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-configuration_objects = [
-    Configuration('conf1', 1.05, 15, 85, 1.2, []),
-    Configuration('conf2', 2.51, 12, 83, 1.4, []),
-    Configuration('conf3', 2.05, 10, 84, 0.8, []),
-]
-
-def compute_pareto_points(configurations):
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy > en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        # outer while loop variable increment
-        start_idx = end_idx
-    return [speedupconfigurations, energyconfigurations]
-
-
-def compute_pareto_points_with_margin(configurations, speedup_band_width, energy_band_width):
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    idx_to_sp_conf_dict = {}
-    idx_to_en_conf_dict = {}
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy < en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        idx_to_sp_conf_dict[start_idx] = len(speedupconfigurations)-1
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        idx_to_en_conf_dict[start_idx] = len(energyconfigurations)-1
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    # We want to add configurations in a band of a certain width around the curves
-    # not possible to do during contruction, because the quality of the curve would
-    # deteriorate quickly
-
-    AdjustedSpeedupCurve = []
-    AdjustedEnergyCurve = []
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup + speedup_band_width >= speedupconfigurations[idx_to_sp_conf_dict[start_idx]].speedup:
-                AdjustedSpeedupCurve.append(sorted_configurations[i])
-            if sorted_configurations[i].energy + energy_band_width >= energyconfigurations[idx_to_en_conf_dict[start_idx]].energy:
-                AdjustedEnergyCurve.append(sorted_configurations[i])
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    return [AdjustedSpeedupCurve, AdjustedEnergyCurve]
-
-
-
-def findParetoConfigs(base_dir, accuracy):
-
-  result_dir = base_dir + "/algo_tuner/pareto/"
-  try:
-      os.mkdir(result_dir)
-  except:
-      print ("could not create dir")
-
-  input_dir = base_dir    
-  config_arr = loadConfigData(input_dir, accuracy, "high_confidence")
-
-  config_list = []
-  it = 0
-  for config in config_arr:
-    config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss, config.flags)
-    config_list.append(config)
-
-
-  if (len(config_list) > 0):   
-    max_speedup = max(config.speedup for config in config_list)
-  else:
-    max_speedup = 1.5
-  
-  SPEEDUP_BAND_SIZE = 0.05 # max_speedup * 1.0 / 12 # 4  # 20% of the max speedup
-  ENERGY_BAND_SIZE = 10
-
-  print ("max_speedup = ", max_speedup, " BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-         
-
-  print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-  
-  ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-
-  #print (ASC)
-  #print (config_list)
-
-  if len(ASC) < 5:
-    ASC = config_list    
-
-
-  if len(ASC) > 50:
-    ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE/4, ENERGY_BAND_SIZE)
- 
-  
-  print ("len(config_list) = ", len(config_list))
-  print ("len(ASC) = ", len(ASC))
-  
-  for conf in ASC:
-    src_path = base_dir + "/algo_tuner/high_confidence/" + conf.name
-    dst_path = base_dir + "/algo_tuner/pareto/" + conf.name
-    shutil.copy(src_path, dst_path)
-
-  return ASC
-
-
-
-def flagsPerLayer(ASC, num_layers):
-
-  layer_flags = []
-  for i in range(num_layers):
-    layer_map = {}
-    layer_flags.append(layer_map)
-    
-
-  for config in ASC:
-    config_flags = config.flags
-    for i in range(len(config_flags)):
-      layer_flags[i][config_flags[i]] = 1
-
-      
-  print (layer_flags)
-    
-  return layer_flags
-
-  
-    
-    
-
-
-def dumpBenchPareto(Bench):
-
-  result_dir1 = Bench.result_dir_1
-  result_dir2 = Bench.result_dir_2
-  result_dir3 = Bench.result_dir_3
-
-  acc_thresh = Bench.promise_accuracy
-  
-  ASC1 = findParetoConfigs(result_dir1, acc_thresh)
-  ASC2 = findParetoConfigs(result_dir2, acc_thresh)
-  ASC3 = findParetoConfigs(result_dir3, acc_thresh)
-
-
-  flags1 = flagsPerLayer(ASC1, Bench.num_layers)
-  flags2 = flagsPerLayer(ASC2, Bench.num_layers)
-  flags3 = flagsPerLayer(ASC3, Bench.num_layers)
-
-  return flags1, flags2, flags3
-
-
-
-
-if __name__ == "__main__":
-
-  Bench = bench_tuner_data["alexnet_cifar10"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["alexnet2_cifar10"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar10"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar100"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["resnet18_cifar10"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["lenet_keras"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]  
-  dumpBenchPareto(Bench)
-
-  Bench = bench_tuner_data["mobilenet_shallow"]  
-  dumpBenchPareto(Bench)
-
-  
-  #get_pareto_configs("")
-  
-  #SC, EC = compute_pareto_points(configuration_objects)
-  #ASC, AEC = compute_pareto_points_with_margin(configuration_objects, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-  #print(SC)
-  #print(EC)
-
-  #print(ASC)
-  #print(AEC)
-
-
-
-
-
-
-    #result_dir = base_dir + "/pareto/"
-  #try:
-  #    os.mkdir(result_dir)
-  #except:
-  #    print "could not create dir"
-
-  #input_dir = base_dir + "/full_results/"    
-  #result_dir = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/batch15"
-  #config_arr = loadConfigData(input_dir, accuracy)
-
-  #config_list = []
-
-  #it = 0
-  #for config in config_arr:
-  #  config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss)
-  #  config_list.append(config)
-
-    
-  #ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-  #for conf in ASC:
-  #  dst_path = conf.name.replace("full_results", "pareto")
-  #  shutil.copy(conf.name, dst_path)
-    
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_utils.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_utils.py
deleted file mode 100644
index ae85160e8f36986c3a58e6033b0684a4338256e2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/pareto_utils.py
+++ /dev/null
@@ -1,257 +0,0 @@
-
-
-from buildRtConfig import loadConfigsFromDir
-import os
-import shutil
-
-
-AL_THRESHOLD = 0.1
-SPEEDUP_BAND_SIZE = 0.1
-ENERGY_BAND_SIZE = 10
-
-
-class Configuration:
-    
-    def __init__(self, name, speedup, energy, accuracy, accuracy_loss, flags):
-        self.name = name
-        self.speedup = speedup
-        self.energy = energy
-        self.accuracy = accuracy
-        self.avg_accuracy = accuracy
-        self.accuracy_loss = accuracy_loss
-        self.avg_loss = accuracy_loss
-        self.flags  = flags
-
-    def __repr__(self):
-        return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-    @staticmethod     
-    def speedup_points(configurations):
-        
-        return [
-                (conf.speedup, conf.accuracy)
-                for conf in configurations
-        ]
-                 
-
-configuration_objects = [
-    Configuration('conf1', 1.05, 15, 85, 1.2, []),
-    Configuration('conf2', 2.51, 12, 83, 1.4, []),
-    Configuration('conf3', 2.05, 10, 84, 0.8, []),
-]
-
-
-
-def is_pareto_efficient(configs, values, value_margins):
-    import numpy as np
-    from pprint import pprint
-    
-    np_values = np.array(values)
-    np_margins = np.array(value_margins)
-    is_efficient = np.ones(np_values.shape[0], dtype=bool)
-
-    for i, c in enumerate(np_values):
-        if is_efficient[i]:
-          # Keep any point with a higher value
-          is_efficient[is_efficient] = np.any(np_values[is_efficient] + np_margins >= c, axis=1)
-          is_efficient[i] = True  # And keep self
-            
-    return (np.array(configs)[is_efficient]).tolist()
-
-                                                                    
-
-
-
-def compute_pareto_points(configurations):
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and \
-              (sorted_configurations[end_idx].accuracy_loss - \
-               sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-            
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy > en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        # outer while loop variable increment
-        start_idx = end_idx
-    return [speedupconfigurations, energyconfigurations]
-
-
-
-
-def compute_pareto_points_with_margin(configurations, speedup_band_width, energy_band_width):
-    
-    speedupconfigurations = []
-    energyconfigurations = []
-    #sort configurations based on speedup
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
-    idx_to_sp_conf_dict = {}
-    idx_to_en_conf_dict = {}
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and \
-              (sorted_configurations[end_idx].accuracy_loss - \
-               sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-            
-        # find best speedup end energy in this accuracy loss level
-        sp = -1.0
-        sp_idx = 0
-        en = -1.0
-        en_idx = 0
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup > sp:
-                sp = sorted_configurations[i].speedup
-                sp_idx = i
-            if sorted_configurations[i].energy < en:
-                en = sorted_configurations[i].energy
-                en_idx = i
-        sp_not_dominated = True
-        # if not empty list of configurations
-        if speedupconfigurations:
-            if speedupconfigurations[-1].speedup >= sp:
-                sp_not_dominated = False
-        en_not_dominated = True
-        # if not empty list of configurations
-        if energyconfigurations:
-            if energyconfigurations[-1].energy >= en:
-                en_not_dominated = False
-        if sp_not_dominated:
-            speedupconfigurations.append(sorted_configurations[sp_idx])
-        idx_to_sp_conf_dict[start_idx] = len(speedupconfigurations)-1
-        if en_not_dominated:
-            energyconfigurations.append(sorted_configurations[en_idx])
-        idx_to_en_conf_dict[start_idx] = len(energyconfigurations)-1
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    # We want to add configurations in a band of a certain width around the curves
-    # not possible to do during contruction, because the quality of the curve would
-    # deteriorate quickly
-
-    AdjustedSpeedupCurve = []
-    AdjustedEnergyCurve = []
-
-    start_idx = 0
-    while start_idx < len(sorted_configurations):
-        end_idx = start_idx + 1;
-        # find end_idx
-        while end_idx < len(sorted_configurations) and \
-              (sorted_configurations[end_idx].accuracy_loss -  \
-               sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
-            end_idx += 1
-            
-        for i in range(start_idx, end_idx):
-            if sorted_configurations[i].speedup + speedup_band_width >= \
-               speedupconfigurations[idx_to_sp_conf_dict[start_idx]].speedup:
-                AdjustedSpeedupCurve.append(sorted_configurations[i])
-            if sorted_configurations[i].energy + energy_band_width >= \
-               energyconfigurations[idx_to_en_conf_dict[start_idx]].energy:
-                AdjustedEnergyCurve.append(sorted_configurations[i])
-        # outer while loop variable increment
-        start_idx = end_idx
-
-    return [AdjustedSpeedupCurve, AdjustedEnergyCurve]
-
-
-
-
-#***** Exported Routine *******/
-def dumpParetoConfigsToDir(input_dir, output_dir, gold_accuracy, enable_band):
-
-  config_arr = loadConfigsFromDir(input_dir, gold_accuracy)
-  config_list = []
-  it = 0
-
-  for config in config_arr:
-    config = Configuration(config.fname , config.speedup, 100, \
-                           config.avg_accuracy, config.avg_loss, config.flags)
-    
-    config_list.append(config)
-
-
-  if (len(config_list) > 0):   
-    max_speedup = max(config.speedup for config in config_list)
-  else:
-    max_speedup = 1.0  # No Speedup since no configuration found
-
-  
-  #SPEEDUP_BAND_SIZE = 0.05    # max_speedup * 1.0 / 12   # 4  # 20% of the max speedup
-
-  if enable_band:
-    SPEEDUP_BAND_SIZE = max_speedup * 1.0 / 10   # 10% of the max speedup is the 'BAND SIZE'  
-    ENERGY_BAND_SIZE = 0 # Unused right now
-
-    ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-  else:
-    SPEEDUP_BAND_SIZE = 0  # no pareto band - true pareto curve  
-    ENERGY_BAND_SIZE = 0 # Unused right now
-
-    #ASC, AEC = compute_pareto_points(config_list)
-
-    speedup_points = Configuration.speedup_points(config_list)
-    ASC = is_pareto_efficient(config_list, speedup_points, [-0.001, -0.001]) # [0.05, 0.05])
-
-        
-
-  print ("*max_speedup = ", max_speedup)         
-  print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-    
-
-  # Prevents very small pareto-curves
-  #if len(ASC) < 10  or len(config_list) < 20:
-
-  #if len(config_list) < 10:
-  #  ASC = config_list    
-
-  
-  print ("len(config_list) = ", len(config_list))
-  print ("len(ASC) = ", len(ASC))
-  
-  for conf in ASC:
-    src_path = input_dir + '/' + conf.name
-    dst_path = output_dir + '/' + conf.name
-    shutil.copy(src_path, dst_path)
-
-  return ASC
-
-
-
- 
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/profiling.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/profiling.py
deleted file mode 100644
index 3ed37822a6fa654c16f5c8ce3b41dc8287931b87..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/profiling.py
+++ /dev/null
@@ -1,26 +0,0 @@
-
-import time
-
-profiled_ops = {}
-
-def startProfile(op_id):
-  start = time.time()
-  return start
-  
-
-def stopProfile(op_id, start):
-  end = time.time()
-  total_time = end - start
- 
-  profiled_ops[op_id] = total_time
- 
-  
-def dumpProfiles(file_name):
-
-  f = open(file_name, "w+")
-  for op_id in profiled_ops:
-    f.write(op_id + " : " + str(profiled_ops[op_id]) + "\n")
-
-  f.close()
-      
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/remap.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/remap.py
deleted file mode 100644
index 8dc69357526b711d563d454f0ce41219dbfe579c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/remap.py
+++ /dev/null
@@ -1,291 +0,0 @@
-
-import sys
-import os
-import shutil
-from validation import invokeBinary
-from buildRtConfig import loadConfigData, loadPromiseConfigs
-from benchmarks import bench_tuner_data, batch_id
-from swing_selection import convL1bins, convL2bins
-
-
-
-def readKnobConfig(file_path):
-
-  knobs_speedup = {}
-  f = open(file_path, "r")
-  for x in f:
-    toks = x.split("\t")
-    ID = int(toks[0].split(",")[1])
-
-    speedup = float(toks[2])
-    knobs_speedup[ID] = speedup
-  
-  print ("knobs_speedup = ", knobs_speedup)
-  
-  return knobs_speedup
-
-
-
-def getPromiseSwing(l1, l2, flag):
-
-  if l1 < 0.1 or l2 < 0.1:
-    return flag
-      
-  swing = 1
-  for i in range(len(convL1bins)):
-    l1_t = convL1bins[i][0]
-    l2_t = convL2bins[i][0]
-    
-    if l1 > l1_t and l2 > l2_t:
-      break
-    swing += 1
-
-  return swing
-
-
-    
-def replaceWithPromise(layer_flags, norms_file):
-
-  num_layers = len(layer_flags)
-
-  f = open(norms_file, "r")
-  it = 0
-  for x in f:    
-    op_name = x.split()[0]
-    print ("op_name = ", op_name)
-    if op_name == "tensorMul":
-      break; 
-    
-    l1 = float(x.split()[5])
-    l2 = float(x.split()[6])
-
-    if it > 0:
-      flag = getPromiseSwing(l1, l2, layer_flags[it])
-      layer_flags[it] = flag
-    
-    #print ("l1 = ", l1, " l2 = ", l2)
-    it += 1                   
-
-    if it == num_layers:
-      break
-
-  print (layer_flags)
-  return layer_flags
-
-
-
-
-def readCostFile(file_path):
-
-  layer_costs = []
-  f = open(file_path)
-  for x in f:
-    cost = float(x.strip())
-    layer_costs.append(cost)
-
-  print ("len(layer_costs) = ", layer_costs)
-  f.close()
-
-  return layer_costs
-
-
-
-def getSpeedup(flags, knobs_speedup, layer_costs):
-
-  orig_cost = 0.0
-  total_cost = 0.0
-  it = 0
-  for flag_value in flags:
-    op_cost = layer_costs[it]
-    speedup = knobs_speedup[flag_value]
-
-    total_cost += (op_cost * 1.0 / speedup * 1.0)
-    orig_cost += op_cost    
-    it += 1
-
-  speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-  
-  return speedup
-
-
-
-def dumpNewFlags(new_flags, orig_file, promise_flags_file, layer_costs, knobs_speedup):
-
-  speedup = getSpeedup(new_flags, knobs_speedup, layer_costs)
-  
-  top_line = ""
-  for x in open(orig_file, "r"):
-    top_line = x
-    break
-  
-  f = open(promise_flags_file, "w+")
-  f.write(top_line.replace("\n", ""))
-  f.write("\tnew_speedup=" + str(speedup) + "\n")
-  
-
-  for flag in new_flags:
-    f.write(str(flag) + "\n")
-    
-  f.close()
-
-
-  
-
-def remapLossConfig(configs_arr, result_dir, sub_dir, layer_costs, knobs_speedup):
-
-  
-  for conf in configs_arr:
-    layer_flags = conf.flags
-    fname = conf.fname
-    norms_file = result_dir + "/algo_tuner/" + sub_dir + "/" + fname + "_norms"
-    orig_file = result_dir + "/algo_tuner/" + sub_dir + "/" + fname
-    new_flags = replaceWithPromise(layer_flags, norms_file)
-
-    promise_test_dir = result_dir + "/algo_tuner/promise_test/"
-    if not os.path.exists(promise_test_dir):
-      os.mkdir(promise_test_dir)
-
-    promise_flags_file = result_dir + "/algo_tuner/promise_test/" + fname + "_promise"
-    dumpNewFlags(new_flags, orig_file, promise_flags_file, layer_costs, knobs_speedup)
-
-  
-
-def remapConfigs(Bench):
-
-  
-  loss1_dir = Bench.result_dir_1
-  loss2_dir = Bench.result_dir_2
-  loss3_dir = Bench.result_dir_3
-
-  loss1_configs = loadConfigData(loss1_dir, 100, "validated")
-  loss2_configs = loadConfigData(loss2_dir, 100, "validated")
-  loss3_configs = loadConfigData(loss3_dir, 100, "validated")
-
-  knobs_speedup = readKnobConfig("../opentuner/data/global_knobs.txt")
-  layer_costs = readCostFile(Bench.cost_file)
-
-  remapLossConfig(loss1_configs, loss1_dir, "validated", layer_costs, knobs_speedup)
-  remapLossConfig(loss2_configs, loss2_dir, "validated", layer_costs, knobs_speedup)
-  remapLossConfig(loss3_configs, loss3_dir, "validated", layer_costs, knobs_speedup)
-  
-  
-
-
-def validateRemapConfigs(Bench):
-
-  num_layers = Bench.num_layers
-  base_conf = getBaselineConfig(num_layers)
-  # Path to binary to run
-  binary_path = Bench.promise_binary
-  # NOTE: 'target_acc' passed 0.0 since unused for baseline run
-  invokeBinary(binary_path, base_conf, 1, 2000, 8000, 0.0)
-  gold_acc = readAccuracy("final_accuracy")
-
-  
-  loss1_dir = Bench.result_dir_1
-  loss2_dir = Bench.result_dir_2
-  loss3_dir = Bench.result_dir_3
-
-  loss1_configs = loadPromiseConfigs(loss1_dir, 100, "promise_test")
-  loss2_configs = loadPromiseConfigs(loss2_dir, 100, "promise_test")
-  loss3_configs = loadPromiseConfigs(loss3_dir, 100, "promise_test")
-
-  runs = 30
-  validateAlgoConfigs(binary_path, loss1_dir, loss1_configs, gold_acc, 1.0, runs)
-  validateAlgoConfigs(binary_path, loss2_dir, loss2_configs, gold_acc, 2.0, runs)
-  validateAlgoConfigs(binary_path, loss3_dir, loss3_configs, gold_acc, 3.0, runs)
-
-
-
-
-
-  
-    
-
-def copyNormFile(fname, result_dir, sub_dir):
-
-  target_dir = result_dir + "/algo_tuner/" + sub_dir
-  dest_file = target_dir + "/" + fname + "_norms"
-
-  shutil.copy("accuracy_summary", dest_file)  
-
-
-
-
-def dumpNorms(binary_path, result_dir, configs_arr):
-
-  runs = 1  
-  for conf in configs_arr:
-    layer_swings = conf.flags
-    invokeBinary(binary_path, layer_swings, runs, 3000, 5000, 100)
-    
-    #copyNormFile(conf.fname, result_dir, "high_confidence")
-    copyNormFile(conf.fname, result_dir, "validated")
-
-
-
-def computeConfigNorms(Bench):
-    
-  loss1_dir = Bench.result_dir_1
-  loss2_dir = Bench.result_dir_2
-  loss3_dir = Bench.result_dir_3
-
-  loss1_configs = loadConfigData(loss1_dir, 100, "validated")
-  loss2_configs = loadConfigData(loss2_dir, 100, "validated")
-  loss3_configs = loadConfigData(loss3_dir, 100, "validated")
-
-
-  binary_path = Bench.promise_binary
-
-  dumpNorms(binary_path, loss1_dir, loss1_configs)
-  dumpNorms(binary_path, loss2_dir, loss2_configs)
-  dumpNorms(binary_path, loss3_dir, loss3_configs)
-  
-
-
-if __name__ == "__main__":
-
-  Bench = bench_tuner_data["alexnet_cifar10"]      
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-  
-  Bench = bench_tuner_data["alexnet2_cifar10"]      
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar10"]      
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar100"]      
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["resnet18_cifar10"]      
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["mobilenet_shallow"]  
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]  
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  Bench = bench_tuner_data["lenet_keras"]  
-  computeConfigNorms(Bench)
-  remapConfigs(Bench)
-  #validateRemapConfigs(Bench)
-
-  #computeConfigNorms(Bench)
-  #remapConfigs(Bench)
-  
-  #validateRemapConfigs(Bench)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner.py
deleted file mode 100644
index 2df75fbfc4e7568361747f75f06a4b818a8f99be..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner.py
+++ /dev/null
@@ -1,102 +0,0 @@
-
-
-import os
-import subprocess
-from error_sensitivity import select_skip_layers
-
-
-def runAlgoTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs):
-
-  tuner_cmd = "python2  ../opentuner/autotuner/algo_tuner.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-layers "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += dir_prefix
-  tuner_cmd += result_dir + "/algo_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
-  tuner_cmd += " --cost-file "
-  tuner_cmd += Bench.cost_file
-  tuner_cmd += " --knobs-config "
-  tuner_cmd += "../opentuner/data/global_knobs.txt"
-  tuner_cmd += " --layer-knobs "
-  tuner_cmd += Bench.layer_knobs
-
-  
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-  
-
-"""
-
-def promiseTunerLoss1(Bench, dir_prefix):
-
-  tuner_runs = int(Bench.autotuner_runs / 3)
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 30)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 50)
-
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers3)
-
-
-def promiseTunerLoss2(Bench, dir_prefix):
-
-  tuner_runs = int(Bench.autotuner_runs / 3) 
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 20)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 40)
-
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers3)
-
-
-  
-def promiseTunerLoss3(Bench, dir_prefix):
-
-  tuner_runs = int (Bench.autotuner_runs / 3)
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 10)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 30)
-  
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers3)
-
-
-"""
-
-
-BASELINE = True
-
-  
-def runAlgoBench(Bench):
-
-  # NOTE-IMP: Changing current directory to one with promise binaries
-  dir_prefix = "../build_tuner/"
-  
-
-  if BASELINE:
-    tuner_runs = Bench.autotuner_runs 
-    runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs)
-    runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs)
-    runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs)
-    
-  else:    
-    promiseTunerLoss1(Bench, dir_prefix)
-    promiseTunerLoss2(Bench, dir_prefix)
-    promiseTunerLoss3(Bench, dir_prefix)
-
-  
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner2.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner2.py
deleted file mode 100644
index 99867fade3aac75d2fcc4c411e25c2d16595052d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_algo_tuner2.py
+++ /dev/null
@@ -1,186 +0,0 @@
-
-
-import os
-import numpy as np
-import subprocess
-from error_sensitivity import select_skip_layers
-from pareto_curve import dumpBenchPareto
-from remap import readCostFile
-
-
-def runAlgoTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs):
-
-  fixed_runs = 100
-  
-  tuner_cmd = "python2  ../opentuner/autotuner/algo_tuner2.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(fixed_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-layers "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += dir_prefix
-  tuner_cmd += result_dir + "/promise_tuner3/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
-  tuner_cmd += " --cost-file "
-  tuner_cmd += Bench.cost_file
-  tuner_cmd += " --layer-file "
-  tuner_cmd += Bench.layer_file
-  tuner_cmd += " --knobs-config "
-  tuner_cmd += "../opentuner/data/global_knobs.txt"
-  tuner_cmd += " --layer-knobs "
-  tuner_cmd += " local_knobs.txt "
-
-  
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-  
-
-  
-def is50Knob(flag):
-  
-  flags50 = {}
-  flags50[21] = 1
-  flags50[22] = 1
-  flags50[26] = 1
-  flags50[27] = 1
-  flags50[31] = 1
-  flags50[32] = 1
-
-  if flag in flags50:
-    return True
-  else:
-    return False
-  
-  
-
-def is25Knob(flag):
-
-  flags25 = {}
-  flags25[23] = 1
-  flags25[24] = 1
-  flags25[25] = 1
-  flags25[28] = 1
-  flags25[29] = 1
-  flags25[30] = 1
-  flags25[33] = 1
-  flags25[34] = 1
-  flags25[35] = 1
-  flags25[36] = 1
-
-  if flag in flags25:
-    return True
-  else:
-    return False
-  
-   
-  
-def addPromiseFlags(flag_map):
-
-  flags = []
-
-  has_50_flag = False
-  has_25_flag = False
- 
-  for flag in flag_map:
-    if is50Knob(flag):
-      has_50_flag = True   
-    if is25Knob(flag):
-      has_25_flag = True
-      
-
-  if has_50_flag:
-    flag_map[7] = 1
-    flag_map[5] = 1
-    flag_map[3] = 1
-
-  if has_25_flag:
-    flag_map[7] = 1
-
-  return flag_map
-
-
-
-def addCostBasedFlags(flag_map, layer_costs, i):
-
-  median = np.median(layer_costs)
-  max_cost = np.max(layer_costs)
-  sorted_vals = np.sort(layer_costs)
-  
-  print ("**** Median = ", median)
-  print ("**** Max_cost = ", max_cost)
-  print ("**** Sorted_vals = ", sorted_vals, "\n\n")
-
-  
-  if (layer_costs[i] > (median * 1.5)):
-    flag_map[7] = 1
-
-  if (layer_costs[i] > (median * 3)) or layer_costs[i] == max_cost:
-    flag_map[7] = 1
-    flag_map[5] = 1
-    flag_map[3] = 1  
-
-    
-  if (layer_costs[i] < (median / 10)):
-    flag_map = {}
-    flag_map[12] = 1
-
-  return flag_map  
-
-
-  
-
-  
-def constructKnobsFile(flags, layer_costs):
-
-  f = open("local_knobs.txt", "w+")
-  for i in range(len(flags)):
-    flag_map = flags[i]
-
-    if i > 0:
-      flag_map = addPromiseFlags(flag_map)
-      flag = addCostBasedFlags(flag_map, layer_costs, i)
-          
-    it = 0
-    for flag in flag_map:
-      f.write(str(flag))
-      if it < len(flag_map) - 1:
-        f.write(",")
-      it += 1  
-      
-    f.write("\n")
-
-  f.close()  
-
-
-
-
-  
-def runPromiseAlgoBench(Bench):
-
-  # NOTE-IMP: Changing current directory to one with promise binaries
-  dir_prefix = "../build_tuner/"
-  
-
-  tuner_runs = Bench.autotuner_runs
-
-  layer_costs = readCostFile(Bench.cost_file)
-
-  flags1, flags2, flags3 = dumpBenchPareto(Bench)
-  
-  constructKnobsFile(flags1, layer_costs)
-  runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.8, tuner_runs)
-
-  constructKnobsFile(flags2, layer_costs)
-  runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.6, tuner_runs)
-
-  constructKnobsFile(flags3, layer_costs)
-  runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.2, tuner_runs)
-    
- 
-  
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_autotuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_autotuner.py
deleted file mode 100644
index 800bf926a5dc3ac9a8d9cd6d7e6c3dfb5e829585..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_autotuner.py
+++ /dev/null
@@ -1,403 +0,0 @@
-
-import os
-import sys
-import subprocess
-import shutil
-
-from swing_selection import loadLayerDesc
-from error_sensitivity import test_sensitivity, test_sensitivity2, test_sensitivity3, test_sensitivity4  
-from benchmarks import bench_tuner_data, batch_id
-from run_psnr import runPSNRTuner
-from run_ha_tuner import runTunerBench
-from run_hs_tuner import runPromiseBench
-from run_algo_tuner import runAlgoBench
-from run_algo_tuner2 import runPromiseAlgoBench
-from compute_confs import computePSNRBenchSwings, computeBenchSwings
-from validation import runPromiseBenchValidation2, runBenchValidation, runAlgoBenchValidate  
-from profiling import startProfile, stopProfile, dumpProfiles  
-from utils import createResultDirs
-from benchmarks import batch_id
-from run_devtime_tuner import DevTimeTuner
-
-  
-  
-def runTunerValidation():
-
-  runBenchValidation(bench_tuner_data["mobilenet_shallow"])
- 
-  #runBenchValidation("mobilenet_cifar10")
- 
-  #runBenchValidation("alexnet_cifar10")
-  #runBenchValidation("vgg16_cifar10")
-  #runBenchValidation("alexnet2_cifar10")
-  #runBenchValidation("resnet18_cifar10") 
-  #runBenchValidation("vgg16_cifar100")
-  
-
-def computeLayerSwings():
-
-
-  computeBenchSwings(bench_tuner_data["mobilenet_shallow"])
-
-  #computeBenchSwings("mobilenet_cifar10")
-
-  #computeBenchSwings("mobilenet_cifar10")
-
-  #computeBenchSwings("lenet_keras")
-  #computeBenchSwings("alexnet_cifar10")
-  #computeBenchSwings("alexnet2_cifar10")
-  #computePSNRBenchSwings("pipeline_GEOM")
-  #computePSNRBenchSwings("pipeline_GEMO")
-  #computePSNRBenchSwings("pipeline_GEO")
-  #computePSNRBenchSwings("pipeline_GSM")
-  #computePSNRBenchSwings("pipeline_GSME")
-
-  
-
-
-  
-def runPromiseTuner():
-
-  
-  start = startProfile("MobileNet")  
-  runPromiseBench(bench_tuner_data["mobilenet_cifar10"])
-  stopProfile("MobileNet", start)
-  
-  start = startProfile("Alexnet")  
-  runPromiseBench(bench_tuner_data["alexnet_cifar10"])
-  stopProfile("Alexnet", start)
-
-  start = startProfile("Alexnet2")  
-  runPromiseBench(bench_tuner_data["alexnet2_cifar10"])
-  stopProfile("Alexnet2", start)  
-
-  start = startProfile("VGG16_10")  
-  runPromiseBench(bench_tuner_data["vgg16_cifar10"])
-  stopProfile("VGG16_10", start)  
-
-  start = startProfile("VGG16_100")  
-  runPromiseBench(bench_tuner_data["vgg16_cifar100"])
-  stopProfile("VGG16_100", start)
-
-  start = startProfile("ResNet")  
-  runPromiseBench(bench_tuner_data["resnet18_cifar10"])
-  stopProfile("ResNet", start)  
-
-  start = startProfile("MobileNet-SH")  
-  runPromiseBench(bench_tuner_data["mobilenet_shallow"])
-  stopProfile("MobileNet-SH", start)  
-  
-  start = startProfile("LeNet")  
-  runPromiseBench(bench_tuner_data["lenet_keras"])
-  stopProfile("LeNet", start)
-  
-
-
-  #runPSNRPromiseBench("pipeline_GEOM")
-  #runPSNRPromiseBench("pipeline_GEMO")
-  #runPSNRPromiseBench("pipeline_GEO")
-  #runPSNRPromiseBench("pipeline_GSM")
-  #runPSNRPromiseBench("pipeline_GSME")
-
-  dumpProfiles("time_profile" + batch_id + ".txt")
-  
-
-
-  
-def runPromiseValidation():
-
-
-  start = startProfile("AlexNet")    
-  runPromiseBenchValidation2(bench_tuner_data["alexnet_cifar10"])
-  stopProfile("AlexNet", start)  
-
-  start = startProfile("AlexNet2")    
-  runPromiseBenchValidation2(bench_tuner_data["alexnet2_cifar10"])
-  stopProfile("AlexNet2", start)  
-
-  start = startProfile("VGG16_100")    
-  runPromiseBenchValidation2(bench_tuner_data["vgg16_cifar100"])
-  stopProfile("VGG16_100", start)  
-
-  start = startProfile("VGG16_10")    
-  runPromiseBenchValidation2(bench_tuner_data["vgg16_cifar10"])
-  stopProfile("VGG16_10", start)  
-  #runPromiseBenchValidation2(bench_tuner_data["lenet_keras"])
-
-  start = startProfile("ResNet")    
-  runPromiseBenchValidation2(bench_tuner_data["resnet18_cifar10"])
-  stopProfile("ResNet", start)  
-
-  start = startProfile("MobileNet_SH")  
-  runPromiseBenchValidation2(bench_tuner_data["mobilenet_shallow"])
-  stopProfile("MobileNet_SH", start)  
-
-  start = startProfile("MobileNet")    
-  runPromiseBenchValidation2(bench_tuner_data["mobilenet_cifar10"])
-  stopProfile("MobileNet", start)  
-
-  
-  dumpProfiles("validation_prof" + batch_id + ".txt")
-
-  
-  
-
-def runAutotuner(): 
-
-  runTunerBench(bench_tuner_data["alexnet_cifar10"])
-  runTunerBench(bench_tuner_data["alexnet2_cifar10"])
-
-  #runTunerBench("mobilenet_shallow")
-  #runTunerBench("mobilenet_cifar10")
-  
-  #runTunerBench("lenet_keras")
-  #runTunerBench("resnet18_cifar10")
-  #runTunerBench("vgg16_cifar10")
-
-  #runPSNRTuner("pipeline_GEOM")
-  #runPSNRTuner("pipeline_GEMO")
-  #runPSNRTuner("pipeline_GEO")
-  #runPSNRTuner("pipeline_GSM")
-  #runPSNRTuner("pipeline_GSME")
-
-
-
-
-def runSensAnalysis():
- 
-  start = startProfile("LeNet")  
-  test_sensitivity4(bench_tuner_data["lenet_keras"])
-  stopProfile("LeNet", start)  
-
-  """
-  start = startProfile("AlexNet")  
-  test_sensitivity4(bench_tuner_data["alexnet_cifar10"])
-  stopProfile("AlexNet", start)  
-
-  start = startProfile("AlexNet2")  
-  test_sensitivity4(bench_tuner_data["alexnet2_cifar10"])
-  stopProfile("AlexNet2", start)  
-
-  start = startProfile("ResNet")  
-  test_sensitivity4(bench_tuner_data["resnet18_cifar10"])
-  stopProfile("ResNet", start)  
-
-  start = startProfile("MobileNet")  
-  test_sensitivity4(bench_tuner_data["mobilenet_cifar10"])
-  stopProfile("MobileNet", start)  
-
-  start = startProfile("MobileNet_SH")  
-  test_sensitivity4(bench_tuner_data["mobilenet_shallow"])
-  stopProfile("MobileNet_SH", start)  
-
-  start = startProfile("VGG_10")  
-  test_sensitivity4(bench_tuner_data["vgg16_cifar10"])
-  stopProfile("VGG16_10", start)  
-
-  start = startProfile("VGG_100")  
-  test_sensitivity4(bench_tuner_data["vgg16_cifar100"]) 
-  stopProfile("VGG16_100", start)  
-
-  dumpProfiles("sens_time_prof.txt")
-
-  """
-  
-  start = startProfile("LeNet")  
-  test_sensitivity3(bench_tuner_data["lenet_keras"])
-  stopProfile("LeNet", start)  
-
-  start = startProfile("AlexNet")  
-  test_sensitivity3(bench_tuner_data["alexnet_cifar10"])
-  stopProfile("AlexNet", start)  
-
-  start = startProfile("AlexNet2")  
-  test_sensitivity3(bench_tuner_data["alexnet2_cifar10"])
-  stopProfile("AlexNet2", start)  
-
-  start = startProfile("ResNet")  
-  test_sensitivity3(bench_tuner_data["resnet18_cifar10"])
-  stopProfile("ResNet", start)  
-
-
-  start = startProfile("MobileNet")  
-  test_sensitivity3(bench_tuner_data["mobilenet_cifar10"])
-  stopProfile("MobileNet", start)  
-
-  start = startProfile("MobileNet_SH")  
-  test_sensitivity3(bench_tuner_data["mobilenet_shallow"])
-  stopProfile("MobileNet_SH", start)  
-
-  start = startProfile("VGG_10")  
-  test_sensitivity3(bench_tuner_data["vgg16_cifar10"])
-  stopProfile("VGG16_10", start)  
-
-  start = startProfile("VGG_100")  
-  test_sensitivity3(bench_tuner_data["vgg16_cifar100"]) 
-  stopProfile("VGG16_100", start)  
-
-  dumpProfiles("sens_time_prof.txt")
-
-  
-  """
-  test_sensitivity2(bench_tuner_data["fc4"]) 
-  test_sensitivity2(bench_tuner_data["lenet_keras"]) 
-  test_sensitivity2(bench_tuner_data["mobilenet_cifar10"]) 
-  test_sensitivity2(bench_tuner_data["mobilenet_shallow"]) 
-  test_sensitivity2(bench_tuner_data["resnet18_cifar10"]) 
-  test_sensitivity2(bench_tuner_data["alexnet_cifar10"]) 
-  test_sensitivity2(bench_tuner_data["alexnet2_cifar10"]) 
-  test_sensitivity2(bench_tuner_data["vgg16_cifar10"]) 
-  test_sensitivity2(bench_tuner_data["vgg16_cifar100"]) 
-
-
-  test_sensitivity(bench_tuner_data["fc4"]) 
-  test_sensitivity(bench_tuner_data["lenet_keras"]) 
-  test_sensitivity(bench_tuner_data["mobilenet_cifar10"]) 
-  test_sensitivity(bench_tuner_data["mobilenet_shallow"]) 
-  test_sensitivity(bench_tuner_data["resnet18_cifar10"]) 
-  test_sensitivity(bench_tuner_data["alexnet_cifar10"]) 
-  test_sensitivity(bench_tuner_data["alexnet2_cifar10"]) 
-  test_sensitivity(bench_tuner_data["vgg16_cifar10"]) 
-  test_sensitivity(bench_tuner_data["vgg16_cifar100"]) 
-  """
-
-
-
-  
-
-
-def runAlgoTuner():
-
-  Bench = bench_tuner_data["alexnet_cifar10"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["mobilenet_shallow"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar10"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["lenet_keras"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["alexnet2_cifar10"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar100"]  
-  runAlgoBench(Bench)
-
-  Bench = bench_tuner_data["resnet18_cifar10"]  
-  runAlgoBench(Bench)
-
-
-
-
-
-def runPromiseAlgoTuner():
-
-  Bench = bench_tuner_data["alexnet_cifar10"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["mobilenet_shallow"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar10"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["lenet_keras"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["alexnet2_cifar10"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar100"]  
-  runPromiseAlgoBench(Bench)
-
-  Bench = bench_tuner_data["resnet18_cifar10"]  
-  runPromiseAlgoBench(Bench)
-
-  
-
-
-  
-
-  
-def runAlgoTunerValidation():
-
-  Bench = bench_tuner_data["alexnet_cifar10"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["mobilenet_shallow"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["mobilenet_cifar10"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar10"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["lenet_keras"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["alexnet2_cifar10"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["vgg16_cifar100"]  
-  runAlgoBenchValidate(Bench)
-
-  Bench = bench_tuner_data["resnet18_cifar10"]  
-  runAlgoBenchValidate(Bench)
-
-
-
-
-  
-# Invoke Dev-time Autotuner
-def runDevTimeBenchs():
-
-  Bench = bench_tuner_data["lenet_keras"]  
-
-  lenetTuner = DevTimeTuner(Bench)
-  lenetTuner.runDevTuner()
-
-
-
-  
-  
-if __name__ == "__main__":
-
-  createResultDirs(bench_tuner_data)
-
-  
-  #-- runAutotuner()
-  
-
-  #runTunerValidation()
-
-  #computeLayerSwings()
-  
-  #runPromiseTuner()    
-
-  #runAlgoTuner()
-
-  
-  runDevTimeBenchs()
-
-  
-  #--- runPromiseAlgoTuner()
-
-
-  
-  #runAlgoTunerValidation()
-  
-  #runPromiseValidation()
-
-  #runSensAnalysis()
-
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_devtime_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_devtime_tuner.py
deleted file mode 100644
index 0c701714f2bfc57466c396c9c9a2522d954cb701..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_devtime_tuner.py
+++ /dev/null
@@ -1,311 +0,0 @@
-
-
-import os
-import sys
-import subprocess
-import shutil
-import time
-from benchmarks import batch_id
-import utils
-import global_paths  
-import pareto_utils
-import buildRtConfig
-import genPlots
-
-
-
-class DevTimeTuner:
-
-  def __init__(self, Bench):
-
-    self.piped_execution = True
-    self.autotuner_runs = 8000
-
-    self.promise_binary = Bench.promise_binary
-    
-    if self.piped_execution:
-      self.binary_path = Bench.piped_binary
-    else:
-      self.binary_path = Bench.promise_binary
-
-    self.num_layers = Bench.num_layers
-    self.gold_accuracy = Bench.promise_accuracy
-    self.cost_file = global_paths.tensorRT_dir + "/" + Bench.cost_file
-    self.layer_file = global_paths.tensorRT_dir + "/" + Bench.layer_file
-    #self.layer_knobs = global_paths.tensorRT_dir + "/" + Bench.layer_knobs
-
-    global_knobs_file = global_paths.tensorRT_dir + "/autotuner/data/global_knobs.txt"
-    buildRtConfig.initializeApproxMap(global_knobs_file) # Initialize knobs - configfile gen
-    utils.createDevKnobs(self.layer_file, global_knobs_file, "dev_knobs.txt")
-    self.layer_knobs = "dev_knobs.txt"
-    
-    self.result_dir = global_paths.tensorRT_dir + "/" + Bench.base_dir + \
-                      "/loss_123/" + batch_id + "/dev_tuner/"
-
-    # NOTE: maintains total iterations completed - across multiple invocations 
-    self.iterations_completed = 0
-    # Start time for timing autotuner runs
-    self.start_time = 0
-
-    
-    
-    
-  def invokeDevTunerScript(self, accuracy_slack, \
-                           additional_error_slack, autotuner_runs):
-
-    accuracy_threshold = self.gold_accuracy - accuracy_slack
-    accuracy_additional_slack = self.gold_accuracy - additional_error_slack
-    
-    tuner_cmd = "python2 " + global_paths.opentuner_src_dir + "/devtuner.py "
-    tuner_cmd += " --test-limit "
-    tuner_cmd += str(self.autotuner_runs)
-    tuner_cmd += " --binary ./"
-    tuner_cmd += self.binary_path
-    tuner_cmd += " --num-layers "
-    tuner_cmd += str(self.num_layers)
-    tuner_cmd += " --result-dir "
-    tuner_cmd += self.result_dir 
-    tuner_cmd += " --accuracy "
-    tuner_cmd += str(accuracy_threshold)
-    tuner_cmd += " --accuracy-slack "
-    tuner_cmd += str(accuracy_additional_slack)
-    tuner_cmd += " --cost-file "
-    tuner_cmd += self.cost_file
-    tuner_cmd += " --knobs-config "
-    tuner_cmd += global_paths.tensorRT_dir + "/autotuner/data/global_knobs.txt"
-    ### tuner_cmd += "../autotuner/data/global_knobs.txt"
-    tuner_cmd += " --layer-knobs "
-    tuner_cmd += self.layer_knobs
-    tuner_cmd += " --start-id "
-    tuner_cmd += str(self.iterations_completed)
-  
-
-    print (tuner_cmd)
-
-    p = subprocess.Popen(tuner_cmd, shell=True)
-    p.wait()
-
-
-    # Update iterations completed after each completed devtuner.py invocation with N iterations
-    self.iterations_completed += self.autotuner_runs
-
-
-
-  def checkExistingDir(self):
-
-    files_dir = self.result_dir + "/high_confidence/"    
-    if os.path.exists(files_dir) and len(os.listdir(files_dir)) >= 1:
-      print ("result_dir = ", files_dir, " has existing files \n ")
-      user_str = input("Enter 'yes' to override - Enter 'no' to skip and exit \n ")
-      if user_str != "yes":
-        print ("\n\n NOTE:Exiting \n\n")
-        sys.exit(0)
-
-
-
-  def dumpBestConfig(self, configurations):
-  
-    best_conf_path = self.result_dir + "/best_config.txt"
-    conf_file = open(best_conf_path, "w+")
-    
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.speedup)
-
-    if len(sorted_configurations) > 0:
-      best_conf = sorted_configurations[-1]
-
-      conf_file.write("speedup = " + str(best_conf.speedup) + \
-                    "  avg_loss = " + str(best_conf.avg_loss) + "\n")
-    
-      for flag in best_conf.flags:
-        conf_file.write(str(flag) + "\n")
-
-      conf_file.close()
-    
-
-    
-  def dumpAllConfigurations(self):
-
-    input_dir = self.result_dir + "/high_confidence/"
-    
-    configurations = buildRtConfig.loadConfigsFromDir(input_dir, self.gold_accuracy)
-    bench_layer_composition = utils.getLayerComposition(self.layer_file)
-
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.speedup)
-
-    # Adding an extra loss to tuned configurations - adjusting for unseen data
-    buildRtConfig.adjustConfigLosses(sorted_configurations)  
-
-
-    config_out_path = self.result_dir + "dev_gpu_all_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "gpu")
-
-    config_out_path = self.result_dir + "dev_cpu_all_config.txt"
-    
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "cpu")
-
-    
-    plot_file_path = self.result_dir + "dev_all_conf_plot.png"
-    genPlots.genScatterPlotFromConfigs(sorted_configurations, plot_file_path)
-
-    
-    self.dumpBestConfig(sorted_configurations)
-
-    
-    
-  def dumpBandPareto(self):
-      
-    input_dir = self.result_dir + "/high_confidence/"
-    output_dir = self.result_dir + "/pareto/"
-    utils.createDir(output_dir)
-
-    configurations = pareto_utils.dumpParetoConfigsToDir(input_dir, \
-                                                         output_dir, self.gold_accuracy, True)   
-    
-    config_out_path = self.result_dir + "dev_pareto_config.txt"
-    bench_layer_composition = utils.getLayerComposition(self.layer_file)
-
-    #-- sorted_configurations = sorted(configurations, key=lambda conf: conf.avg_loss)
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.speedup)
-    
-    # Adding an extra loss to tuned configurations - adjusting for unseen data
-    buildRtConfig.adjustConfigLosses(sorted_configurations)  
-
-
-    config_out_path = self.result_dir + "dev_gpu_pareto_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "gpu")
-
-    config_out_path = self.result_dir + "dev_cpu_pareto_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "cpu")
-
-    plot_file_path = self.result_dir + "dev_pareto_plot.png"
-    genPlots.genScatterPlotFromConfigs(sorted_configurations, plot_file_path)
-
-    
-
-  def dumpTruePareto(self):
-    
-    input_dir = self.result_dir + "/high_confidence/"
-    output_dir = self.result_dir + "/true_pareto/"
-    utils.createDir(output_dir)
-
-    # NOTE: This is a true pareto curve construction
-    configurations = pareto_utils.dumpParetoConfigsToDir(input_dir, \
-                                                         output_dir, self.gold_accuracy, False)
-    
-    config_out_path = self.result_dir + "true_pareto_config.txt"
-    bench_layer_composition = utils.getLayerComposition(self.layer_file)
-
-    sorted_configurations = sorted(configurations, key=lambda conf: conf.avg_loss)
-
-    # Adding an extra loss to tuned configurations - adjusting for unseen data
-    buildRtConfig.adjustConfigLosses(sorted_configurations)  
-
-
-    config_out_path = self.result_dir + "true_gpu_pareto_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "gpu")
-
-    config_out_path = self.result_dir + "true_cpu_pareto_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "cpu")
-
-
-    plot_file_path = self.result_dir + "true_pareto_plot.png"
-    genPlots.genScatterPlotFromConfigs(sorted_configurations, plot_file_path)
-    
-      
-
-    
-  def dumpParetoFiles(self):
-
-    self.dumpBandPareto()
-    self.dumpTruePareto()
-    
-    
-
-  def dumpReferenceFiles(self):
-
-    ref_dir = self.result_dir + "/references/"
-    utils.createDir(ref_dir)
-
-    sources = {"run_devtime_tuner.py", "benchmarks.py", \
-               "buildRtConfig.py", "global_paths.py"}
-
-    for src in sources:
-      src_path = global_paths.tensorRT_dir + "/autotuner/tuner_driver_src/" + src
-      dst_path = ref_dir + "/" + src    
-      shutil.copy(src_path, dst_path)
-    
-
-    data_files = {self.cost_file, self.layer_file, self.layer_knobs}
-
-    for datafile in data_files:
-      shutil.copy(datafile, ref_dir)
-  
-       
-
-  def setBaselineAccuracy(self):
-
-    self.gold_accuracy = utils.getBaselineAccuracy(self.promise_binary, self.num_layers)
-    print ("NOTE: Baseline Accuracy = ", self.gold_accuracy,  "\n\n")
-
-
-
-  def startTimer(self):
-    self.start_time = time.time()
-    print ("\n\n ---Starting DevTuner Timer ----- \n\n")
-    
-
-    
-  def endTimer(self):
-    end_time = time.time()
-    total_tuning_time = end_time - self.start_time
-    time_hrs = total_tuning_time * 1.0 / (60 * 60)
-    print ("\n\n --- Time In Hours = ", time_hrs, " \n\n")
-
-    time_file_path = self.result_dir + "tuning_time.txt"
-
-    f = open(time_file_path, "w+")
-    f.write("time_hrs = " + str(time_hrs) + "\n")
-    f.close()
-    
-
-        
-    
-  def runDevTuner(self):
-    
-    #self.checkExistingDir()
-    
-    
-    self.startTimer()
-
-    self.setBaselineAccuracy()
-    
-    self.invokeDevTunerScript(0.8, 2.1, self.autotuner_runs)
-    self.invokeDevTunerScript(1.5, 2.1, self.autotuner_runs)
-    self.invokeDevTunerScript(2.1, 2.1, self.autotuner_runs)
-  
-    self.dumpParetoFiles()
-    self.dumpAllConfigurations()
-
-
-    # NOTE: dumping files for checking experimental parameters for each batch
-    self.dumpReferenceFiles()
-
-    
-    self.endTimer()
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_ha_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_ha_tuner.py
deleted file mode 100644
index 055d2c4c1bde6bf02e080c53101f03dc1791fd9e..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_ha_tuner.py
+++ /dev/null
@@ -1,52 +0,0 @@
-
-
-
-import subprocess
-
-
-
-#, bench_name
-def runTunerBench(Bench):
-
-  #Bench = bench_tuner_data[bench_name]
-
-  #FIXIT: Replace  approxhpvm_tuner2 with  approxhpvm_tuner
-  tuner_cmd = "python  ../opentuner/autotuner/approxhpvm_tuner.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.tuner_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_flags)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(Bench.error_range_2)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += Bench.result_dir_2
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.tuner_accuracy - 1.70)
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-
-  tuner_cmd = "python  ../opentuner/autotuner/approxhpvm_tuner.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.tuner_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_flags)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(Bench.error_range_1)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += Bench.result_dir_1
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.tuner_accuracy - 0.85)
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_hs_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_hs_tuner.py
deleted file mode 100644
index f1a9c8f417bafdf4084a687670074101bec3faa0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_hs_tuner.py
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-import os
-import subprocess
-from error_sensitivity import select_skip_layers
-
-
-def runPromiseTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs, skip_layers):
-
-  tuner_cmd = "python2  ../opentuner/autotuner/promise_tuner3.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --start-range "
-  tuner_cmd += str(Bench.start_promise_range)
-  tuner_cmd += " --error-range "
-  #tuner_cmd += str(10)
-  # NOTE: Increasing flags from ApproxTechiqueTuner
-  tuner_cmd += str(12) 
-  tuner_cmd += " --result-dir "
-  tuner_cmd += dir_prefix
-  tuner_cmd += result_dir + "/promise_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
-  tuner_cmd += " --layer-file "
-  tuner_cmd += dir_prefix
-  tuner_cmd += Bench.tensor_desc_file
-  # NOTE: Cost file is new addition - ***NOT*** present in promisetuner1 and promisetuner2
-  tuner_cmd += " --cost-file "
-  tuner_cmd += Bench.cost_file
-  #tuner_cmd += " --gpu-layers "
-  #tuner_cmd += str(Bench.skip_layers)
-  tuner_cmd += " --gpu-layers 0 "
-  tuner_cmd += " --skip-layers \""
-  #tuner_cmd += str(Bench.skip_layer_str) + "\""
-  tuner_cmd += str(skip_layers) + "\""
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-  
-
-
-
-def promiseTunerLoss1(Bench, dir_prefix):
-
-  tuner_runs = Bench.autotuner_runs 
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 30)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 50)
-
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers3)
-
-
-def promiseTunerLoss2(Bench, dir_prefix):
-
-  tuner_runs = Bench.autotuner_runs 
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 20)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 40)
-
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers3)
-
-
-  
-def promiseTunerLoss3(Bench, dir_prefix):
-
-  tuner_runs = Bench.autotuner_runs 
-  
-  skip_layers1 = "0"
-  skip_layers2 = "0_" + select_skip_layers(Bench, 10)
-  skip_layers3 = "0_" + select_skip_layers(Bench, 30)
-  
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers1)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers2)
-  runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3,  2.5, tuner_runs, skip_layers3)
-  
-
-BASELINE = True
-
-  
-def runPromiseBench(Bench):
-
-  # NOTE-IMP: Changing current directory to one with promise binaries
-  dir_prefix = "../build_tuner/"
-  
-
-  if BASELINE:
-    tuner_runs = Bench.autotuner_runs * 2
-    skip_layers = "0"
-    runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers)
-    runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers)
-    runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers)
-    
-  else:
-    
-    promiseTunerLoss1(Bench, dir_prefix)
-
-    promiseTunerLoss2(Bench, dir_prefix)
-
-    promiseTunerLoss3(Bench, dir_prefix)
-
-  
-  
-  
-  """  
-  #tuner_cmd = "python  ../opentuner/autotuner/promise_tuner2.py "
-  tuner_cmd = "python  ../opentuner/autotuner/promise_tuner3.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --start-range "
-  tuner_cmd += str(Bench.start_promise_range)
-  tuner_cmd += " --error-range "
-  #tuner_cmd += str(10)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.result_dir_2 + "/promise_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy - 1.90)
-  tuner_cmd += " --layer-file "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.tensor_desc_file
-  # NOTE: Cost file is new addition - ***NOT*** present in promisetuner1 and promisetuner2
-  tuner_cmd += " --cost-file "
-  tuner_cmd += Bench.cost_file
-  #tuner_cmd += " --gpu-layers "
-  #tuner_cmd += str(Bench.skip_layers)
-  tuner_cmd += " --gpu-layers 0 "
-  tuner_cmd += " --skip-layers \""
-  tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-  #tuner_cmd = "python  ../opentuner/autotuner/promise_tuner2.py "
-  tuner_cmd = "python  ../opentuner/autotuner/promise_tuner3.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --start-range "
-  tuner_cmd += str(Bench.start_promise_range)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(10)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.result_dir_1 + "/promise_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy - 0.95)
-  tuner_cmd += " --layer-file "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.tensor_desc_file
-  tuner_cmd += " --cost-file "
-  tuner_cmd += Bench.cost_file
-  #tuner_cmd += " --gpu-layers "
-  #tuner_cmd += str(Bench.skip_layers)
-  tuner_cmd += " --gpu-layers 0 "
-  tuner_cmd += " --skip-layers \""
-  tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-  
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-  """
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_install_tuner.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_install_tuner.py
deleted file mode 100644
index 6fe682eb4eb715ce3dd290ef77f20a06e5e18856..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_install_tuner.py
+++ /dev/null
@@ -1,164 +0,0 @@
-
-
-import os
-import sys
-import subprocess
-import shutil
-import time
-from benchmarks import batch_id
-import utils
-import global_paths  
-import pareto_utils
-import buildRtConfig
-import genPlots
-from run_devtime_tuner import DevTimeTuner
-import validation
-import knob_pruning
-
-
-class InstallTimeTuner(DevTimeTuner):
-
-  def __init__(self, Bench):
-
-    self.knob_pruning = True
-    self.piped_execution = True
-    self.autotuner_runs = 10000      #  Bench.autotuner_runs
-    self.validation_runs = 15
-    self.abort_after = 3
-    self.conf_threshold = 100
-
-    self.promise_binary = Bench.promise_binary
-    
-    if self.piped_execution:
-      self.binary_path = Bench.piped_binary
-    else:
-      self.binary_path = Bench.promise_binary
-
-    self.num_layers = Bench.num_layers
-    #self.gold_accuracy = Bench.promise_accuracy
-    self.setBaselineAccuracy()
-
-    
-    self.cost_file = global_paths.tensorRT_dir + "/" + Bench.cost_file
-    self.layer_file = global_paths.tensorRT_dir + "/" + Bench.layer_file
-
-    
-    global_knobs_file = global_paths.tensorRT_dir + "/autotuner/data/global_knobs.txt"
-    buildRtConfig.initializeApproxMap(global_knobs_file) # Initialize knobs - configfile gen
-
-
-    if self.knob_pruning == False:     
-      utils.createInstallAndDevKnobs(self.layer_file, global_knobs_file, "install_knobs.txt")
-
-    elif self.knob_pruning == True:
-       pruned_knobs = knob_pruning.getPrunedKnobs(self.promise_binary, self.layer_file, \
-                                     global_knobs_file, self.gold_accuracy, 3)
-       
-       print ("*** pruned_knobs = ", pruned_knobs)
-       utils.dumpKnobsFile(pruned_knobs, "install_knobs.txt")
-  
- 
-    
-    self.layer_knobs = "install_knobs.txt"
-    
-    self.result_dir = global_paths.tensorRT_dir + "/" + Bench.base_dir + \
-                      "/loss_123/" + batch_id + "/install_tuner/"
-
-    # NOTE: maintains total iterations completed - across multiple invocations 
-    self.iterations_completed = 0
-    # Start time for timing autotuner runs
-    self.start_time = 0
-
-
-
-  def validateAccuracyConfigs(self, configurations, accuracy_slack):
-
-    filtered_configs = []
-    for config in configurations:
-      flags = config.flags
-      avg_acc, confidence = validation.getStatisticalConfidence(self.promise_binary, flags, \
-                                                                self.gold_accuracy, \
-                                                                accuracy_slack, self.validation_runs, \
-                                                                self.abort_after)
-
-      print ("avg_acc, confidence = ", avg_acc, confidence)
-      
-      
-      if confidence >= self.conf_threshold:
-        config.avg_accuracy = avg_acc
-        filtered_configs.append(config)
-
-    return filtered_configs
-   
-      
-
-    
-  def dumpValidatedConfigs(self, accuracy_slack):
-
-    #input_dir = self.result_dir + "/high_confidence/"
-    input_dir = self.result_dir + "/high_confidence/"
-    output_dir = self.result_dir + "/pareto/"
-    utils.createDir(output_dir)
-
-    # Get Pareto Points with a "BAND" -- enable_band below is 'True'
-    configurations = pareto_utils.dumpParetoConfigsToDir(input_dir, \
-                                                         output_dir, self.gold_accuracy, True)   
-
-    print ("**** pareto config count = ", len(configurations), "\n")
-    time.sleep(10)
-           
-    #configurations = buildRtConfig.loadConfigsFromDir(input_dir, self.gold_accuracy)
-    bench_layer_composition = utils.getLayerComposition(self.layer_file)
-
-    
-    filtered_configs = self.validateAccuracyConfigs(configurations, accuracy_slack)
-    
-
-    sorted_configurations = sorted(filtered_configs, key=lambda conf: conf.speedup)
-
-
-    config_out_path = self.result_dir + "install_gpu_all_config.txt"
-
-    buildRtConfig.dumpDevConfigsToRTFile(sorted_configurations, \
-                                         config_out_path, bench_layer_composition, \
-                                         self.gold_accuracy, "gpu")
-
-    
-    plot_file_path = self.result_dir + "install_all_conf_plot.png"
-    genPlots.genScatterPlotFromConfigs(sorted_configurations, plot_file_path)
-
-    
-    self.dumpBestConfig(sorted_configurations)
-    
-
-
-    
-  def runDevTuner(self):
-    
-    #self.checkExistingDir()
-    
-    
-    self.startTimer()
-
-    self.setBaselineAccuracy()
-    
-    #self.invokeDevTunerScript(0.8, 2.1, self.autotuner_runs)
-    #self.invokeDevTunerScript(1.5, 2.1, self.autotuner_runs)
-    #self.invokeDevTunerScript(2.1, 2.1, self.autotuner_runs)
-    #self.invokeDevTunerScript(0.9, 2.1, self.autotuner_runs)
-
-    # NOTE: for purposes of comparison with fedtuning
-    self.invokeDevTunerScript(2.1, 2.1, self.autotuner_runs)
-
-    
-    #--- self.dumpParetoFiles()
-    self.dumpValidatedConfigs(2.1)
-
-
-    # NOTE: dumping files for checking experimental parameters for each batch
-    self.dumpReferenceFiles()
-
-    
-    self.endTimer()
-
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_psnr.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_psnr.py
deleted file mode 100644
index 77e70609b89f200e37af1a12348874f9d447d0cd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/run_psnr.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-
-import subprocess
-
-
-def gen30dbFile():
-
-  f = open("psnr.txt", "w+");
-  f.write("30");
-  f.close()
-  
-
-def gen20dbFile():
-
-  f = open("psnr.txt", "w+");
-  f.write("20");
-  f.close()
-
-
-
-def runPSNRTuner(bench_name):
-
-  Bench = bench_tuner_data[bench_name]
-
-  # 20DB run
-  gen20dbFile()  
-  tuner_cmd = "python  ../opentuner/autotuner/approxhpvm_tuner.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.tuner_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_flags)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(Bench.error_range_2)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += Bench.result_dir_2
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.tuner_accuracy)
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-
-  # 30DB run
-  gen30dbFile()
-  tuner_cmd = "python  ../opentuner/autotuner/approxhpvm_tuner.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.tuner_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_flags)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(Bench.error_range_1)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += Bench.result_dir_1
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.tuner_accuracy)
-
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-
-
-def runPSNRPromiseBench(bench_name):
-
-  # NOTE-IMP: Changing current directory to one with promise binaries
-  #os.chdir("../build_promise/")
-  result_dir_prefix = "../build_tuner/"
-  
-  Bench = bench_tuner_data[bench_name]
-
-  # 20db Run
-  gen20dbFile()
-  tuner_cmd = "python  ../opentuner/autotuner/promise_tuner2.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --start-range "
-  tuner_cmd += str(Bench.start_promise_range)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(10)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.result_dir_2 + "/promise_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy)
-  tuner_cmd += " --layer-file "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.tensor_desc_file
-  tuner_cmd += " --gpu-layers 0 "
-  tuner_cmd += " --skip-layers \""
-  tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-  
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-  # 30DB run
-  gen30dbFile()
-  tuner_cmd = "python  ../opentuner/autotuner/promise_tuner2.py "
-  tuner_cmd += " --test-limit "
-  tuner_cmd += str(Bench.autotuner_runs)
-  tuner_cmd += " --binary ./"
-  tuner_cmd += Bench.promise_binary
-  tuner_cmd += " --num-flags "
-  tuner_cmd += str(Bench.num_layers)
-  tuner_cmd += " --start-range "
-  tuner_cmd += str(Bench.start_promise_range)
-  tuner_cmd += " --error-range "
-  tuner_cmd += str(10)
-  tuner_cmd += " --result-dir "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.result_dir_1 + "/promise_tuner/"
-  tuner_cmd += " --accuracy "
-  tuner_cmd += str(Bench.promise_accuracy)
-  tuner_cmd += " --layer-file "
-  tuner_cmd += result_dir_prefix
-  tuner_cmd += Bench.tensor_desc_file
-  tuner_cmd += " --gpu-layers 0 "
-  tuner_cmd += " --skip-layers \""
-  tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-  
-  print (tuner_cmd)
-
-  p = subprocess.Popen(tuner_cmd, shell=True)
-  p.wait()
-
-
-
-
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection.py
deleted file mode 100644
index 399143c357c618aeba1665f5f1b8ecda4097d84c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection.py
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
-    results = []
-    with open(textFile, "r") as f:
-        for line in f:
-            token = line.split("\t")
-            if (len(token) < 7):
-                continue
-            record = (token[0], float(token[1]), float(token[5]), float(token[6]))
-            results.append(record)
-    return results
-
-
-convL1bins =  [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins =  [(0.995298, 1.3643), (0.861066, 1.16279), (0.426857, 0.547827), (0.262645, 0.330186), (0.218984, 0.273731), (0.187878, 0.233872), (0.150619, 0.186512), (0.10106, 0.124477), (0.00035427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins=  [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins=  [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
-    if op == 'tensorConv':
-        return convL1bins, convL2bins
-    if op == 'tensorAdd':
-        return biasL1bins, biasL2bins
-    if op == 'tensorGemm':
-        return gemmL1bins, gemmL2bins
-
-    return None, None
-
-
-def getSwing(Lx, opLxbin):
-    if opLxbin == None:
-        return 0
-    for i, (minT, maxT) in enumerate(opLxbin):
-        if Lx > minT:
-            return i
-
-    return 9
-
-
-
-def getConfiguration(L_thresholds):
-    configuration = []
-    for l in L_thresholds:
-        # L0 is op_type
-        opL1bin, opL2bin = findBinByOp(l[0])
-        # NOTE: L2 is L1 error, L3 is L2 error
-        sL1 = getSwing(l[2], opL1bin)
-        sL2 = getSwing(l[3], opL2bin)
-        if sL1 < 7:
-            sL1 = sL1 + 1
-        if sL2 < 7:
-            sL2 = sL2 + 1
-        configuration.append((l[0], l[1], l[2], l[3], sL1, sL2, max(sL1, sL2)))
-
-    return configuration
-
-
-def displayConfig(config):
-    for c in config:
-        print(c)
-
-def displayMultipleConfigurations(configurations):
-    for f, c in configurations.items():
-        print(f)
-        displayConfig(c)
-        print()
-
-def getConfigFromFile(filename):
-    L_requirements = readDataFromText(filename)
-    config = getConfiguration(L_requirements)
-    return config
-
-
-def getConfigurationsFromDir(dirname):
-    configurations = dict()
-    for f in os.listdir(dirname):
-        configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
-    return configurations
-              
-
-def getLayerWiseTarget(config):
-    target = []
-    for i, op in enumerate(config):
-        if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
-            t = op[6]
-            for j in range(i+1, len(config)):
-                if config[j][0] == 'tensorGemm' or config[j][0] == 'tensorConv':
-                    break
-                t = max(t, config[j][6])
-            target.append(t)
-            t = 0
-
-    return target
-
-
-def dumpLayerWiseTarget(file, targets):
-    with open(file, "w") as f:
-        for name, t in targets.items():
-            f.write(name)
-            f.write(" ")
-            for i in t:
-                f.write(str(i))
-                f.write(" ")
-            f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
-    targets = dict()
-    for f, c in configs.items():
-        targets[f] = [d[6] for d in c]
-
-    return targets
-                
-
-def dumpBenchmarkTargets(name, benchmark_dir):
-    benchmark_targets = dict()
-    error = ['linear', 'log', 'quad']
-    for e in error:
-        results_dir = os.path.join(benchmark_dir, e)
-        configs = getConfigurationsFromDir(results_dir)
-        benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
-    return benchmark_targets
-
-
-
-def dumpTargets(filename, targets):
-    with open(filename, "w") as f:
-        for e, file_configs in targets.items():
-            for name, config in file_configs.items():
-                for c in config:
-                    f.write(str(c))
-                    f.write(" ")
-                f.write("\n")
-
-
-                
-def getLayerSwings(layer_desc, configurations):
-
-    layer_swings = []
-    for i in range(len(configurations)):
-      config_vals = configurations[i]
-      if len(config_vals) == 0:
-        continue
-      
-      layer_index = 0
-      index = 0
-      swing_vals = []
-                   
-      while layer_index < len(layer_desc):
-        if len(layer_desc[layer_index]) == 1:
-          promise_swing = config_vals[index]
-          layer_type = layer_desc[layer_index][0]
-          layer_type = layer_type.strip()
-          print ("****layer_type = ", layer_type)
-          if layer_type != "conv" and layer_type != "dense":
-            promise_swing = -9
-          if layer_type == "depthwise_conv":
-            promise_swing = -9  
-          index += 1
-        else:
-          #print ("index = ", index)
-          # FIXIT: Doesn't look right
-          print (config_vals[index], config_vals[index+1])
-          promise_swing = max(config_vals[index], config_vals[index+1])                  
-          stride = len(layer_desc[layer_index])
-          index += stride
-          
-        swing_vals.append(promise_swing)
-        layer_index += 1  
-        
-      layer_swings.append(swing_vals)
-
-    return layer_swings
-
-                   
-                
-
-def loadLayerDesc(layer_desc_file):
-
-    layer_desc = []
-    f = open(layer_desc_file)
-    for x in f:
-      vals = x.split()
-      layer_desc.append(vals)
-
-    return layer_desc
-      
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
-    layer_desc = loadLayerDesc(layer_desc_file)
-    print (layer_desc)
-
-    file_names = []
-    configurations = []
-    for e, file_configs in targets.items():
-      for name, config in file_configs.items():
-        config_vals = []  
-        for c in config:
-          config_vals.append(c)         
-        print (config_vals)
-
-        configurations.append(config_vals)
-
-        rank = e + "_" +  "_".join(name.split("_")[-2:])
-        file_names.append(rank)
-        
-        
-    # NOTE: get PROMISE swing values corresponding to each layer
-    layer_swings = getLayerSwings(layer_desc, configurations)
-
-    targets_file_path = tuned_result_dir + "/layer_targets.txt"
-    f = open(targets_file_path, "w+")
-
-    for config in layer_swings:
-      index = 0
-      for swing in config:
-        swing_str = ""
-        if swing == 8 or swing == 9:
-          layer_size = len(layer_desc[index])
-          for i in range(layer_size):
-            swing_str += str(swing)
-            if i < layer_size - 1:
-              swing_str += " "
-        elif swing == -9:
-          swing_str += "8"                   
-        else:
-          swing_str += str(swing)
-
-        if index < len(config) - 1:
-          swing_str += ","    
-          
-        f.write(swing_str)
-        index += 1
-        
-      f.write("\n")
-        
-    f.close()
-    
-    print(layer_swings)    
-    return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
-  # Ensuring first conv on GPU
-  for conf in layer_swings:
-    conf[0] = 9
-    
-    
-    
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
-    targets_file_path = tuned_result_dir + "/tensor_targets.txt"
-    targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
-    dumpTargets(targets_file_path, targets)
-    
-    layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
-    replaceFirstLayer(layer_swings)
-    
-    return layer_swings, file_names
-    
-
-# Externally-called function    
-def compute_swing_selection(tuned_result_dir, layer_file):
-   
-    return computeLayerTargets(tuned_result_dir, layer_file)
-
-                            
-        
-                
-if __name__ == "__main__":
-
-    tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition.txt"
-
-    tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition2.txt"
-    computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection2.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection2.py
deleted file mode 100644
index 588edad2a289a67d30c1ade15d4737556327f4fb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/swing_selection2.py
+++ /dev/null
@@ -1,289 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
-    results = []
-    with open(textFile, "r") as f:
-        for line in f:
-            token = line.split("\t")
-            if (len(token) < 7):
-                continue
-            record = (token[0], float(token[1]), float(token[5]), float(token[6]))
-            results.append(record)
-    return results
-
-
-convL1bins =  [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins =  [(0.995298, 1.3643), (0.18, 0.19), (0.14, 0.16), (0.11, 0.12), (0.08, 0.09), (0.06, 0.07), (0.04, 0.05), (0.029, 0.035), (0.00031427528200671077, 0.020199092105031013)]
-#convL2bins =  [(0.995298, 1.3643), (0.18, 0.19), (0.14, 0.16), (0.11, 0.12), (0.08, 0.09), (0.06, 0.07), (0.04, 0.05),     (0.001, 0.004), (0.00031427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins=  [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins=  [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
-    if op == 'tensorConv':
-        return convL1bins, convL2bins
-    if op == 'tensorAdd':
-        return biasL1bins, biasL2bins
-    if op == 'tensorGemm':
-        return gemmL1bins, gemmL2bins
-
-    return None, None
-
-
-def getSwing(Lx, opLxbin):
-    if opLxbin == None:
-        return 0
-    for i, (minT, maxT) in enumerate(opLxbin):
-        if Lx > minT:
-            return i
-
-    return 9
-
-
-
-def getConfiguration(L_thresholds):
-    configuration = []
-    for l in L_thresholds:
-        # L0 is op_type
-        opL1bin, opL2bin = findBinByOp(l[0])
-        # NOTE: L2 is L1 error, L3 is L2 error
-        # only using L2 for image pipelines
-        sL2 = getSwing(l[3], opL2bin)
-        if sL2 < 7:
-            sL2 = sL2 + 1
-        configuration.append((l[0], l[1], l[2], l[3], sL2, sL2, sL2))
-
-    return configuration
-
-
-def displayConfig(config):
-    for c in config:
-        print(c)
-
-def displayMultipleConfigurations(configurations):
-    for f, c in configurations.items():
-        print(f)
-        displayConfig(c)
-        print()
-
-def getConfigFromFile(filename):
-    L_requirements = readDataFromText(filename)
-    config = getConfiguration(L_requirements)
-    return config
-
-
-def getConfigurationsFromDir(dirname):
-    configurations = dict()
-    for f in os.listdir(dirname):
-        configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
-    return configurations
-              
-
-def getLayerWiseTarget(config):
-    target = []
-    for i, op in enumerate(config):
-        if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
-            t = op[6]
-            target.append(t)
-         
-    return target
-
-
-def dumpLayerWiseTarget(file, targets):
-    with open(file, "w") as f:
-        for name, t in targets.items():
-            f.write(name)
-            f.write(" ")
-            for i in t:
-                f.write(str(i))
-                f.write(" ")
-            f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
-    targets = dict()
-    for f, c in configs.items():
-        targets[f] = [d[6] for d in c]
-
-    return targets
-                
-
-def dumpBenchmarkTargets(name, benchmark_dir):
-    benchmark_targets = dict()
-    error = ['linear', 'log', 'quad']
-    for e in error:
-        results_dir = os.path.join(benchmark_dir, e)
-        configs = getConfigurationsFromDir(results_dir)
-        benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
-    return benchmark_targets
-
-
-def dumpTargets(filename, targets):
-    with open(filename, "w") as f:
-        for e, file_configs in targets.items():
-            for name, config in file_configs.items():
-                for c in config:
-                    f.write(str(c))
-                    f.write(" ")
-                f.write("\n")
-
-
-                
-def getLayerSwings(layer_desc, configurations):
-
-    layer_swings = []
-    for i in range(len(configurations)):
-      config_vals = configurations[i]   
-      layer_index = 0
-      index = 0
-      swing_vals = []
-                   
-      while layer_index < len(layer_desc):
-        if len(layer_desc[layer_index]) == 1:
-          promise_swing = config_vals[index]
-          layer_type = layer_desc[layer_index] 
-          if layer_type != "conv" and layer_type != "dense":
-            promise_swing = -9
-          index += 1
-        else:
-          print (config_vals[index], config_vals[index+1])
-          promise_swing = max(config_vals[index], config_vals[index+1])                  
-          stride = len(layer_desc[layer_index])
-          #print ("*stride = ", stride)
-          index += stride
-          
-        swing_vals.append(promise_swing)
-        layer_index += 1  
-        
-      layer_swings.append(swing_vals)
-
-    return layer_swings
-
-                   
-                
-
-def loadLayerDesc(layer_desc_file):
-
-    layer_desc = []
-    f = open(layer_desc_file)
-    for x in f:
-      vals = x.split()
-      layer_desc.append(vals)
-
-    return layer_desc
-      
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
-    layer_desc = loadLayerDesc(layer_desc_file)
-    print (layer_desc)
-
-    file_names = []
-    configurations = []
-    for e, file_configs in targets.items():
-      for name, config in file_configs.items():
-        config_vals = []  
-        for c in config:
-          config_vals.append(c)         
-        print (config_vals)
-
-        configurations.append(config_vals)
-
-        rank = e + "_" +  "_".join(name.split("_")[-2:])
-        file_names.append(rank)
-        
-        
-    # NOTE: get PROMISE swing values corresponding to each layer
-    layer_swings = getLayerSwings(layer_desc, configurations)
-
-    targets_file_path = tuned_result_dir + "/layer_targets.txt"
-    f = open(targets_file_path, "w+")
-
-    for config in layer_swings:
-      index = 0
-      for swing in config:
-        swing_str = ""
-        if swing == 8 or swing == 9:
-          layer_size = len(layer_desc[index])
-          for i in range(layer_size):
-            swing_str += str(swing)
-            if i < layer_size - 1:
-              swing_str += " "
-        elif swing == -9:
-          swing_str += "8"                   
-        else:
-          swing_str += str(swing)
-
-        if index < len(config) - 1:
-          swing_str += ","    
-          
-        f.write(swing_str)
-        index += 1
-        
-      f.write("\n")
-        
-    f.close()
-    
-    print(layer_swings)    
-    return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
-  # Ensuring first conv on GPU
-  for conf in layer_swings:
-    conf[0] = 9
-    
-    
-    
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
-    targets_file_path = tuned_result_dir + "/tensor_targets.txt"
-    targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
-    dumpTargets(targets_file_path, targets)
-    
-    layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
-    replaceFirstLayer(layer_swings)
-    
-    return layer_swings, file_names
-    
-
-# Externally-called function    
-def compute_swing_selection2(tuned_result_dir, layer_file):
-   
-    return computeLayerTargets(tuned_result_dir, layer_file)
-
-                            
-        
-                
-if __name__ == "__main__":
-
-    tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition.txt"
-
-    tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition2.txt"
-    computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/utils.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/utils.py
deleted file mode 100644
index 9ff3622d13c1c0c65a21938d487d968efae428f0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/utils.py
+++ /dev/null
@@ -1,296 +0,0 @@
-
-
-import os
-import sys
-import subprocess
-from benchmarks import batch_id
-from global_paths import tensorRT_dir
-
-
-
-def createDir(dir_path):
-
-  try:
-    if not os.path.exists(dir_path):      
-      os.mkdir(dir_path)
-  except:
-    print ("!ERROR: Could NOT create result directory = ", dir_path)
-    sys.exit(-1)      
-
-    
-def createResultDirs(benchmarks):
-
-  for bench_name in benchmarks:
-    Bench = benchmarks[bench_name]
-
-    print ("Base Directory: ", Bench.base_dir , "BatchId = ", batch_id)
-        
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_1")    
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_2")    
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_3")
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_123/" + batch_id)
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_1/" + batch_id)
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_2/" + batch_id)    
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_3/" + batch_id)
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_123/" + batch_id + "/devtuner/")
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_1/" + batch_id + "/devtuner/" )    
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_2/" + batch_id + "/devtuner/")    
-    createDir(tensorRT_dir + Bench.base_dir + "/loss_3/" + batch_id + "/devtuner/")
-
-
-
-
-    
-def genBaselineConfig(flags_file_path, default_flag, num_layers):
-
-  f = open(flags_file_path, "w+")
-  for i in range(num_layers):
-    f.write(str(default_flag) + "\n")
-
-  f.close()
-
-
-
-def readAccuracy(accuray_res_file):
-    
-    file = open(accuray_res_file, "r")
-    accuracy_str = file.read()
-    file.close()
-    accuracy = 0     #  float(accuracy_str)
-    
-    try:
-      accuracy = float(accuracy_str)
-    except:
-      print("ERROR: Reading Accuray file - Aborting.... \n")
-      sys.exit(-1)
-      
-    
-    print ("*Configuration Accuracy = ", accuracy)
-    return accuracy
-
-  
-
-#**** Exported Function ****/
-def getBaselineAccuracy(binary_path, num_layers):
-
-    genBaselineConfig("promise_flags", 11, num_layers)
-    
-    run_cmd = "./" + binary_path
-    print (run_cmd)
-
-    p = subprocess.Popen(run_cmd, shell=True)
-    p.wait()
-    
-    return readAccuracy("final_accuracy")
-
-
-
-def getLayerComposition(layer_composition_file):
-
-    layer_desc = []
-    f = open(layer_composition_file)
-    for x in f:
-      vals = x.split()
-      layer_desc.append(vals)
-
-    return layer_desc
-      
-
-def debug_print(str):
-
-  debug_flag = False
-  
-  if debug_flag == True:
-    print (str)
-
-
-    
-def readOpKnobs(global_knobs_file, op_type, analysis_mode):
-
-  knobs_file = open(global_knobs_file, "r")
-
-  tuning_knobs = []
-  for knob in knobs_file:
-    toks = knob.split("\t")
-    if op_type in toks[-1] and analysis_mode in toks[-2]:
-      knob_id = toks[0].split(",")[1]
-      tuning_knobs.append(knob_id)
-
-  return tuning_knobs
-
-
-
-def readConvKnobs(global_knobs_file, analysis_mode):
-
-  return readOpKnobs(global_knobs_file, "conv", analysis_mode)
-        
-
-def readFCKnobs(global_knobs_file, analysis_mode):
-  
-  return readOpKnobs(global_knobs_file, "fc", analysis_mode)
-
-  
-def readRedKnobs(global_knobs_file, analysis_mode):
-
-  return readOpKnobs(global_knobs_file, "red", analysis_mode)
-
-    
-
-
-def createDevKnobs(layer_file, global_knobs_file, out_file):
-
-  f = open(layer_file, "r")
-
-  conv_knobs = readConvKnobs(global_knobs_file, "dev")
-  fc_knobs = readFCKnobs(global_knobs_file, "dev")
-  red_knobs = readRedKnobs(global_knobs_file, "dev")
-
-  print (conv_knobs, fc_knobs, red_knobs)
-
-  f_out = open(out_file, "w+")
-  
-  for x in f:
-    if "conv" in x:
-      f_out.write(",".join(conv_knobs) + "\n")  
-    if "dense" in x:
-      f_out.write(",".join(fc_knobs) + "\n")  
-    if "red" in x:
-      f_out.write(",".join(red_knobs) + "\n")  
-
-  f_out.close()
-  
-
-
-def removePromiseKnobs(conv_knobs):
-
-  promise_knobs = ["1", "2", "3", "4", "5", "6", "7"]
-  conv_knobs2 = []
-
-  for knob in conv_knobs:
-    if knob not in promise_knobs:
-      conv_knobs2.append(knob)
-      
-  return conv_knobs2
-
-
-  
-def createInstallAndDevKnobs(layer_file, global_knobs_file, out_file):
-
-  f = open(layer_file, "r")
-
-  conv_knobs_dev = readConvKnobs(global_knobs_file, "dev")
-  fc_knobs_dev = readFCKnobs(global_knobs_file, "dev")
-  red_knobs_dev = readRedKnobs(global_knobs_file, "dev")
-
-  conv_knobs_install = readConvKnobs(global_knobs_file, "install")
-  fc_knobs_install = readFCKnobs(global_knobs_file, "install")
-  red_knobs_install = readRedKnobs(global_knobs_file, "install")
-
-
-  
-  #conv_knobs_dev.remove("11") # remove FP32 from install-time tuning
-  #fc_knobs_dev.remove("11") # remove FP32 from install-time tuning
-  #red_knobs_dev.remove("11") # remove FP32 from install-time tuning  
-  
-  conv_knobs = conv_knobs_dev + conv_knobs_install 
-  fc_knobs = fc_knobs_dev + fc_knobs_install 
-  red_knobs = red_knobs_dev + red_knobs_install 
-  
-  print (conv_knobs, fc_knobs, red_knobs)
-
-  #sys.exit(0)
-  
-  f_out = open(out_file, "w+")
-
-  ind = 0
-  for x in f:
-    if "conv" in x:
-      layer_conv_knobs = conv_knobs
-      if ind == 0:
-        layer_conv_knobs = removePromiseKnobs(conv_knobs)
-      f_out.write(",".join(layer_conv_knobs) + "\n")  
-    if "dense" in x:
-      f_out.write(",".join(fc_knobs) + "\n")  
-    if "red" in x:
-      f_out.write(",".join(red_knobs) + "\n")
-
-    ind += 1
-
-  f_out.close()
-  
-
-
-
-  
-def getInstallAndDevKnobs(layer_file, global_knobs_file):
-
-  f = open(layer_file, "r")
-
-  conv_knobs_dev = readConvKnobs(global_knobs_file, "dev")
-  fc_knobs_dev = readFCKnobs(global_knobs_file, "dev")
-  red_knobs_dev = readRedKnobs(global_knobs_file, "dev")
-
-  conv_knobs_install = readConvKnobs(global_knobs_file, "install")
-  fc_knobs_install = readFCKnobs(global_knobs_file, "install")
-  red_knobs_install = readRedKnobs(global_knobs_file, "install")
-
-  
-  conv_knobs = conv_knobs_dev + conv_knobs_install 
-  fc_knobs = fc_knobs_dev + fc_knobs_install 
-  red_knobs = red_knobs_dev + red_knobs_install 
-  
-  print (conv_knobs, fc_knobs, red_knobs)
-
-
-  bench_knobs = []
-  
-  ind = 0
-  for x in f:
-    if "conv" in x:
-      layer_conv_knobs = conv_knobs
-      if ind == 0:
-        layer_conv_knobs = removePromiseKnobs(conv_knobs)
-      bench_knobs.append(layer_conv_knobs)  
-    if "dense" in x:
-      bench_knobs.append(fc_knobs)    
-    if "red" in x:
-      bench_knobs.append(red_knobs)    
-
-    ind += 1
-
-
-  return bench_knobs
-  
-
-
-
-
-
-def dumpKnobsFile(knobs, out_file):
-
-
-  f_out = open(out_file, "w+")
-
-  for layer_knobs in knobs:
-      f_out.write(",".join(layer_knobs) + "\n")
-      
-  f_out.close()
-
-  
-
-
-  
-
-
-  
-if __name__ == "__main__":
-
-
-  #createDevKnobs("../data/alexnet2/alexnet2_layers.txt", \
-  #               "../data/global_knobs.txt", "dev_knobs.txt")
-
-
-  knobs = getInstallAndDevKnobs("../data/alexnet2/alexnet2_layers.txt", \
-                                "../data/global_knobs.txt")
-
-  print ("*** knobs = ", knobs)
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/validation.py b/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/validation.py
deleted file mode 100644
index c334b2b319965ad8a866a2ec9d86137e8a5c6a28..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/autotuner/tuner_driver_src/validation.py
+++ /dev/null
@@ -1,802 +0,0 @@
-
-
-import os
-import sys
-import subprocess
-import shutil
-from compute_confs import computePSNRBenchSwings, computeBenchSwings
-from buildRtConfig import loadConfigData
-
-
-
-def getLayerString(layer_swings):
-
-  index = 0
-  layer_string = ""
-  for swing in layer_swings:
-    layer_string += str(swing)
-    if index < len(layer_swings) - 1:
-      layer_string += ","
-  return layer_string
-
-
-
-def testValidationRun(Bench, validation_dir, layer_swings, threshold, rank_str):
-
-  #### FIXME
-  #os.chdir("../build_promise/")
-
-  validation_acc = Bench.validation_accuracy
-  target_acc = validation_acc - threshold
-
-  validation_binary = Bench.validation_binary
-
-  # Write to promise_flags
-  fout = open("promise_flags", "w+")
-  for swing in layer_swings:
-    int_swing = int(swing)
-    if int_swing > 0:
-      fout.write(str(swing) + "\n")
-  fout.close()
-  
-  # Execute Validation Run
-  p = subprocess.Popen("./" + validation_binary, shell=True)
-  p.wait()
-
-  f = open("run_accuracies.txt")
-  index = 0.0
-  unsuccessful = 0.0
-  sum_acc = 0.0
-  for x in f:
-    x = x.strip()
-    acc = float(x)
-    if acc < target_acc:
-      unsuccessful += 1
-    index += 1
-    sum_acc += acc
-
-  f.close()
-  
-  confidence = ( (index - unsuccessful) / index) * 100.0
-  print ("run_confidence = ", confidence) 
-  avg_acc = sum_acc / index
-
-  out_fname = validation_dir + validation_binary + "_" + str(avg_acc)
-  shutil.copy("run_accuracies.txt", out_fname + "_" + rank_str)
-
-  layer_string = getLayerString(layer_swings)
-  f = open(out_fname, "w")
-  f.write("config:\t" + layer_string + "\n")
-  f.write("confidence:\t" + str(confidence) + "\n")
-  f.close()
-
-  return confidence
-
-
-
-
-def testPromiseRun(Bench, layer_swings, threshold):
-
-  #### FIXME
-  #os.chdir("../build_promise/")
-
-  validation_acc = Bench.validation_accuracy
-  target_acc = validation_acc - threshold
-
-  validation_binary = Bench.validation_binary
-
-  # Write to promise_flags
-  fout = open("promise_flags", "w+")
-  for swing in layer_swings:
-    int_swing = int(swing)
-    if int_swing > 0:
-      fout.write(str(swing) + "\n")
-  fout.close()
-  
-  # Execute Validation Run
-  p = subprocess.Popen("./" + validation_binary, shell=True)
-  p.wait()
-
-  f = open("run_accuracies.txt")
-  index = 0.0
-  unsuccessful = 0.0
-  sum_acc = 0.0
-  for x in f:
-    x = x.strip()
-    acc = float(x)
-    if acc < target_acc:
-      unsuccessful += 1
-    index += 1
-    sum_acc += acc
-
-  f.close()
-  
-  confidence = ( (index - unsuccessful) / index) * 100.0
-  print ("run_confidence = ", confidence) 
-  avg_acc = sum_acc / index
-
-  return confidence
-
-
-
-
-
-  
-  
-
-def dumpConfigConfidence(configs, confidence_list,
-                         result_dir, layer_desc_file):
-
-    #### FIXME
-    #os.chdir("../build_tuner/")
-
-    layer_desc = loadLayerDesc(layer_desc_file)
-    print (layer_desc)
-
-    f = open(result_dir + "/conf_confidences.txt", "w+")
-
-    count = 0
-    for config in configs:
-      index = 0
-      for swing in config:
-        swing_str = ""
-        if swing == 8 or swing == 9:
-          layer_size = len(layer_desc[index])
-          for i in range(layer_size):
-            swing_str += str(swing)
-            if i < layer_size - 1:
-              swing_str += " "
-        elif swing == -9:
-          swing_str += "8"                   
-        else:
-          swing_str += str(swing)
-
-        if index < len(config) - 1:
-          swing_str += ","              
-        f.write(swing_str)
-        
-        index += 1
-
-      f.write("\t" + str(confidence_list[count]))  
-      f.write("\n")
-      count +=1
-      
-    f.close()
-  
-  
-  
-
-def dumpValidatedConfigs(configs, result_dir, layer_desc_file,
-                         output_file_name):
-
-    os.chdir("../build_tuner/")
-
-    layer_desc = loadLayerDesc(layer_desc_file)
-    print (layer_desc)
-
-    f = open(result_dir + "/" + output_file_name, "w+")
-
-    for config in configs:
-      index = 0
-      for swing in config:
-        swing_str = ""
-        if swing == 8 or swing == 9:
-          layer_size = len(layer_desc[index])
-          for i in range(layer_size):
-            swing_str += str(swing)
-            if i < layer_size - 1:
-              swing_str += " "
-        elif swing == -9:
-          swing_str += "8"                   
-        else:
-          swing_str += str(swing)
-
-        if index < len(config) - 1:
-          swing_str += ","              
-        f.write(swing_str)
-        
-        index += 1      
-      f.write("\n")
-    f.close()
-  
-
-
-def dumpRankings(validated_ranks, result_dir, rank_file):
-
-    os.chdir("../build_tuner/")
-    f = open(result_dir + "/" + rank_file, "w+")
-    for rank in validated_ranks:
-      f.write(rank + "\n")
-
-    f.close()
-  
- 
-
-
-def replaceFP32Configs(loss_confs1, loss_confs2):
-  
-  for swing_conf in loss_confs1:
-    for i in range(0, len(swing_conf)):
-      if swing_conf[i] == 9:
-        swing_conf[i] = 8
-      if i == len(swing_conf) - 1:
-        swing_conf[i] = 7
-
-  for swing_conf in loss_confs2:
-    for i in range(0, len(swing_conf)):
-      if swing_conf[i] == 9:
-        swing_conf[i] = 8
-      if i == len(swing_conf) - 1:     
-        swing_conf[i] = 7
-  
-
-  return loss_confs1, loss_confs2      
-
-
-
-def replaceGPUConfigs(Bench, loss_confs1, loss_confs2):
-
-  skip_layer_str = Bench.skip_layer_str
-  layer_ids = skip_layer_str.split("_")
-  skip_layers = []
-  for layer_id in layer_ids:
-    skip_layers.append(int(layer_id))
-    
-  
-  for swing_conf in loss_confs1:
-    for i in range(0, len(swing_conf)):
-      if i in skip_layers and swing_conf[i] < 8:
-        swing_conf[i] = 8
-   
-  for swing_conf in loss_confs2:
-    for i in range(0, len(swing_conf)):
-      if i in skip_layers and swing_conf[i] < 8:
-        swing_conf[i] = 8
-   
-
-  return loss_confs1, loss_confs2      
-
-
-
-
-def runBenchValidation(Bench):
-
-  #Bench = bench_tuner_data[bench_name]
-
-  loss_confs, conf_ranks = computeBenchSwings(Bench)
-  loss1_confs = loss_confs[0]
-  loss2_confs = loss_confs[1]
-  conf_ranks1 = conf_ranks[0]
-  conf_ranks2 = conf_ranks[1]
-
-  #loss1_confs, loss2_confs = replaceFP32Configs(loss1_confs, loss2_confs)
-  
-
-  validation_dir_1 = "../build_tuner/" + Bench.result_dir_1 + "/validation_runs/"
-  if not os.path.exists(validation_dir_1):
-    os.mkdir(validation_dir_1)
-    
-  validation_dir_2 = "../build_tuner/" +  Bench.result_dir_2 + "/validation_runs/"
-  if not os.path.exists(validation_dir_2):
-    os.mkdir(validation_dir_2)
-
-
-  ind = 0
-  validated_confs1 = []
-  validated_ranks1 = []
-  failed_confs1 = []
-  confidences1 = []
-  for layer_swings in loss1_confs:
-    print ("len(layer_Swings)  = ", len(layer_swings), "\n")
-    confidence = testValidationRun(Bench, validation_dir_1,
-                                   layer_swings, 1.0, conf_ranks1[ind])
-    if confidence >= 95:
-      validated_confs1.append(layer_swings)
-      confidences1.append(confidence)
-      validated_ranks1.append(conf_ranks1[ind])
-    else:
-      failed_confs1.append(layer_swings)
-    ind += 1
-    
-
-  ind = 0
-  validated_confs2 = []
-  validated_ranks2 = []
-  failed_confs2 = []
-  confidences2 = []
-  for layer_swings in loss2_confs:
-    confidence = testValidationRun(Bench, validation_dir_2, layer_swings, 2.0, conf_ranks2[ind])
-    if confidence >= 92:
-      validated_confs2.append(layer_swings)
-      confidences2.append(confidence)
-      validated_ranks2.append(conf_ranks2[ind])
-    else:
-      failed_confs2.append(layer_swings)
-    ind += 1  
-
-  dumpValidatedConfigs(validated_confs1, Bench.result_dir_1,
-                       Bench.layer_file, "validated_confs.txt")                      
-  dumpValidatedConfigs(validated_confs2, Bench.result_dir_2,
-                       Bench.layer_file, "validated_confs.txt")
-
-  dumpValidatedConfigs(failed_confs1, Bench.result_dir_1,
-                       Bench.layer_file, "failed_confs.txt")
-  dumpValidatedConfigs(failed_confs2, Bench.result_dir_2,
-                       Bench.layer_file, "failed_confs.txt")
-
-  dumpRankings(validated_ranks1, Bench.result_dir_1, "validated_ranks.txt")
-  dumpRankings(validated_ranks2, Bench.result_dir_2, "validated_ranks.txt")
-
-  dumpConfigConfidence(validated_confs1, confidences1,
-                       Bench.result_dir_1, Bench.layer_file)
-
-  dumpConfigConfidence(validated_confs2, confidences2,
-                       Bench.result_dir_2, Bench.layer_file)
-
-  
-  print (validated_confs1)  
-  print (validated_confs2)
-
-
-
-def readPromiseResults(loss1_file, loss2_file):
-
-  loss_confs = []
-  loss1_confs = []
-  f1 = open(loss1_file)
-  for x in f1:
-    print (x)
-    swing_toks = x.split(",")
-    swing_list = []
-    for swing_str in swing_toks:    
-      swing_val = int(swing_str.split(" ")[0])
-      swing_list.append(swing_val)
-    loss1_confs.append(swing_list)  
-
-  loss_confs.append(loss1_confs)
-    
-  loss2_confs = []
-  f2 = open(loss1_file)
-  for x in f2:
-    swing_toks = x.split(",")
-    swing_list = []
-    for swing_str in swing_toks:    
-      swing_val = int(swing_str.split(" ")[0])
-      swing_list.append(swing_val)
-    loss2_confs.append(swing_list)  
-    
-  loss_confs.append(loss2_confs)
-
-  return loss_confs
-
-
-
-
-
-
-def readPromiseResults2(loss1_file, loss2_file, layer_file):
-
-  layer_desc = loadLayerDesc(layer_file)
-  
-  loss_confs = []
-  loss1_confs = []
-  f1 = open(loss1_file)
-  for x in f1:
-    print (x)
-    swing_toks = x.split(",")
-    swing_list = []
-
-    it = 0
-    for swing_str in swing_toks:    
-      swing_val = int(swing_str.split(" ")[0])
-      if "conv" in layer_desc[it] or "dense" in layer_desc[it]:
-        swing_list.append(swing_val)
- 
-      it += 1
-      
-    loss1_confs.append(swing_list)  
-
-  loss_confs.append(loss1_confs)
-    
-  loss2_confs = []
-  f2 = open(loss1_file)
-  for x in f2:
-    swing_toks = x.split(",")
-    swing_list = []
-
-    it = 0
-    for swing_str in swing_toks:    
-      swing_val = int(swing_str.split(" ")[0])
-      if "conv" in layer_desc[it] or "dense" in layer_desc[it]:
-        swing_list.append(swing_val)
-
-      it += 1
-      
-    loss2_confs.append(swing_list)  
-    
-  loss_confs.append(loss2_confs)
-
-  return loss_confs
-
-
-
-
-
-def readPromiseResults3(result_dir):
-  
-  loss_confs = []
-  # NOTE: Second parameter is ignored
-  config_arr = loadConfigData(result_dir, 100)
-
-  for config in config_arr:
-    loss_confs.append(config.flags)
-
-  return loss_confs  
-  
-  
-
-
-
-
-
-
-def runPromiseBenchValidation(Bench):
-
-  
-  dir_prefix = "../build_tuner/"
-  #Bench = bench_tuner_data[bench_name]
-  #loss_confs = readPromiseResults(dir_prefix + Bench.loss1_result_file, dir_prefix + Bench.loss2_result_file)
-  loss_confs = readPromiseResults2(dir_prefix + Bench.loss1_result_file, \
-                                   dir_prefix + Bench.loss2_result_file, Bench.layer_file)
-  
-  loss1_confs = loss_confs[0]
-  loss2_confs = loss_confs[1]
- 
-  ind = 0
-  validated_confs1 = []
-  failed_confs1 = []
-  for layer_swings in loss1_confs:
-    confidence = testPromiseRun(Bench, layer_swings, 1.0)
-    if confidence >= 95:
-      validated_confs1.append(layer_swings)
-    else:
-      failed_confs1.append(layer_swings)
-    ind += 1
-    
-
-  ind = 0
-  validated_confs2 = []
-  failed_confs2 = []
-  for layer_swings in loss2_confs:
-    confidence = testPromiseRun(Bench, layer_swings, 2.0)
-    if confidence >= 95:
-      validated_confs2.append(layer_swings)
-    else:
-      failed_confs2.append(layer_swings)
-    ind += 1  
-
-
-  dumpValidatedConfigs(validated_confs1, Bench.result_dir_1,
-                       Bench.layer_file, "promise_validated_confs.txt")                      
-  dumpValidatedConfigs(validated_confs2, Bench.result_dir_2,
-                       Bench.layer_file, "promise_validated_confs.txt")
-
-  dumpValidatedConfigs(failed_confs1, Bench.result_dir_1,
-                       Bench.layer_file, "promise_failed_confs.txt")
-  dumpValidatedConfigs(failed_confs2, Bench.result_dir_2,
-                       Bench.layer_file, "promise_failed_confs.txt")
-
-
-
-
-  
-def copyValidatedConf(result_dir, validated_confs):
-
-  src_dir = result_dir + "/promise_tuner/high_confidence/"
-  dest_dir = result_dir + "/promise_tuner/validated/"
-
-  if not os.path.isdir(dest_dir):
-    os.mkdir(dest_dir)
-
-  for fname in validated_confs:
-    shutil.copy(src_dir + fname, dest_dir + fname)  
-
-  
-
-def copyFailedConf(result_dir, failed_confs):
-
-  src_dir = result_dir + "/promise_tuner/high_confidence/"
-  dest_dir = result_dir + "/promise_tuner/failed/"
-
-  if not os.path.isdir(dest_dir):
-    os.mkdir(dest_dir)
-
-  for fname in failed_confs:
-    shutil.copy(src_dir + fname, dest_dir + fname)  
-    
-  
-  
-
-def validateConfigs(Bench, result_dir, configs_arr, acc_thresh):
-
-  validated_confs = []
-  failed_confs = []
-  for conf in configs_arr:
-    layer_swings = conf.flags
-    confidence = testPromiseRun(Bench, layer_swings, acc_thresh)
-    if confidence >= 95:
-      validated_confs.append(conf.fname)
-    else:
-      failed_confs.append(conf.fname)
-
-    
-  copyValidatedConf(result_dir, validated_confs)                    
-  copyFailedConf(result_dir, failed_confs) 
-
-
-
-
-
-                 
-
-def runPromiseBenchValidation2(Bench):
-
-  
-  config_arr1 = loadConfigData(Bench.result_dir_1, 100)
-  config_arr2 = loadConfigData(Bench.result_dir_2, 100)
-  config_arr3 = loadConfigData(Bench.result_dir_3, 100)
-
-  
-  validateConfigs(Bench, Bench.result_dir_1, config_arr1, 1.0)
-  validateConfigs(Bench, Bench.result_dir_2, config_arr2, 2.0)
-  validateConfigs(Bench, Bench.result_dir_3, config_arr3, 3.0)
-  
-
-
-
-### NOTE: Algo Tuner Validation routines
-
-
-  
-
-
-def addAccuracyLoss(dest_file, accuracy_loss):
-
-  f = open(dest_file, "r")
-  file_str = ""
-  ind = 0
-  for x in f:
-    line_str = x
-    if ind == 0:
-      line_str = x.replace("\n", "")
-      line_str += "\tvalidation_loss=" + str(accuracy_loss) + "\n"
-
-    file_str += line_str
-    ind += 1    
-  f.close()
-
-  
-  f_out = open(dest_file, "w+")
-  f_out.write(file_str)
-  f_out.close()
-
-
-
-   
-def dumpValidConfigs(result_dir, validated_confs, src_dir):
-
-  src_dir = result_dir + "/algo_tuner/" + src_dir + "/" # high_confidence/"
-  dest_dir = result_dir + "/algo_tuner/validated/"
-
-  if not os.path.isdir(dest_dir):
-    os.mkdir(dest_dir)
-
-  for (fname, accuracy_loss) in validated_confs:
-    dest_file = dest_dir + fname  
-    shutil.copy(src_dir + fname, dest_file)  
-    addAccuracyLoss(dest_file, accuracy_loss)
-  
-  
-
-def dumpFailedConfigs(result_dir, failed_confs, src_dir):
-
-  src_dir = result_dir + "/algo_tuner/" + src_dir + "/" # high_confidence/"
-  dest_dir = result_dir + "/algo_tuner/failed/"
-
-  if not os.path.isdir(dest_dir):
-    os.mkdir(dest_dir)
-
-  for (fname, accuracy_loss) in failed_confs:
-    dest_file = dest_dir + fname  
-    shutil.copy(src_dir + fname, dest_file) 
-    addAccuracyLoss(dest_file, accuracy_loss)
-
-  
-  
- 
-def readAccuracy(file_name):
-  
-  file = open(file_name, "r")
-  file_str = file.read()
-  file.close()
-
-  accuracy = 0.0
-  try:
-    accuracy = float(file_str)
-  except:
-    print ("ERROR: Reading accuracy from 'final_accuracy' file")
-    sys.exit(0)
-  
-  print ("accuracy = ", accuracy)
-  return accuracy
-
-def getBaselineConfig(num_layers):
-
-  fp32_swing = 11
-  swings = []
-  
-  for i in range(num_layers):
-    swings.append(str(fp32_swing))
-    
-  return swings
-
-
-
-def readConfidence(target_acc):
-
-  f = open("run_accuracies.txt")
-  index = 0.0
-  unsuccessful = 0.0
-  sum_acc = 0.0
-  for x in f:
-    x = x.strip()
-    acc = float(x)
-    if acc < target_acc:
-      unsuccessful += 1
-    index += 1
-    sum_acc += acc
-
-  f.close()
-  
-  confidence = ( (index - unsuccessful) / index) * 100.0
-  print ("run_confidence = ", confidence) 
-  avg_acc = sum_acc / index
-
-  return confidence, avg_acc
-
-
-
-def invokeBinary(binary_path, layer_swings, runs, input_size, offset, target_acc): # threshold):
-
-  default_skip = 4
-  # Write to promise_flags
-  fout = open("promise_flags", "w+")
-  for swing in layer_swings:
-    int_swing = int(swing)
-    if int_swing > 0:
-      fout.write(str(swing) + "\n")
-  fout.close()
-
-  run_cmd = "./" + binary_path + " " + str(runs) + " " + str(target_acc) + " " + str(default_skip) + " " + str(input_size) + " " + str(offset)
-  # Execute Validation Run
-  #p = subprocess.Popen("./" + validation_binary, shell=True)
-
-  p = subprocess.Popen(run_cmd, shell=True)
-  p.wait()
-  
-
-
-
-
-def validateAlgoConfigs(binary_path, result_dir, configs_arr, gold_acc, \
-                        acc_thresh, runs, src_dir = "high_confidence"):
-
-  # NOTE: Use confidence target as 95%
-  confidence_target = 95
-  # NOTE: 1 run sufficient for software approximations
-  
-  validated_confs = []
-  failed_confs = []
-
-  #validation_acc = Bench.validation_accuracy
-  target_acc = gold_acc - acc_thresh
-  
-  for conf in configs_arr:
-    layer_swings = conf.flags
-    invokeBinary(binary_path, layer_swings, runs, 2000, 8000, target_acc) 
-    confidence, avg_acc = readConfidence(target_acc)
-    
-    accuracy_loss = gold_acc - avg_acc      
-    if confidence >= confidence_target:
-      validated_confs.append((conf.fname, accuracy_loss))
-    else:
-      failed_confs.append((conf.fname, accuracy_loss))
-
-
-  dumpValidConfigs(result_dir, validated_confs, src_dir)                    
-  dumpFailedConfigs(result_dir, failed_confs, src_dir) 
-  
-
-
-
-def runAlgoBenchValidate(Bench):
-
-  num_layers = Bench.num_layers
-  base_conf = getBaselineConfig(num_layers)
-  # Path to binary to run
-  binary_path = Bench.promise_binary
-  # NOTE: 'target_acc' passed 0.0 since unused for baseline run
-  invokeBinary(binary_path, base_conf, 1, 2000, 8000, 0.0)
-  gold_acc = readAccuracy("final_accuracy")
-
-  
-  loss1_dir = Bench.result_dir_1
-  loss2_dir = Bench.result_dir_2
-  loss3_dir = Bench.result_dir_3
-
-  loss1_configs = loadConfigData(loss1_dir, 100)
-  loss2_configs = loadConfigData(loss2_dir, 100)
-  loss3_configs = loadConfigData(loss3_dir, 100)
-
-  runs = 1
-  validateAlgoConfigs(binary_path, loss1_dir, loss1_configs, gold_acc, 1.0, runs)
-  validateAlgoConfigs(binary_path, loss2_dir, loss2_configs, gold_acc, 2.0, runs)
-  validateAlgoConfigs(binary_path, loss3_dir, loss3_configs, gold_acc, 3.0, runs)
-  
-
-
-
-
-
-def getStatisticalConfidence(binary_path, layer_swings, \
-                             gold_accuracy, accuracy_slack, \
-                             total_runs, abort_after):
-
-
-  target_acc = gold_accuracy - accuracy_slack
-
-  # Write to promise_flags
-  fout = open("promise_flags", "w+")
-  for swing in layer_swings:
-    int_swing = int(swing)
-    if int_swing > 0:
-      fout.write(str(swing) + "\n")
-  fout.close()
-
-  extra_args = str(total_runs) + " " + str(target_acc) + " " + str(abort_after)
-  # Execute Validation Run
-  p = subprocess.Popen("./" + binary_path + " " + extra_args, shell=True)
-  p.wait()
-
-  f = open("run_accuracies.txt")
-  index = 0.0
-  unsuccessful = 0.0
-  sum_acc = 0.0
-  for x in f:
-    x = x.strip()
-    acc = float(x)
-    if acc < target_acc:
-      unsuccessful += 1
-    index += 1
-    sum_acc += acc
-
-  f.close()
-  
-  confidence = ( (index - unsuccessful) / index) * 100.0
-  avg_acc = sum_acc / index 
-  print ("run_confidence = ", confidence, " avg_acc = ", avg_acc) 
-  
-  return avg_acc, confidence
-
-
-
-
-
-  
-
-
-if __name__ == "__main__" :
-
-
-  getStatisticalConfidence("lenet_promise", [7, 1, 1, 1], 99.0, 2, 50, 5)
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/automated_tests.py b/hpvm/projects/hpvm-tensor-rt/bin/automated_tests.py
deleted file mode 100644
index 8ac059ba0d0ac16dc354a367810dce5a31a15fc0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/automated_tests.py
+++ /dev/null
@@ -1,136 +0,0 @@
-
-
-import os
-import sys
-from tuner_src import benchmarks
-import subprocess
-
-
-def readAccuracy():
-
-  f = open("final_accuracy")
-  acc_str = f.read()
-  return acc_str
-
-    
-def executeAndDumpOutput(binary_path):
-
-  run_cmd = "./" + binary_path
-  output_file_path = "./test_dumps/" + binary_path
-  output_file = open(output_file_path, "a+")
-    
-  p = subprocess.Popen(run_cmd, shell=True, stdout=output_file)
-  retcode = p.wait()
-
-  output_file.close()
-
-  accuracy = readAccuracy()
-  print ("accuracy = ", accuracy)
-  
-  return retcode
-
-  
-
-  
-def runTensorBinaries(test_benchmarks):
-
-  # List of programs that faile during execution - For reporting
-  failed_progs = []
-  for bench_id in test_benchmarks:
-    bench = test_benchmarks[bench_id]
-    print ("bench = ", bench.tuner_binary)
-
-    retcode = executeAndDumpOutput(bench.tuner_binary)
-
-    if retcode != 0:
-      failed_progs.append(bench.tuner_binary)
-      
-  return failed_progs
-
-
-
-def runLayerBinaries(test_benchmarks):
-
-  # List of programs that faile during execution - For reporting
-  failed_progs = []
-  
-  for bench_id in test_benchmarks:
-    bench = test_benchmarks[bench_id]
-    print ("bench = ", bench.promise_binary)
- 
-    retcode = executeAndDumpOutput(bench.promise_binary)
-
-    if retcode != 0:
-      failed_progs.append(bench.promise_binary)
-      
-  return failed_progs
-
-
-
-def runFp16Binaries(test_benchmarks):
-
-  # List of programs that faile during execution - For reporting
-  failed_progs = []
-  for bench_id in test_benchmarks:
-    bench = test_benchmarks[bench_id]
-    print ("bench = ", bench.fp16_binary)
-
-    retcode = executeAndDumpOutput(bench.fp16_binary)
-
-    if retcode != 0:
-      failed_progs.append(bench.tuner_binary)
-      
-  return failed_progs
-
-
-    
-
-def runTests(test_benchmarks):
-
-  if not os.path.exists("test_dumps"):
-    os.mkdir("test_dumps")
-    
-  tensor_failed_progs = runTensorBinaries(test_benchmarks)
-  layer_failed_progs = runLayerBinaries(test_benchmarks)
-  fp16_failed_progs = runFp16Binaries(test_benchmarks)
-
-  failed_progs = tensor_failed_progs + layer_failed_progs + fp16_failed_progs
-
-  total_tests = len(test_benchmarks) * 3
-  succesful_tests = total_tests - len(failed_progs)
-
-  
-  print ("\n\n\n **** Results Summary ***** \n\n\n")
-  
-  print ("Total_Tests = ", total_tests, "\n")
-  print ("Successful_Tests = ", succesful_tests, "\n")
-  print ("Failed_Tests = ", total_tests - succesful_tests, "\n")
-
-  print ("\n\n --- Failing Tests = ", tensor_failed_progs + layer_failed_progs)
-  
-  print ("\n *Per-process logs dumped to ./test_dumps/")
-
-
-
-def checkEnvironment():
-
-  if not "CUDA_INCLUDE_PATH" in os.environ:
-    print ("ERROR: CUDA_INCLUDE_PATH NOT SET!")
-    sys.exit(0)    
-      
-  if not "CUDNN_PATH" in os.environ:
-    print ("ERROR: CUDA_PATH NOT SET!")
-    sys.exit(0)
-
-
-  if not os.path.exists("promise_flags"):
-    print ("promise_flags NOT found -- CREATE promise_flags with flag assignment per-layer")
-    sys.exit(0)
-    
-
-if __name__ == "__main__":
-
-  checkEnvironment()
-  
-  runTests(benchmarks.bench_tuner_data)
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/compute_install_times.py b/hpvm/projects/hpvm-tensor-rt/bin/compute_install_times.py
deleted file mode 100644
index 6e59b72f023a7869e721ba62f923f5e4ca791113..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/compute_install_times.py
+++ /dev/null
@@ -1,116 +0,0 @@
-
-
-class TuningParameters:
-  def __init__(self):
-    self.iterations_measured = 150
-    self.total_iterations = 30000
-
-    
-tunerParams = TuningParameters()
-
-
-class Benchmark:
-  def __init__(self):
-    self.binary_time = 0
-
-
-### All times are real profiled times on the Jetson Board
-### Times are for 150 OpenTuner iterations on Jetson
-
-ResNet50 = Benchmark()
-ResNet50.tuner_time = 3.85 * 100 * 150  # 50 images * 100 batches
-
-VGG16_ImageNet = Benchmark()
-VGG16_ImageNet.tuner_time = 4.55 * 100 * 150  # 50 images * 100 batches
-
-AlexNet_ImageNet = Benchmark()
-AlexNet_ImageNet.tuner_time = 0.7 * 100 * 150
-
-
-VGG16_CIFAR10 = Benchmark()
-VGG16_CIFAR10.tuner_time = 1.54 * 60 * 60  # 50 images * 100 batches
-
-
-VGG16_CIFAR100 = Benchmark()
-VGG16_CIFAR100.tuner_time = 1.57 * 60 * 60  # 50 images * 100 batches
-
-
-ResNet18 = Benchmark()
-ResNet18.tuner_time = 0.52 * 60 * 60  # 12.9 measured for 1000 images
-
-
-MobileNet = Benchmark()
-MobileNet.tuner_time = 0.72 * 60 * 60  # 50 images * 100 batches
-
-
-AlexNet_CIFAR10 = Benchmark()
-AlexNet_CIFAR10.tuner_time = 0.67 * 60 * 60  # Time in hours
-
-
-AlexNet2_CIFAR10 = Benchmark()
-AlexNet2_CIFAR10.tuner_time = 0.19 * 60 * 60 
-
-
-LeNet_CIFAR10 = Benchmark()
-LeNet_CIFAR10.tuner_time = 0.11 * 60 * 60
-
-
-
-
-
-def getInstallTime(Bench):
-
-  ## We limit pareto configs to 50 after iterations of tuning complete
-
-  tuner_invocations = tunerParams.total_iterations / tunerParams.iterations_measured
- 
-  extrapolated_time = tuner_invocations * Bench.tuner_time
-  
-  time_hours = extrapolated_time / (60 * 60)
-
-  return time_hours
-
-  
-
-# Routine to compute extrapolated tuning times
-def computeExtrapolatedInstallTime():
-
-
-    resnet50_time = getInstallTime(ResNet50)
-    print ("*** ResNet50 time (hrs) = ", resnet50_time)
-
-    resnet18_time = getInstallTime(ResNet18)
-    print ("*** ResNet18 time (hrs) = ", resnet18_time)
-
-    mobilenet_time = getInstallTime(MobileNet)
-    print ("*** MobileNet time (hrs) = ", mobilenet_time)
-    
-    vgg16_img_time = getInstallTime(VGG16_ImageNet)
-    print ("*** VGG16-Imagenet time (hrs) = ", vgg16_img_time)
-
-    vgg16_cifar10_time = getInstallTime(VGG16_CIFAR10)
-    print ("*** VGG16-CIFAR10 time (hrs) = ", vgg16_cifar10_time)
-
-    vgg16_cifar100_time = getInstallTime(VGG16_CIFAR100)
-    print ("*** VGG16-CIFAR100 time (hrs) = ", vgg16_cifar100_time)
-
-    alexnet_img_time = getInstallTime(AlexNet_ImageNet)
-    print ("*** AlexNet-Imagenet time (hrs) = ", alexnet_img_time)
-
-    alexnet_cifar10_time = getInstallTime(AlexNet_CIFAR10)
-    print ("*** AlexNet-CIFAR10 time (hrs) = ", alexnet_cifar10_time)
-
-    alexnet2_cifar10_time = getInstallTime(AlexNet2_CIFAR10)
-    print ("*** AlexNet2-CIFAR10 time (hrs) = ", alexnet2_cifar10_time)
-
-    lenet_cifar10_time = getInstallTime(LeNet_CIFAR10)
-    print ("*** LeNet-CIFAR10 time (hrs) = ", lenet_cifar10_time)
-
-
-
-  
-
-if __name__ == "__main__":
-
-    computeExtrapolatedInstallTime()
-
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py b/hpvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py
deleted file mode 100644
index 9f2ffb3eacd3cb81bcefb4b44a48f1d0a8a8356d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py
+++ /dev/null
@@ -1,139 +0,0 @@
-
-
-import subprocess
-import os
-import operator
-
-
-def constructTunerFile(num_flags, tensor_id, error_level, default_error):
-
-  f = open("opentuner_flags", "w+")
-
-  for i in range(num_flags):
-    if i == tensor_id:
-      f.write(str(error_level) + "\n")
-    else:
-      f.write(str(default_error) + "\n")
-
-  f.close()
-    
-
-
-def runAndTestError(binary_name, gold_acc):
-
-  num_runs = 20
-
-  binary_name = "./" + binary_name
-  FNULL = open(os.devnull, 'wb')
-  p = subprocess.Popen([binary_name, str(num_runs)], stdout = FNULL)
-  p.wait()
-
-  f = open("run_accuracies.txt")
-
-  total_err = 0.0
-  for x in f:
-    acc = float(x.strip())    
-    total_err += (gold_acc - acc)
-
-  avg_err = total_err / num_runs
-
-  return avg_err
-    
-
-
-
-def test_sensitivity(Bench):
-
-  tensor_errors = []
-  
-  error_levels = [6, 9, 12, 15]
-  num_flags = Bench.num_flags
-
-  for tensor_id in range(num_flags):
-    total_error = 0
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 0)
-      error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-      print (tensor_id, error_level, error)
-      total_error += error
-
-    avg_error = total_error / len(error_levels)
-
-    tensor_errors.append([tensor_id, avg_error])
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/tensor_errors_1000.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i) +  "\t" + str(tensor_errors[i][1]) + "\n")
-
-  f.close()
-
-  f_name = Bench.base_dir + "/tensor_errors_ranked_1000.txt"  
-  f2 = open(f_name, "w+")
-  tensor_errors.sort(key=operator.itemgetter(1))
-
-
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-
-    f2.write(str(tensor_errors[i][0]) +  "\t" + str(tensor_errors[i][1]) + "\n")
-    
-
-  f2.close()
-
-
-
-def test_sensitivity2(Bench):
-
-  num_flags = Bench.num_flags
-
-  constructTunerFile(num_flags, 0, 3, 3)
-  error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
-  ref_acc = Bench.tuner_accuracy - error
-  print ("*** Gold accuracy = ", Bench.tuner_accuracy, "  Ref accuracy = ", ref_acc, " *** \n\n")
-  
-  
-  tensor_errors = []
-  
-  error_levels = [6, 9, 12, 15]
-
-  for tensor_id in range(num_flags):
-    total_error = 0
-    for error_level in error_levels:
-      constructTunerFile(num_flags, tensor_id, error_level, 3)
-      error = runAndTestError(Bench.tuner_binary, ref_acc)
-      print (tensor_id, error_level, error)
-      total_error += error
-
-    avg_error = total_error / len(error_levels)
-
-    tensor_errors.append([tensor_id, avg_error])
-
-
-  print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
-  f_name = Bench.base_dir + "/tensor_composite_errors.txt"  
-  f = open(f_name, "w+")
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-    f.write(str(i) +  "\t" + str(tensor_errors[i][1]) + "\n")
-
-  f.close()
-
-  f_name = Bench.base_dir + "/tensor_composite_errors_ranked.txt"  
-  f2 = open(f_name, "w+")
-  tensor_errors.sort(key=operator.itemgetter(1))
-
-
-  for i in range(len(tensor_errors)):
-    print (i, tensor_errors[i][1])
-
-    f2.write(str(tensor_errors[i][0]) +  "\t" + str(tensor_errors[i][1]) + "\n")
-    
-
-  f2.close()
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/exhaustive.py b/hpvm/projects/hpvm-tensor-rt/bin/exhaustive.py
deleted file mode 100644
index bae38bf7e497897ae3db4e12dce48914903739fb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/exhaustive.py
+++ /dev/null
@@ -1,140 +0,0 @@
-
-import os
-import sys
-import shutil
-import subprocess
-import shutil
-
-
-
-class Benchmark:
-  def __init__(self):
-    self.binary = ""
-    self.num_flags = 4
-
-    
-
-Alexnet1 = Benchmark()
-Alexnet1.binary = "./lenet_keras_promise"
-Alexnet1.accuracy = 98.8
-Alexnet1.flags = [[8], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4] ] 
-
-
-Alexnet2 = Benchmark()
-Alexnet2.binary = "./fc4_clipped_promise"
-Alexnet2.accuracy = 93.72 
-Alexnet2.flags = [[3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7] ] 
-
-
-
-def dumpConfig(conf_flags, dir_prefix, file_id):
-  
-  shutil.copy("promise_flags", dir_prefix + "/" + str(file_id) + ".txt")
-
-  
-def dumpFinalConfigs(final_confs, dir_prefix):
-
-  f = open(dir_prefix + "/final_confs.txt", "w+")
-  for conf in final_confs:
-    ind = 0
-    for flag in conf:
-      f.write(str(flag))
-      if ind < len(conf) - 1:
-        f.write(",")
-      
-      ind += 1
-    f.write("\n")   
-
-  f.close()  
-
-
-def getAccuracy():
-  
-  file = open("final_accuracy", "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-
-  print accuracy 
-  return accuracy
-
-    
-
-
-def testConfidence(binary, target_acc, total_runs):
-
-  for i in range(total_runs):  
-    p = subprocess.Popen("./" + binary, shell=False)
-    p.wait()  
-    acc = getAccuracy()
-    if acc < target_acc:
-      return False
-
-  return True
-
-    
-def singleRun(binary):
-
-  p = subprocess.Popen("./" + binary, shell=False)
-  p.wait()  
-
-  return getAccuracy()
-
-  
-
-def createPromiseFile(conf_flags):
-
-    f = open("promise_flags", "w+")
-    for flag in conf_flags:
-        f.write(str(flag) + "\n")
-    f.close()
-    
-
-
-def runExhaustive(Bench, threshold, dir_prefix):
-
-  flags = Bench.flags
-  
-  accepted_confs = []
-  ind = 0
-  for flag1 in flags[0]:
-    for flag2 in flags[1]:
-      for flag3 in flags[2]:
-        for flag4 in flags[3]:
-          print (flag1, flag2, flag3, flag4)
-          conf_flags = []
-          conf_flags.append(flag1)
-          conf_flags.append(flag2)
-          conf_flags.append(flag3)
-          conf_flags.append(flag4)        
-    
-          createPromiseFile(conf_flags)
-
-          accuracy = singleRun(Bench.binary)
-          target_acc = Bench.accuracy - threshold
-          
-          if accuracy > target_acc:
-            if testConfidence(Bench.binary, target_acc, 3):
-              dumpConfig(conf_flags, dir_prefix, ind)
-              accepted_confs.append(conf_flags)
-
-          ind += 1   
-              
-  dumpFinalConfigs(accepted_confs, dir_prefix)
-  
-              
-
-if __name__ == "__main__":
-
-    #runExhaustive(Alexnet1, 1.0, "lenet_1")
-    #runExhaustive(Alexnet1, 2.0, "lenet_2")
-      
-    runExhaustive(Alexnet2, 1.0, "fc4_1")
-    runExhaustive(Alexnet2, 2.0, "fc4_2")
-      
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py b/hpvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py
deleted file mode 100644
index 0b7f09d92e91894d284b40cc0bd2d346c08e36c7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-import sys
-
-
-if __name__ == "__main__":
-
-    f = open(sys.argv[1], "r")
-    f2 = open("quant_ranges.txt", "w+")
-
-    layer_line = False
-    for x in f:
-        if "ConvLayer_PROMISE" in x or "FCLayer_PROMISE" in x or layer_line == True:
-            if layer_line == True:
-              layer_line = False
-            else:
-              layer_line = True
-            
-            print x 
-            toks = x.split(",")
-
-            for tok in toks:
-                tok = tok.strip()
-                tok_val = ""
-                try:
-                    tok_val = float(tok)
-                    try:
-                        tok_val = int(tok)
-                    except: 
-                        print (tok_val)
-                        f2.write(str(tok_val) + " ")
-                        #f2.write("tok_val = ", tok_val + " ")
-                except:
-                    continue
-
-            f2.write("\n")
-    
-
-    f.close()
-    f2.close()
-
-        
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/get_power_stats.py b/hpvm/projects/hpvm-tensor-rt/bin/get_power_stats.py
deleted file mode 100644
index e81cf10ece72c43457de718365bd2017e1684ab2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/get_power_stats.py
+++ /dev/null
@@ -1,79 +0,0 @@
-
-import sys
-import numpy as np
-import subprocess
-
-
-def get_avg_power(f_name):
-    
-    f = open(f_name, "r")
-    
-    gpu_power = []
-    ddr_power = []
-    sys_power = []
-    
-    for x in f:
-        toks = x.split()
-
-        gpu_power.append(float(toks[1]))
-        ddr_power.append(float(toks[2]))
-        sys_power.append(float(toks[3]))
-
-
-    avg_gpu_power = np.mean(gpu_power)
-    avg_ddr_power = np.mean(ddr_power)
-    avg_sys_power = np.mean(sys_power)
-
-    print ("** avg_gpu_power = ", avg_gpu_power, " avg_ddr_power = ", \
-           avg_ddr_power, " avg_sys_power = ", avg_sys_power)
-
-    return (avg_gpu_power, avg_ddr_power, avg_sys_power)
-
-
-#avail_frequencies = [140250000, 229500000, 318750000, 408000000, 497250000,
-#                     586500000, 675750000, 765000000, 854250000,       
-#                     943500000, 1032750000, 1122000000, 1211250000, 1300500000];
-
-
-avail_frequencies = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13];
-
-
-if __name__ == "__main__":
-
-
-  programs = ["alexnet_promise", "alexnet2_promise", "vgg16_cifar10_promise", "resnet18_promise", "resnet50_imagenet_promise", "mobilenet_promise", "vgg16_imagenet_promise"]
-  
-  for binary_path in programs:
-    
-    power_avgs = []
-    power_freq_file = "power_data/" + binary_path + "/power_vals.txt"
-    fout = open(power_freq_file, "w+")
-    
-    for frequency in avail_frequencies:
-
-      print (frequency)
-      poll_path = "./poll"
-      iterations = 10
-
-      poll_cmd = poll_path + " " + str(frequency) # sudo needed for frequency change
-      subprocess.call(poll_cmd, shell=True)
-
-      
-      binary_path = "./" + binary_path
-      power_file = " power_data/" + binary_path  +  "/power.out." + str(frequency)
-      profile_cmd = "../../system_profiler/build/offline_profiler " + binary_path + " " + \
-                    str(iterations) + " tensor.out." + str(frequency) + power_file
-      
-      subprocess.call(profile_cmd, shell=True)
-    
-    
-      #avg_power = get_avg_power("power.out." + str(frequency))
-      avg_power = get_avg_power(power_file)
-      power_avgs.append(avg_power)
-      
-      fout.write(str(avg_power[0]) + " " + str(avg_power[1]) + " " +  str(avg_power[2]) + "\n")      
-      print (avg_power)
-      
-
-    print (power_avgs)
-    fout.close()
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/install_runtime.sh b/hpvm/projects/hpvm-tensor-rt/bin/install_runtime.sh
deleted file mode 100644
index 33a54cd0de626113e5cf11e2f6a6928d4fa384eb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/install_runtime.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/sh
-
-export HPVM_TENSOR_RT_HOME=/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/
-export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-
-clang++ -I/software/cuda-9.1/include -emit-llvm -c ${HPVM_TENSOR_RT_HOME}/tensor_runtime/include/tensor_signatures.cc -o ${HPVM_TENSOR_RT_HOME}/lib/tensor_runtime.bc
-llvm-dis --version
-llvm-dis ${HPVM_TENSOR_RT_HOME}/lib/tensor_runtime.bc
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py b/hpvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py
deleted file mode 100644
index c64a9f242fcf80b585c5862ceef16b8fb8ce50a5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py
+++ /dev/null
@@ -1,48 +0,0 @@
-
-import sys
-
-
-def loadLayerDesc(layer_desc_file):
-
-    layer_desc = []
-    f = open(layer_desc_file)
-    for x in f:
-      vals = x.split()
-      layer_desc.append(vals)
-
-    return layer_desc      
-
-
-
-if __name__ == "__main__":
-
-  if len(sys.argv) < 4:
-      print ("Usage: python mark_depthwise.py  $layer_file  $input_conf  $output_conf")
-      
-  layer_file_name = sys.argv[1]
-  input_file_name = sys.argv[2]
-  output_file_name = sys.argv[3]
-
-  
-  layer_desc = loadLayerDesc(layer_file_name)
-
-  f_in = open(input_file_name)
-  f_out = open(output_file_name, "w+")
-    
-  for x in f_in:
-      it = 0
-      confs = x.split(",")
-      print confs
-      for conf in confs:
-          print (" it = ", it, " layer_desc[it] = ", layer_desc[it], " \n")
-          if layer_desc[it][0] == "depthwise_conv":
-              f_out.write("9,")
-          else:
-              f_out.write(conf)
-              if it < len(confs) - 1:
-                  f_out.write(",")
-
-          it += 1        
-
-  f_in.close()
-  f_out.close()
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py b/hpvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py
deleted file mode 100644
index 4ca1f3f52e59498725414f37e56e06e5e74f1953..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py
+++ /dev/null
@@ -1,316 +0,0 @@
-
-import os
-import sys
-import shutil
-import subprocess
-import shutil
-import numpy as np
-
-
-
-class Benchmark:
-  def __init__(self):
-    self.binary = ""
-
-
-
-benchmarks = {} 
-
-Alexnet1 = Benchmark()
-Alexnet1.binary = "./lenet_keras_promise"
-Alexnet1.accuracy = 98.7
-Alexnet1.loss1_conf = "8 8 8 8,4,4,7"
-Alexnet1.loss2_conf = "8 8 8 8,3,4,7"
-
-benchmarks["lenet"] = Alexnet1
-
-
-Alexnet2 = Benchmark()
-Alexnet2.binary = "./fc4_clipped_promise"
-Alexnet2.accuracy = 93.72 
-Alexnet2.loss1_conf = "7,7,6,7"
-Alexnet2.loss2_conf = "4,4,4,5"
-
-benchmarks["fc4"] = Alexnet2
-
-
-Alexnet3 = Benchmark()
-Alexnet3.binary = "./alexnet_valid"
-Alexnet3.accuracy = 79.16 
-Alexnet3.loss1_conf = "8 8 8 8,6,6,6,7,7"
-Alexnet3.loss2_conf = "8 8 8 8,4,4,6,4,7"
-
-benchmarks["alexnet"] = Alexnet3
-
-
-Alexnet4 = Benchmark()
-Alexnet4.binary = "./alexnet2_valid"
-Alexnet4.accuracy = 85.09
-Alexnet4.loss1_conf = "9 9 9,7,7,7,9 9 9,7,9 9"
-Alexnet4.loss2_conf = "9 9 9,7,7,6,8 8 8,6,9 9"
-
-benchmarks["alexnet2"] = Alexnet4
-
-
-Alexnet5 = Benchmark()
-Alexnet5.binary = "./resnet18_valid"
-Alexnet5.accuracy = 89.44 
-Alexnet5.loss1_conf = "9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8"
-Alexnet5.loss2_conf = "9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,7,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8,8 8"
-
-benchmarks["resnet"] = Alexnet5
-
-
-
-Alexnet6 = Benchmark()
-Alexnet6.binary = "./vgg16_cifar10_valid"
-Alexnet6.accuracy = 89.41
-Alexnet6.loss1_conf = "9 9 9,7,7,7,9 9 9,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9"
-Alexnet6.loss2_conf = "9 9 9,5,5,8 8 8 8,4,6,4,7,8 8 8,4,4,4,7,8 8 8,8 8"
-
-benchmarks["vgg16_cifar10"] = Alexnet6
-
-
-Alexnet7 = Benchmark()
-Alexnet7.binary = "./vgg16_cifar100_valid"
-Alexnet7.accuracy = 66.19
-Alexnet7.loss1_conf = "9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8"
-Alexnet7.loss2_conf = "9 9 9,8 8 8 8,8 8 8,7,8 8 8,8 8 8,8 8 8 8,6,6,7,8 8 8,7,6,8 8 8,8 8"
-
-benchmarks["vgg16_cifar100"] = Alexnet7
-
-
-
-Alexnet8 = Benchmark()
-Alexnet8.binary = "./pipeline_GEOM_valid"
-Alexnet8.loss1_conf = "8 8,8 8 8,8 8,7"
-Alexnet8.loss2_conf = "8 8,8 8 8,8 8,6"
-
-benchmarks["pipeline_GEOM"] = Alexnet8
-
-
-
-Alexnet9 = Benchmark()
-Alexnet9.binary = "./pipeline_GEMO_valid"
-Alexnet9.loss1_conf = "8 8,8 8 8,8 8,8 8"
-Alexnet9.loss2_conf = "7,8 8 8,8 8,8 8"
-
-benchmarks["pipeline_GEMO"] = Alexnet9
-
-
-
-Alexnet10 = Benchmark()
-Alexnet10.binary = "./pipeline_GEO_valid"
-Alexnet10.loss1_conf = "8 8,8 8 8,8 8"
-Alexnet10.loss2_conf = "8 8,8 8 8,8 8"
-
-benchmarks["pipeline_GEO"] = Alexnet10
-
-
-
-Alexnet11 = Benchmark()
-Alexnet11.binary = "./pipeline_GSM_valid"
-Alexnet11.loss1_conf = "8 8,8 8,7"
-Alexnet11.loss2_conf = "7,8 8,6"
-
-benchmarks["pipeline_GSM"] = Alexnet11
-
-
-
-Alexnet12 = Benchmark()
-Alexnet12.binary = "./pipeline_GSME_valid"
-Alexnet12.loss1_conf = "8 8,8 8,8 8,8 8 8"
-Alexnet12.loss2_conf = "7,8 8,8 8,8 8 8"
-
-benchmarks["pipeline_GSME"] = Alexnet12
-
-
-
-def createPromiseFile(conf_flag_str):
-
-    conf_flags = conf_flag_str.split(",")   
-    f = open("promise_flags", "w+")
-    for flag_str in conf_flags:
-        flags = flag_str.split()
-        f.write(str(flags[0]) + "\n")
-    f.close()
-
-    
-def getRunAccuracies():
-
-  run_accuracies = []  
-  file = open("run_accuracies.txt", "r")
-  file_str = file.read()
-
-  for flag in file_str.split("\n"):
-    print ("*** flag = ", flag)
-    flag = flag.strip()
-    if flag == "":
-        continue  
-    run_accuracies.append(float(flag))        
-
-  file.close()
-
-  return run_accuracies
-    
-
-
-def testConfidence(binary):
-
-  p = subprocess.Popen("./" + binary, shell=False)
-  p.wait()  
-  run_accuracies = getRunAccuracies()
-        
-  return np.mean(run_accuracies), np.std(run_accuracies)
-
-
-
-def getAccuracy():
-  
-  file = open("final_accuracy", "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return 20
-
-  #print accuracy 
-  return accuracy
-
-
-def getPSNR():
-  
-  file = open("avg_psnr", "r")
-  acc_str = file.read()
-  file.close()
-  accuracy = float(acc_str)
-  
-  try:
-    accuracy = float(acc_str)
-  except:
-    return -100
-
-  #print accuracy 
-  return accuracy
-
-
-
-
-def testPSNRConfidence(binary, total_runs):
-
-  run_accuracies = []
-  run_psnr = []
-  for i in range(total_runs):  
-    p = subprocess.Popen("./" + binary, shell=False)
-    p.wait()  
-    acc = getAccuracy()
-    psnr = getPSNR()
-    run_accuracies.append(acc)
-    run_psnr.append(psnr)
-
-  return np.mean(run_accuracies), np.std(run_accuracies), np.mean(run_psnr), np.std(run_psnr)
-
-    
-
-def runBench(bench_name, dir_prefix):
-
-  Bench = benchmarks[bench_name]
-  binary = Bench.binary
-  accuracy = Bench.accuracy
-
-  createPromiseFile(Bench.loss1_conf)
-  mean, std = testConfidence(binary)
-  print ("mean = ", mean, " std = ", std)
-
-  
-  f = open(dir_prefix + "/" + binary + "_loss1.txt" ,"w+")
-  f.write("mean = " + str(mean) + " std = " + str(std))
-  f.close()
-  
-  createPromiseFile(Bench.loss2_conf)
-  mean, std = testConfidence(binary)
-  print ("mean = ", mean, " std = ", std)
-
-  
-  f = open(dir_prefix + "/" + binary + "_loss2.txt" ,"w+")
-  f.write("mean = " + str(mean) + " std = " + str(std))
-  f.close()
-  
-
-
-  
-
-def gen30dbFile():
-
-  f = open("psnr.txt", "w+");
-  f.write("30");
-  f.close()
-  
-
-def gen20dbFile():
-
-  f = open("psnr.txt", "w+");
-  f.write("20");
-  f.close()
-
-
-
-def runPSNRBench(bench_name, dir_prefix):
-
-  Bench = benchmarks[bench_name]
-  binary = Bench.binary
-
-  gen30dbFile()
-  createPromiseFile(Bench.loss1_conf)
-  mean, std, psnr_mean, psnr_std = testPSNRConfidence(binary, 20)
-  print ("mean = ", mean, " std = ", std)
-
-  
-  f = open(dir_prefix + "/" + binary + "_loss30.txt" ,"w+")
-  f.write("mean = " + str(mean) + " std = " + str(std))
-  f.write("  psnr_mean = " + str(psnr_mean) + " psnr_std = " + str(psnr_std)) 
-  f.close()
-
-  
-  gen20dbFile()
-  createPromiseFile(Bench.loss2_conf)
-  mean, std, psnr_mean, psnr_std = testPSNRConfidence(binary, 20)
-  print ("mean = ", mean, " std = ", std)
-  
-  f = open(dir_prefix + "/" + binary + "_loss20.txt" ,"w+")
-  f.write("mean = " + str(mean) + " std = " + str(std))
-  f.write("  psnr_mean = " + str(psnr_mean) + " psnr_std = " + str(psnr_std)) 
-  f.close()
-  
-
-
-
-  
-
-def runDNNs():
-
-  #runBench("fc4", "avg_accuracies")
-  #runBench("lenet", "avg_accuracies")
-  #runBench("alexnet", "avg_accuracies")
-  #runBench("alexnet2", "avg_accuracies")
-  #runBench("resnet", "avg_accuracies")
-  #runBench("vgg16_cifar10", "avg_accuracies")
-  #runBench("vgg16_cifar100", "avg_accuracies")
-
-  runPSNRBench("pipeline_GEOM", "avg_accuracies")
-  runPSNRBench("pipeline_GEMO", "avg_accuracies")
-  runPSNRBench("pipeline_GEO", "avg_accuracies")
-  runPSNRBench("pipeline_GSM", "avg_accuracies")
-  runPSNRBench("pipeline_GSME", "avg_accuracies")
-
-  
-      
-
-if __name__ == "__main__":
-
-  runDNNs()
-  
-      
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/measure_confidence.py b/hpvm/projects/hpvm-tensor-rt/bin/measure_confidence.py
deleted file mode 100644
index 74aa23c71aa3e81fc9422a3cc73ba3b69ed98c8a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/measure_confidence.py
+++ /dev/null
@@ -1,125 +0,0 @@
-
-import argparse
-import os
-import subprocess
-import sys
-
-
-def getAccuracy(file_name):
-
-  if not os.path.exists(file_name):
-    print("final_accuracy file not found ")
-    sys.exit(0)
-    
-  file = open(file_name, "r")
-  acc_str = file.read()
-  accuracy = float(acc_str)
-  print accuracy
-  return accuracy  
-
-
-total_runs = 12.0
-skip_lines = 0
-
-
-def test_func():
-  print "test_func"
-  sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
-  #total_runs = 100.0
-  successful_runs = 0.0
-  total_acc = 0
-
-  for i in range(int(total_runs)):
-    subprocess.call(binary_name)
-    accuracy = getAccuracy("final_accuracy")
-    total_acc += accuracy
-
-    if accuracy > accuracy_threshold:
-      successful_runs += 1
-
-  confidence = (successful_runs / total_runs) * 100.0    
-  print("confidence = ", confidence)    
-  avg_acc = total_acc / total_runs
-  print("average accuracy = ", avg_acc)
-
-  return confidence, avg_acc
-  
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
-  confidence_list = []
-  
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-    
-    f = open(result_dir + "/" + file_name)
-    tuner_file = open("opentuner_flags", "w+")
-
-    index = 0
-    results_str = ""
-    for x in f:
-      if index >= skip_lines:
-        error_knob = int(float(x.split()[1]))
-        print error_knob
-        tuner_file.write(str(error_knob) + "\n")
-
-      results_str += x
-      index += 1
-      
-    tuner_file.close()
-    
-    run_confidence, avg_accuracy = do_multiple_runs(binary, accuracy, confidence)
-
-    if run_confidence > 90:
-      f2 = open(output_dir + "/" + file_name, "w+")
-      f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
-      f2.write(results_str)
-      f2.close()
-
-    conf_result = (run_confidence, avg_accuracy, file_name)
-    confidence_list.append(conf_result) 
-
-  return confidence_list
-    
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-  argparser.add_argument('--output-dir', help='Directory for storing output directory')
-  argparser.add_argument('--binary', help='Binary name to run')
-  argparser.add_argument('--accuracy', type=float,  help='Accuracy constraint')
-  argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-  
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-  output_dir = args.output_dir
-  binary = args.binary
-  accuracy = args.accuracy
-  confidence = args.confidence
-
-  confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
-  #print confidence_list
-
-  sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-   
-  output_file = open(output_dir + "/confidence_summary.txt", "w+")
-  for x in sorted_list:
-    output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")    
-
-  output_file.close()
-  
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py b/hpvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py
deleted file mode 100644
index 3c9ea9de2854ed133350950d3995f459120176de..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py
+++ /dev/null
@@ -1,60 +0,0 @@
-
-
-
-if __name__ == "__main__":
-
-  dnn_benchs = []
-  dnn_benchs.append("fc4")
-  dnn_benchs.append("lenet_keras")
-  dnn_benchs.append("alexnet_cifar10")
-  dnn_benchs.append("alexnet2_cifar10")
-  dnn_benchs.append("vgg16_cifar10")
-  dnn_benchs.append("vgg16_cifar100")
-  dnn_benchs.append("resnet18_cifar10")
-  dnn_benchs.append("mobilenet")
-  dnn_benchs.append("mobilenet_shallow")
-
-  
-  for bench in dnn_benchs:
-    errors_file1 = "build_tuner/tuner_results/" + bench + "/tensor_errors_1000.txt"    
-    errors_file2 = "build_test/tuner_results/" + bench + "/tensor_composite_errors.txt"    
-    ops_file = "build_tuner/tuner_results/" + bench + "/op_names.txt"    
-
-    f1 = open(errors_file1)
-    f2 = open(errors_file2)
-    f3 = open(ops_file)
-
-    fout = open("build_tuner/tuner_results/" + bench + "/tensor_op_errors.txt", "w+")
-
-    bench_data = []
-    for x in f3:
-      op_name = x.strip()  
-      bench_data.append([op_name, 0.0, 0.0])    
-
-    it = 0
-    for x in f1:
-      if it >= len(bench_data):
-        break
-      toks = x.split()
-      error1 = float(toks[1])
-      print error1
-      bench_data[it][1] = error1
-      it += 1
-
-    it = 0
-    for x in f2:
-      if it >= len(bench_data):
-        break
-      toks = x.split()
-      error2 = float(toks[1])
-      bench_data[it][2] = error2
-      it += 1
-
-    for i in range(len(bench_data)):
-      fout.write(str(i) + "\t" + bench_data[i][0] + "\t" + str(bench_data[i][1]) + "\t" + str(bench_data[i][2]) + "\n")
-
-    fout.close()
-    f1.close()
-    f2.close()
-    f3.close()
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py b/hpvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py
deleted file mode 100644
index c54d7dfcddc161aa20dd8378d2652d32c4905e38..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-import numpy as np
-import os
-import struct
-
-
-def read_value_range(file_name):
-
-  print file_name
-  f = open(file_name, "rb")
-
-  bytes = os.stat(file_name).st_size
-  elems = bytes/4
-
-  data_arr = struct.unpack('f'*elems, f.read(4*elems))
-
-  print (np.amin(data_arr))
-  print (np.amax(data_arr))
-
-
-  
-
-if __name__ == "__main__":
-
-  dir_prefix = "model_params/alexnet2_cifar10/"
-  print dir_prefix
-  read_value_range(dir_prefix + "norm_cifar_input.bin")
-  read_value_range(dir_prefix + "conv1.bin")
-  read_value_range(dir_prefix + "conv1_bias.bin")
-  read_value_range(dir_prefix + "conv2.bin")
-  read_value_range(dir_prefix + "conv2_bias.bin")
-  read_value_range(dir_prefix + "conv3.bin")
-  read_value_range(dir_prefix + "conv3_bias.bin")
-  read_value_range(dir_prefix + "conv4.bin")
-  read_value_range(dir_prefix + "conv4_bias.bin")
-  read_value_range(dir_prefix + "conv5.bin")
-  read_value_range(dir_prefix + "conv5_bias.bin")
-  read_value_range(dir_prefix + "conv6.bin")
-  read_value_range(dir_prefix + "conv6_bias.bin")
-  read_value_range(dir_prefix + "fc1.bin")
-  read_value_range(dir_prefix + "fc1_bias.bin")
-      
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py b/hpvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py
deleted file mode 100644
index b75a7d4750074cf6234151ae21a8bff5af1050d5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py
+++ /dev/null
@@ -1,35 +0,0 @@
-
-
-import sys
-
-
-if __name__ == "__main__":
-
-  if len(sys.argv) < 3:
-    print ("Usage: python replace_half_calls.py  in_file.cc  half_out_file.cc \n")
-    sys.exit(0)
-          
-  file_name = sys.argv[1]
-  out_file_name = sys.argv[2]
-
-  f = open(file_name)
-  str = f.read()
-
-  str = str.replace("tensorConvolution", "tensorHalfConvolution")
-  str = str.replace("tensorAdd", "tensorHalfAdd")
-  str = str.replace("tensorRelu", "tensorHalfRelu")
-  str = str.replace("tensorRelu2", "tensorHalfRelu2")
-  str = str.replace("tensorTanh", "tensorHalfTanh")
-  str = str.replace("tensorPooling", "tensorHalfPooling")
-  str = str.replace("tensorGemmGPU", "tensorHalfGemmGPU")
-  
-  print (str)
-
-  f.close()
-
-  f2 = open(out_file_name, "w+")
-
-  f2.write(str)
-
-  f2.close()
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/run_dyn.py b/hpvm/projects/hpvm-tensor-rt/bin/run_dyn.py
deleted file mode 100644
index 83956051bef2a868f7f685f3d471e5d5f84ac03d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/run_dyn.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from pathlib import Path
-
-name_ranges = {
-    "canny_pareto": list(range(11, 28 + 1)),
-    "blend_pareto": list(range(11, 20 + 1))
-}
-iters = 20
-
-def run_binary(config_prefix, binary_file, vals):
-    from subprocess import run
-    from os import rename
-    from shutil import copy
-    from tqdm import tqdm
-
-    out_dir = Path("run_data_{}".format(binary_file))
-    out_dir.mkdir()
-    for i in tqdm(vals):
-        config = (config_prefix/"{}.txt".format(binary_file)).as_posix()
-        copy(config, "tuner_confs.txt")
-        with open("slowdowns.txt", 'w') as f:
-            f.write('\n'.join((str(i / 10) for _ in range(iters))))
-        command = "./{} >out 2>&1".format(binary_file)
-        tqdm.write("{}; {}".format(command, i))
-        run(command, shell=True, check=True)
-        out_path = (out_dir/"out{}".format(i)).as_posix()
-        profile_path = (out_dir/"profile_info_out{}.txt".format(i)).as_posix()
-        rename("out", out_path)
-        rename("profile_info_0.txt", profile_path)
-        # rename("final_accuracy", out_dir/"final_accuracy{}".format(i))
-
-
-def main():
-    from sys import argv
-
-    config_prefix = Path(argv[1])
-    for binary_file, vals in name_ranges.items():
-        print(binary_file)
-        run_binary(config_prefix, binary_file, vals)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/select_top_results.py b/hpvm/projects/hpvm-tensor-rt/bin/select_top_results.py
deleted file mode 100644
index 898b4c4f42211e010b1544039cbd4b4125c03b92..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/select_top_results.py
+++ /dev/null
@@ -1,89 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 7
-linear_index = 8
-quad_index = 9
-
-top_k = 10
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
-  ref_dir = result_dir + "/" + sub_dir
-  if not os.path.exists(ref_dir):
-    os.mkdir(ref_dir)
-  
-  for i in range(k):
-    file_name = sorted_list[i][1]
-    file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
-    f = open(file_name, "w+")
-    f.write(str(sorted_list[i][2]) + "\t")
-    f.write(str(sorted_list[i][3]) + "\t")
-    f.write(str(sorted_list[i][4]) + "\n")
-    f.write(sorted_list[i][0])
-    f.close()
-
-    
-    
-
-def select_top_results(result_dir):
-
-  if not os.path.exists(result_dir):
-    print("Path does not exist")
-    sys.exit(0)
-
-  file_names = os.listdir(result_dir)
-  print file_names
-
-  results_arr = []
-  
-  for file_name in file_names:
-    # Skip sub-directories
-    if os.path.isdir(result_dir + "/" + file_name):
-      continue
-
-    log_result = 0.0
-    linear_result = 0.0
-    quad_result = 0.0
-    file_str = ""
-    
-    f = open(result_dir + "/" + file_name)
-    for x in f:
-      words = x.split()
-      log_result += float(words[log_index])
-      linear_result += float(words[linear_index])
-      quad_result += float(words[quad_index])
-      file_str += x 
-      
-
-    file_result = (file_str, file_name, log_result, linear_result, quad_result)          
-    results_arr.append(file_result)    
-
-    
-  sorted_list = sorted(results_arr, key = lambda tup: tup[2])
-  dump_results(sorted_list, top_k, result_dir, "log")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[3])
-  dump_results(sorted_list, top_k, result_dir, "linear")
-
-  sorted_list = sorted(results_arr, key = lambda tup: tup[4])
-  dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-
-if __name__ == "__main__":
-
-  argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
-  argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
-  args = argparser.parse_args()
-  result_dir = args.result_dir
-
-  select_top_results(result_dir)
-  
-
-    
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setupEnv.sh b/hpvm/projects/hpvm-tensor-rt/bin/setupEnv.sh
deleted file mode 100644
index 58f16f20d0af12f041840b8037ae13e49c214ed4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setupEnv.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-module load cuda-toolkit/8.0
-export CUDNN_PATH=/software/cuda-toolkit-8.0/lib64/
-export LIBRARY_PATH=$LIBRARY_PATH:/software/cuda-toolkit-8.0/lib64/
-
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh b/hpvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh
deleted file mode 100644
index d9f092a19f12a91bd588a356fc99744c14deb26a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-# CUDNN Path setup
-# module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/usr/local/cuda/include
-export CUDNN_PATH=/use/local/cuda/lib64/
-export LIBRARY_PATH=/usr/local/cuda/lib64/:$LIBRARY_PATH
-#export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
-
-# HPVM Path setup
-#export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-#export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-#export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-#export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setup_cuda_paths.sh b/hpvm/projects/hpvm-tensor-rt/bin/setup_cuda_paths.sh
deleted file mode 100644
index 9f45a76033c7e82728a2bdaf0f82d2bfe9230272..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setup_cuda_paths.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-# NOTE: Set Paths to local installation paths
-# NOTE: Module cuda-took/9.1 not supported on non-EngrIT systems
-module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/software/cuda-9.1/include
-export CUDNN_PATH=/software/cuda-9.1/lib64/
-export LIBRARY_PATH=/software/cuda-9.1/lib64/:$LIBRARY_PATH
-export LD_LIBRARY_PATH=/software/cuda-9.1/lib64/:$LD_LIBRARY_PATH
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh b/hpvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh
deleted file mode 100644
index b288ccfe43c577f9ad14c4eb16284539ae5682ea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-
-export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-9.0/targets/aarch64-linux/lib/
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-9.0/targets/aarch64-linux/lib/
-export CUDNN_PATH=/usr/local/cuda-9.0/
-export CUDA_INCLUDE_PATH=${CUDNN_PATH}/include
-
-export LLVM_BUILD_ROOT=/home/nvidia/Gitlab/hpvm/build/
-export LLVM_SRC_ROOT=/home/nvidia/Gitlab/hpvm/llvm/
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setup_paths.sh b/hpvm/projects/hpvm-tensor-rt/bin/setup_paths.sh
deleted file mode 100644
index 446481b79a47827bf47341ce9d14f15f57d26866..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setup_paths.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/sh
-
-# Setting include path for Anaconda include files
-export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-# Setting path for llvm/clang-4.0 build
-export PATH=/home/hsharif3/Gitlab/llvm/llvm/build/bin/:$PATH
-
-export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-
-export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh b/hpvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh
deleted file mode 100644
index 05db92cc08c8532ae5f83f6bdee15c12b8ed9159..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-# CUDNN Path setup
-module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/software/cuda-9.1/include
-export CUDNN_PATH=/software/cuda-9.1/lib64/
-export LIBRARY_PATH=/software/cuda-9.1/lib64/:$LIBRARY_PATH
-export LD_LIBRARY_PATH=/software/cuda-9.1/lib64/:$LD_LIBRARY_PATH
-
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/swing_selection.py b/hpvm/projects/hpvm-tensor-rt/bin/swing_selection.py
deleted file mode 100644
index b5c484a23029f97218500571ebb8bcafc718d430..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/swing_selection.py
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
-    results = []
-    with open(textFile, "r") as f:
-        for line in f:
-            token = line.split("\t")
-            if (len(token) < 7):
-                continue
-            record = (token[0], float(token[1]), float(token[5]), float(token[6]))
-            results.append(record)
-    return results
-
-
-convL1bins =  [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins =  [(0.995298, 1.3643), (0.861066, 1.16279), (0.426857, 0.547827), (0.262645, 0.330186), (0.218984, 0.273731), (0.187878, 0.233872), (0.150619, 0.186512), (0.10106, 0.124477), (0.00035427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins=  [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins=  [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
-    if op == 'tensorConv':
-        return convL1bins, convL2bins
-    if op == 'tensorAdd':
-        return biasL1bins, biasL2bins
-    if op == 'tensorGemm':
-        return gemmL1bins, gemmL2bins
-
-    return None, None
-
-
-def getSwing(Lx, opLxbin):
-    if opLxbin == None:
-        return 0
-    for i, (minT, maxT) in enumerate(opLxbin):
-        if Lx > minT:
-            return i
-
-    return 9
-
-
-
-def getConfiguration(L_thresholds):
-    configuration = []
-    for l in L_thresholds:
-        # L0 is op_type
-        opL1bin, opL2bin = findBinByOp(l[0])
-        # NOTE: L2 is L1 error, L3 is L2 error
-        sL1 = getSwing(l[2], opL1bin)
-        sL2 = getSwing(l[3], opL2bin)
-        if sL1 < 7:
-            sL1 = sL1 + 1
-        if sL2 < 7:
-            sL2 = sL2 + 1
-        configuration.append((l[0], l[1], l[2], l[3], sL1, sL2, max(sL1, sL2)))
-
-    return configuration
-
-
-def displayConfig(config):
-    for c in config:
-        print(c)
-
-def displayMultipleConfigurations(configurations):
-    for f, c in configurations.items():
-        print(f)
-        displayConfig(c)
-        print()
-
-def getConfigFromFile(filename):
-    L_requirements = readDataFromText(filename)
-    config = getConfiguration(L_requirements)
-    return config
-
-
-def getConfigurationsFromDir(dirname):
-    configurations = dict()
-    for f in os.listdir(dirname):
-        configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
-    return configurations
-              
-
-def getLayerWiseTarget(config):
-    target = []
-    for i, op in enumerate(config):
-        if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
-            t = op[6]
-            for j in range(i+1, len(config)):
-                if config[j][0] == 'tensorGemm' or config[j][0] == 'tensorConv':
-                    break
-                t = max(t, config[j][6])
-            target.append(t)
-            t = 0
-
-    return target
-
-
-def dumpLayerWiseTarget(file, targets):
-    with open(file, "w") as f:
-        for name, t in targets.items():
-            f.write(name)
-            f.write(" ")
-            for i in t:
-                f.write(str(i))
-                f.write(" ")
-            f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
-    targets = dict()
-    for f, c in configs.items():
-        targets[f] = [d[6] for d in c]
-
-    return targets
-                
-
-def dumpBenchmarkTargets(name, benchmark_dir):
-    benchmark_targets = dict()
-    error = ['linear', 'log', 'quad']
-    for e in error:
-        results_dir = os.path.join(benchmark_dir, e)
-        configs = getConfigurationsFromDir(results_dir)
-        benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
-    return benchmark_targets
-
-
-
-def dumpTargets(filename, targets):
-    with open(filename, "w") as f:
-        for e, file_configs in targets.items():
-            for name, config in file_configs.items():
-                for c in config:
-                    f.write(str(c))
-                    f.write(" ")
-                f.write("\n")
-
-
-                
-def getLayerSwings(layer_desc, configurations):
-
-    layer_swings = []
-    for i in range(len(configurations)):
-      config_vals = configurations[i]
-      if len(config_vals) == 0:
-        continue
-      
-      layer_index = 0
-      index = 0
-      swing_vals = []
-                   
-      while layer_index < len(layer_desc):
-        if len(layer_desc[layer_index]) == 1:
-          promise_swing = config_vals[index]
-          layer_type = layer_desc[layer_index][0]
-          layer_type = layer_type.strip()
-          print ("****layer_type = ", layer_type)
-          if layer_type != "conv" and layer_type != "dense":
-            promise_swing = -9
-          if layer_type == "depthwise_conv":
-            promise_swing = 9  
-          index += 1
-        else:
-          #print ("index = ", index)
-          # FIXIT: Doesn't look right
-          print (config_vals[index], config_vals[index+1])
-          promise_swing = max(config_vals[index], config_vals[index+1])                  
-          stride = len(layer_desc[layer_index])
-          index += stride
-          
-        swing_vals.append(promise_swing)
-        layer_index += 1  
-        
-      layer_swings.append(swing_vals)
-
-    return layer_swings
-
-                   
-                
-
-def loadLayerDesc(layer_desc_file):
-
-    layer_desc = []
-    f = open(layer_desc_file)
-    for x in f:
-      vals = x.split()
-      layer_desc.append(vals)
-
-    return layer_desc
-      
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
-    layer_desc = loadLayerDesc(layer_desc_file)
-    print (layer_desc)
-
-    file_names = []
-    configurations = []
-    for e, file_configs in targets.items():
-      for name, config in file_configs.items():
-        config_vals = []  
-        for c in config:
-          config_vals.append(c)         
-        print (config_vals)
-
-        configurations.append(config_vals)
-
-        rank = e + "_" +  "_".join(name.split("_")[-2:])
-        file_names.append(rank)
-        
-        
-    # NOTE: get PROMISE swing values corresponding to each layer
-    layer_swings = getLayerSwings(layer_desc, configurations)
-
-    targets_file_path = tuned_result_dir + "/layer_targets.txt"
-    f = open(targets_file_path, "w+")
-
-    for config in layer_swings:
-      index = 0
-      for swing in config:
-        swing_str = ""
-        if swing == 8 or swing == 9:
-          layer_size = len(layer_desc[index])
-          for i in range(layer_size):
-            swing_str += str(swing)
-            if i < layer_size - 1:
-              swing_str += " "
-        elif swing == -9:
-          swing_str += "8"                   
-        else:
-          swing_str += str(swing)
-
-        if index < len(config) - 1:
-          swing_str += ","    
-          
-        f.write(swing_str)
-        index += 1
-        
-      f.write("\n")
-        
-    f.close()
-    
-    print(layer_swings)    
-    return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
-  # Ensuring first conv on GPU
-  for conf in layer_swings:
-    conf[0] = 9
-    
-    
-    
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
-    targets_file_path = tuned_result_dir + "/tensor_targets.txt"
-    targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
-    dumpTargets(targets_file_path, targets)
-    
-    layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
-    replaceFirstLayer(layer_swings)
-    
-    return layer_swings, file_names
-    
-
-# Externally-called function    
-def compute_swing_selection(tuned_result_dir, layer_file):
-   
-    return computeLayerTargets(tuned_result_dir, layer_file)
-
-                            
-        
-                
-if __name__ == "__main__":
-
-    tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition.txt"
-
-    tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
-    layer_file = "layer_composition2.txt"
-    computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh b/hpvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh
deleted file mode 100755
index f67f22ebad5352d99238addd26d9e1b568ee2125..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-clang-4.0 -emit-llvm tensor_cpu_runtime.cc -S -o tensor_cpu_runtime.ll
-opt-4.0 -always-inline tensor_cpu_runtime.ll -S -o tensor_cpu_runtime.ll
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/time_jetson_profiles.py b/hpvm/projects/hpvm-tensor-rt/bin/time_jetson_profiles.py
deleted file mode 100644
index d0cde1e016fbbe67f9e98e43546bb3df38971f12..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/time_jetson_profiles.py
+++ /dev/null
@@ -1,256 +0,0 @@
-
-
-
-
-
-
-class Benchmark:
-  def __init__(self):
-    self.binary_path = ""
-    self.binary_time = 0
-    self.batch_time = 0
-    self.num_layers = 0
-    self.data_size = 0
-    self.num_classes = 0
-    self.batch_size = 50
-
-
-
-ResNet50 = Benchmark()
-ResNet50.binary_path = "resnet_imagenet"
-ResNet50.binary_time = 3.85 * 100  # 50 images * 100 batches
-ResNet50.batch_time = 3.85  # Time for batch with 50 images
-ResNet50.num_layers = 53
-ResNet50.data_size = 50 * 3 * 224 * 224 * 4   # *4 for Float32 Data
-ResNet50.num_classes = 1000
-ResNet50.batch_size = 50
-
-
-
-ResNet18 = Benchmark()
-ResNet18.binary_path = "resnet18_cifar10"
-#ResNet50.binary_time = 5.1 * 60  # 5.1 mins * 60 secs/min
-#ResNet18.binary_time = 12.9  # 50 images * 100 batches
-#ResNet18.batch_time = 12.9 / 50  # Time for batch with 50 images
-
-# Updated numbers based on batch_size = 50 -- NOTE: Underutilizes GPU - this can be better
-ResNet18.binary_time = 78  # 50 images * 100 batches
-ResNet18.batch_time = 78 / 100  # Time for batch with 50 images
-
-ResNet18.num_layers = 21
-ResNet18.data_size = 50 * 3 * 32 * 32 * 4   # *4 for Float32 Data
-ResNet18.num_classes = 10
-ResNet18.batch_size = 50
-
-
-
-MobileNet = Benchmark()
-MobileNet.binary_path = "mobilenet_cifar10"
-MobileNet.binary_time = 103.0  # 50 images * 100 batches
-MobileNet.batch_time = 103.0 / 100  # Time for batch with 50 images
-MobileNet.num_layers = 15
-MobileNet.data_size = 50 * 3 * 32 * 32 * 4   # *4 for Float32 Data
-MobileNet.num_classes = 10
-MobileNet.batch_size = 50
-
-
-
-VGG16_ImageNet = Benchmark()
-VGG16_ImageNet.binary_path = "vgg16_imagenet"
-#VGG16_ImageNet.binary_time = 10.6 * 60  # 5.1 mins * 60 secs/min
-VGG16_ImageNet.binary_time = 4.55 * 100  # 50 images * 100 batches
-VGG16_ImageNet.batch_time = 4.55
-VGG16_ImageNet.num_layers = 16
-VGG16_ImageNet.data_size = 50 * 3 * 224 * 224 * 4
-VGG16_ImageNet.num_classes = 1000
-VGG16_ImageNet.batch_size = 50
-
-
-VGG16_CIFAR10 = Benchmark()
-VGG16_CIFAR10.binary_path = "vgg16_cifar10"
-#VGG16_CIFAR10.binary_time = 19.0  # 50 images * 100 batches
-#VGG16_CIFAR10.batch_time = 19.0 /50
-
-# Updated numbers based on batch_size = 50 -- NOTE: Underutilizes GPU - this can be better
-VGG16_CIFAR10.binary_time = 55.7  # 50 images * 100 batches
-VGG16_CIFAR10.batch_time = 55.7 / 100
-
-VGG16_CIFAR10.num_layers = 15
-VGG16_CIFAR10.data_size = 50 * 3 * 32 * 32 * 4
-VGG16_CIFAR10.num_classes = 10
-VGG16_CIFAR10.batch_size = 50
-
-
-VGG16_CIFAR100 = Benchmark()
-VGG16_CIFAR100.binary_path = "vgg16_cifar100"
-VGG16_CIFAR100.binary_time = 55.7  # 50 images * 100 batches
-VGG16_CIFAR100.batch_time = 55.7 / 100
-VGG16_CIFAR100.num_layers = 15
-VGG16_CIFAR100.data_size = 50 * 3 * 32 * 32 * 4
-VGG16_CIFAR100.num_classes = 100
-VGG16_CIFAR100.batch_size = 50
-
-
-
-AlexNet_ImageNet = Benchmark()
-AlexNet_ImageNet.binary_path = "alexnet_imagenet"
-AlexNet_ImageNet.binary_time = 0.7 * 100  
-AlexNet_ImageNet.batch_time = 0.7
-AlexNet_ImageNet.num_layers = 8
-AlexNet_ImageNet.data_size = 50 * 3 * 224 * 224 * 4
-AlexNet_ImageNet.num_classes = 1000
-AlexNet_ImageNet.batch_size = 50
-
-
-
-AlexNet_CIFAR10 = Benchmark()
-AlexNet_CIFAR10.binary_path = "alexnet_cifar10"
-AlexNet_CIFAR10.binary_time = 23.52  
-AlexNet_CIFAR10.batch_time = 23.52 / 100 
-AlexNet_CIFAR10.num_layers = 6
-AlexNet_CIFAR10.data_size = 50 * 3 * 32 * 32 * 4
-AlexNet_CIFAR10.num_classes = 10
-AlexNet_CIFAR10.batch_size = 50
-
-
-AlexNet2_CIFAR10 = Benchmark()
-AlexNet2_CIFAR10.binary_path = "alexnet2_cifar10"
-AlexNet2_CIFAR10.binary_time = 27.1  
-AlexNet2_CIFAR10.batch_time = 27.1 / 100 
-AlexNet2_CIFAR10.num_layers = 7
-AlexNet2_CIFAR10.data_size = 50 * 3 * 32 * 32 * 4
-AlexNet2_CIFAR10.num_classes = 10
-AlexNet2_CIFAR10.batch_size = 50
-
-
-
-LeNet_CIFAR10 = Benchmark()
-LeNet_CIFAR10.binary_path = "lenet_keras"
-LeNet_CIFAR10.binary_time = 2.5  
-LeNet_CIFAR10.batch_time = 2.5 / 50 
-LeNet_CIFAR10.num_layers = 4
-LeNet_CIFAR10.data_size = 50 * 3 * 32 * 32 * 4
-LeNet_CIFAR10.num_classes = 10
-LeNet_CIFAR10.batch_size = 50
-
-
-
-
-
-
-# 100 batches with batch size of 50 each
-batch_count = 100
-promise_conf_runs = 30  # 30 runs for Statistical Confidence
-promise_prof_runs = 10  # 10 runs for error profile collection
-promise_knobs = 7
-
-total_machines = 100
-total_confs = 50
-download_time_per_1MB = (6.1 * 60) / 100  # 6.1 mins over 4G LTE network for 100 MB data upload
-upload_time_per_1MB = (26.4 * 60) / 100  # 26.4 mins over 4G LTE network for 100 MB data upload
-
-
-
-
-def getErrorProfileTime(Bench):
-
-    #time_per_batch = Bench.binary_time / batch_count
-
-    time_per_batch = Bench.batch_time
-    
-    total_knobs = promise_knobs * Bench.num_layers
-    total_runs = total_knobs * promise_prof_runs
-
-    promise_total_time = total_runs * time_per_batch
-
-    fp16_total_time = Bench.num_layers * time_per_batch
-
-    profile_time = promise_total_time + fp16_total_time
-
-    return profile_time
-
-    
-    
-
-
-def getConfTime(Bench):
-
-    conf_per_machine = promise_conf_runs * (total_confs * 1.0 / total_machines)
-    conf_time = conf_per_machine * Bench.binary_time
-    
-    return conf_time
-
-
-
-
-def getNetworkTime(Bench):
-
-    # Calibration Download Time
-    download_data_MB = Bench.data_size * 1.0 / 1000000  
-    download_data_time = download_data_MB * download_time_per_1MB
-
-    # Profile Uploading (to Cloud Server) Time
-    total_knobs = (promise_knobs + 1) * Bench.num_layers
-    profile_size = total_knobs * Bench.batch_size * Bench.num_classes * 4  # *4 for FP32 data
-
-    print ("  ")
-    print ("--- profile_size = ", profile_size)
-    profile_size_MB = profile_size * 1.0 / 1000000
-    upload_data_time = profile_size_MB * upload_time_per_1MB
-
-    network_time = download_data_time + upload_data_time
-
-    print( "network_time = ", download_data_time, upload_data_time, network_time)
-    return network_time
-
-  
-  
-
-def getTimeOnEdge(Bench):
-
-    err_time = getErrorProfileTime(Bench)
-    conf_time = getConfTime(Bench)
-    network_time = getNetworkTime(Bench)
-
-    total_time = err_time + conf_time + network_time
-    total_time = total_time / 60
-    
-    return total_time
-    
-
-
-
-if __name__ == "__main__":
-
-
-    resnet50_time = getTimeOnEdge(ResNet50)
-    print ("*** ResNet50 time (mins) = ", resnet50_time)
-
-    resnet18_time = getTimeOnEdge(ResNet18)
-    print ("*** ResNet18 time (mins) = ", resnet18_time)
-
-
-    mobilenet_time = getTimeOnEdge(MobileNet)
-    print ("*** MobileNet time (mins) = ", mobilenet_time)
-
-    
-    vgg16_img_time = getTimeOnEdge(VGG16_ImageNet)
-    print ("*** VGG16-Imagenet time (mins) = ", vgg16_img_time)
-
-    vgg16_cifar10_time = getTimeOnEdge(VGG16_CIFAR10)
-    print ("*** VGG16-CIFAR10 time (mins) = ", vgg16_cifar10_time)
-
-    vgg16_cifar100_time = getTimeOnEdge(VGG16_CIFAR100)
-    print ("*** VGG16-CIFAR100 time (mins) = ", vgg16_cifar100_time)
-
-    alexnet_img_time = getTimeOnEdge(AlexNet_ImageNet)
-    print ("*** AlexNet-Imagenet time (mins) = ", alexnet_img_time)
-
-    alexnet_cifar10_time = getTimeOnEdge(AlexNet_CIFAR10)
-    print ("*** AlexNet-CIFAR10 time (mins) = ", alexnet_cifar10_time)
-
-    alexnet2_cifar10_time = getTimeOnEdge(AlexNet2_CIFAR10)
-    print ("*** AlexNet2-CIFAR10 time (mins) = ", alexnet2_cifar10_time)
-
-    lenet_cifar10_time = getTimeOnEdge(LeNet_CIFAR10)
-    print ("*** LeNet-CIFAR10 time (mins) = ", lenet_cifar10_time)
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/times.py b/hpvm/projects/hpvm-tensor-rt/bin/times.py
deleted file mode 100644
index 082b0d91acb19e70a6c217b25f8747f3197b45b7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/times.py
+++ /dev/null
@@ -1,78 +0,0 @@
-
-
-
-class Config:
-  def __init__(self):
-    self.runtime = 0
-    self.fed_runs = 0
-    self.full_runs = 0
-    
-
-def computeTimes(bench):
-
-  conf_runs = 60
-  fed_time = (bench.runtime * 100) + (bench.fed_runs * conf_runs * bench.runtime)
-  fed_time_hrs = fed_time / (60*60)
-  
-  full_time = (bench.runtime * 1000) + (bench.full_runs * conf_runs * bench.runtime)
-  full_time_hrs = full_time / (60*60)
-    
-  print ("fedtime_hrs = ", fed_time_hrs, " full_time_hrs = ", full_time_hrs, "\n")
-  
-  
-
-if __name__ == "__main__":
-    
-
-  resnet = Config()
-  resnet.runtime = 8
-  resnet.fed_runs = 3
-  resnet.full_runs = 5
-
-  computeTimes(resnet)
-
-  alexnet = Config()
-  alexnet.runtime = 7.8
-  alexnet.fed_runs = 47
-  alexnet.full_runs = 274
-
-  computeTimes(alexnet)
-
-  alexnet2 = Config()
-  alexnet2.runtime = 2.3
-  alexnet2.fed_runs = 62
-  alexnet2.full_runs = 339
-
-  computeTimes(alexnet2)
-
-  vgg1 = Config()
-  vgg1.runtime = 7.4
-  vgg1.fed_runs = 15
-  vgg1.full_runs = 211
-
-  computeTimes(vgg1)
-  
-
-  vgg2 = Config()
-  vgg2.runtime = 15.4
-  vgg2.fed_runs = 8
-  vgg2.full_runs = 150
-
-  computeTimes(vgg2)
-  
-  
-  lenet = Config()
-  lenet.runtime = 0.98
-  lenet.fed_runs = 64
-  lenet.full_runs = 228
-
-  computeTimes(lenet)
-  
-  
-  mobilenet = Config()
-  mobilenet.runtime = 11
-  mobilenet.fed_runs = 32
-  mobilenet.full_runs = 267
-
-  computeTimes(mobilenet)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/tuner_postprocess.py b/hpvm/projects/hpvm-tensor-rt/bin/tuner_postprocess.py
deleted file mode 100644
index 6fc680973783f700ed0297279a4ab5802c15e8ab..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/tuner_postprocess.py
+++ /dev/null
@@ -1,523 +0,0 @@
-from sys import stderr
-
-output_perf = list(range(21, 30 + 1))
-input_sampling = list(range(31, 36 + 1))
-red_sampling = list(range(41, 46 + 1))
-groups = {
-    "fp32": [11],
-    "fp16": [12],
-    "perf": output_perf,
-    "samp": input_sampling,
-    "red_samp": red_sampling
-}
-# 11 -> 1, 12 -> 1
-param_remap = {
-    11: 1, 12: 1
-}
-fp32_fp16_remap = {
-    41: 42, 43: 44, 45: 46, 11: 12
-}
-inv_group = {v: k for k, vs in groups.items() for v in vs}
-speedups = {
-    11: 1.0,
-    12: 1.5,
-    21: 2.25,
-    22: 2.25,
-    23: 1.88,
-    24: 1.88,
-    25: 1.88,
-    26: 2.25,
-    27: 2.25,
-    28: 1.88,
-    29: 1.88,
-    30: 1.88,
-    31: 2.25,
-    32: 2.25,
-    33: 1.8,
-    34: 1.8,
-    35: 1.8,
-    36: 1.8,
-    41: 1.5,
-    42: 2.25,
-    43: 1.4,
-    44: 2,
-    45: 1.25,
-    46: 1.8
-}
-
-
-def read_list_of_ops(template_filepath):
-    from re import match
-    from itertools import dropwhile, takewhile
-    with template_filepath.open() as f:
-        all_lines = [line.strip() for line in f.readlines()]
-    head_chopped = list(dropwhile(
-        (lambda line: match(r"\++", line) is None), all_lines))[1:]
-    tail_chopped = list(takewhile(
-        (lambda line: match(r"-+", line) is None), head_chopped))
-    if not tail_chopped:
-        raise RuntimeError(
-            "Format error in file {}".format(template_filepath.as_posix())
-        )
-    op_lines = tail_chopped[1:]
-    ops = [line.split()[2] for line in op_lines]
-    return ops
-
-
-def read_op_costs(filepath):
-    with filepath.open() as f:
-        return [float(line.strip()) for line in f.readlines()]
-
-
-class Config(object):
-    def __init__(self, avg_acc, cost, speedup, values):
-        self.avg_acc, self.cost, self.speedup = avg_acc, cost, speedup
-        self.values = values
-
-    @classmethod
-    def from_file(cls, filepath, ops):
-        from re import match
-
-        with filepath.open() as f:
-            file_lines = f.readlines()
-        if not file_lines:
-            raise RuntimeError(
-                "Format error in file {}".format(filepath.as_posix()))
-        summary_line, config_lines = file_lines[0], file_lines[1:]
-        values = [int(l.strip()) for l in config_lines]
-        if len(values) != len(ops):
-            raise RuntimeError(
-                "Format error in file {}".format(filepath.as_posix()))
-
-        # Summary line format:
-        # avg_accuracy=34.5229	config_cost=818.838299524	speedup=2.08307548754
-        matched = match(
-            r"\s*avg_accuracy=([\d.]+)\s*config_cost=([\d.]+)\s*speedup=([\d.]+)\s*", summary_line
-        )
-        avg_acc, cost, speedup = [float(matched.group(i)) for i in range(1, 4)]
-
-        return cls(avg_acc, cost, speedup, values)
-
-    @classmethod
-    def from_tuner_conf(cls, tuner_conf):
-        speedup = tuner_conf.speedup
-        cost = 0
-        avg_acc = tuner_conf.acc
-        lines = []
-        for _, approx_name, v in tuner_conf.lines:
-            replacements = groups.get(approx_name, [])
-            if len(replacements) == 1:
-                lines.append(replacements[0])
-            else:
-                lines.append(v)
-        return cls(avg_acc, cost, speedup, lines)
-
-    @staticmethod
-    def calculate_cost(flags, baseline_costs):
-        total_cost = 0
-        for flag, cost in zip(flags, baseline_costs):
-            speedup = speedups.get(flag)
-            if speedup is None:
-                raise RuntimeError(f"Speedup of flag {flag} not given")
-            total_cost += cost / speedup
-        return total_cost
-
-    def remap_to_fp16(self, baseline_costs):
-        remapped = [fp32_fp16_remap.get(v, v) for v in self.values]
-        if len(baseline_costs) != len(remapped):
-            raise RuntimeError(
-                "Provided baseline_costs does not map one-on-one to ops")
-        old_cost_match = self.calculate_cost(self.values, baseline_costs)
-        if abs(self.cost - old_cost_match) > 1e-2:
-            raise RuntimeError(
-                "Cost computation mismatch. Probably reading wrong costs "
-                "or speedup params have changed"
-            )
-        new_cost = self.calculate_cost(remapped, baseline_costs)
-        speedup = (self.cost * self.speedup) / new_cost
-        return Config(self.avg_acc, new_cost, speedup, remapped)
-
-    def __repr__(self):
-        head = f"avg_accuracy={self.avg_acc}\tconfig_cost={self.cost}\tspeedup={self.speedup}"
-        body = "\n".join((str(v) for v in self.values))
-        return f"{head}\n{body}"
-
-
-class TunerConf(object):
-    psnr_upper_bound = 200
-
-    def __init__(self, speedup, energy, acc, acc_loss, lines, seq_id=0):
-        self.speedup = speedup
-        self.energy = energy
-        self.acc = acc
-        self.acc_loss = acc_loss
-        for l in lines:
-            if len(l) != 3:
-                raise RuntimeError(f"Line {l} is malformed")
-        self.lines = lines
-        self.seq_id = seq_id
-
-    @staticmethod
-    def get_baseline_conf(ops):
-        baseline = groups["fp32"][0]
-        baseline_config = Config(
-            avg_acc=TunerConf.psnr_upper_bound,
-            cost=0,  # won't be used by TunerConf
-            speedup=1.0,
-            values=[baseline for _ in range(len(ops))]
-        )
-        return TunerConf.from_config(ops, baseline_config, 0)
-
-    @classmethod
-    def from_config(cls, ops, config, seq_id):
-        if len(ops) != len(config.values):
-            raise RuntimeError(
-                f"Number of ops mismatch in {ops} and {config.values}"
-            )
-        lines = []
-        for o, v in zip(ops, config.values):
-            approx_name = inv_group.get(v)
-            if approx_name is None:
-                raise RuntimeError(f"Promise flag {v} is not understood")
-            lines.append((o, approx_name, v))
-        return cls(
-            speedup=config.speedup, energy=1.0,
-            acc=config.avg_acc, acc_loss=cls.psnr_upper_bound - config.avg_acc,
-            lines=lines, seq_id=seq_id
-        )
-
-    @classmethod
-    def many_from_file(cls, filepath):
-        def maybe_int(value, default=None):
-            try:
-                return int(value)
-            except ValueError:
-                return None
-
-        import re
-
-        with filepath.open() as f:
-            file_lines = f.read()
-        tuner_confs = []
-        for match in re.finditer(r"\++\n([^-]*)\n\-+", file_lines, re.MULTILINE):
-            meta, *config_lines = match.group(1).split('\n')
-            _, *stats = meta.split(' ')
-            speedup, energy, acc, acc_loss = [float(s) for s in stats]
-            configs = []
-            for line in config_lines:
-                _, _, op, approx, param = line.split(' ')
-                param = maybe_int(param, 1)
-                configs.append((op, approx, param))
-            tuner_confs.append(cls(speedup, energy, acc, acc_loss, configs))
-        return tuner_confs
-
-    def __repr__(self):
-        def repr_line(idx, line):
-            op, approx, param = line
-            param = param_remap.get(param, param)
-            return f"{idx + 1} gpu {op} {approx} {param}\n"
-
-        head = (
-            f"+++++\nconf{self.seq_id} {self.speedup:.4f} {self.energy:.4f} "
-            f"{self.acc:.4f} {self.acc_loss:.4f}\n"
-        )
-        tail = "-----"
-        printed_lines = "".join((
-            repr_line(i, line) for i, line in enumerate(self.lines)
-        ))
-        return head + printed_lines + tail
-
-
-def parse_config(filepath, ops, op_counter, config_summaries):
-    config = Config.from_file(filepath, ops)
-    config_summaries.append((config.speedup, config.avg_acc))
-    for v, name in zip(config.values, ops):
-        v_group = inv_group.get(v)
-        op_counter[name][v_group] += 1
-
-
-def plot_pareto_stats(pareto, others, save_to):
-    import matplotlib.pyplot as plt
-
-    if not pareto and not others:
-        return
-    p_xs, p_ys = zip(*pareto) if pareto else ([], [])
-    o_xs, o_ys = zip(*others) if others else ([], [])
-    scale = 10
-    alpha = 1
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    ax.scatter(p_xs, p_ys, c="green", label="pareto", s=scale, alpha=alpha)
-    ax.scatter(o_xs, o_ys, c="red", label="non-pareto", s=scale, alpha=alpha)
-    ax.set_xlabel("speedup")
-    ax.set_ylabel("avg_psnr")
-    ax.legend()
-    fig.savefig(save_to, dpi=200)
-
-
-def scan_config_dirs(configs_base_dir, ops):
-    from collections import Counter
-
-    all_configs_dir = configs_base_dir/"high_confidence"
-    pareto_dir = configs_base_dir/"pareto"
-    if not pareto_dir.is_dir():
-        print(
-            "No pareto directory found at {}; skipping".format(
-                pareto_dir.as_posix()),
-            file=stderr
-        )
-        pareto_confs = set()
-    else:
-        pareto_confs = set((p.name for p in pareto_dir.iterdir()))
-    
-    counters = {name: Counter() for name in set(ops)}
-    pareto_summaries, other_summaries = [], []
-    for filepath in all_configs_dir.iterdir():
-        filename = filepath.name
-        if filename in pareto_confs:
-            filepath = pareto_dir / filename
-            parse_config(filepath, ops, counters, pareto_summaries)
-        else:
-            parse_config(filepath, ops, counters, other_summaries)
-
-    return pareto_summaries, other_summaries, counters
-
-
-def translate_configs(configs_base_dir, ops):
-    from pathlib import Path
-
-    pareto_dir = configs_base_dir/"pareto"
-    output_file = configs_base_dir/"tuner_confs.txt"
-    baseline = str(TunerConf.get_baseline_conf(ops))
-    tuner_conf_strs = [baseline]
-    for i, config_path in enumerate(pareto_dir.iterdir()):
-        config = Config.from_file(config_path, ops)
-        tuner_conf = TunerConf.from_config(ops, config, i + 1)
-        tuner_conf_strs.append(str(tuner_conf))
-    with output_file.open('w') as f:
-        print("\n".join(tuner_conf_strs), file=f)
-
-
-def print_stats(args):
-    from pprint import pprint
-
-    ops = read_list_of_ops(args.bench_info/"tuner_conf_template.txt")
-    pareto, others, counters = scan_config_dirs(args.configs, ops)
-    if pareto:
-        plot_pareto_stats(pareto, others, args.configs/"pareto.png")
-        translate_configs(args.configs, ops)
-    pprint(counters)
-
-
-def run_binary(bin_path):
-    import subprocess
-    import os
-
-    fnull = open(os.devnull, 'wb')
-    p = subprocess.Popen(["./" + bin_path], stdout=fnull)
-    p.wait()
-    if p.returncode != 0:
-        # Something went wrong
-        print(
-            "Child program returned non-zero; you may want to stop and check.",
-            file=stderr
-        )
-
-
-def getPSNR(file_name):
-    with open(file_name) as f:
-        try:
-            raw_str = f.read()
-            violation, avg_psnr = [float(s) for s in raw_str.split(",")]
-        except:
-            return None, None
-    return 100 - violation, avg_psnr
-
-
-def run_validation(args):
-    from pathlib import Path
-    from shutil import copy
-    from tqdm import tqdm
-    ops = read_list_of_ops(args.bench_info/"tuner_conf_template.txt")
-    binary = Path(args.binary).resolve()
-    dump_path = args.dump_violation
-    if dump_path is not None and not dump_path.is_dir():
-        dump_path.mkdir()
-    configs = [p for p in args.configs.iterdir() if p.is_file()]
-    for config_path in tqdm(configs):
-        config = Config.from_file(config_path, ops)
-        promise_flags = binary.parent / "promise_flags"
-        with promise_flags.open('w') as f:
-            f.writelines((f"{v}\n" for v in config.values))
-        run_binary(args.binary)
-        success_rate, avg_psnr = getPSNR("final_accuracy")
-        tqdm.write(
-            f"config: {config_path.as_posix()}, "
-            f"success_rate = {success_rate}, "
-            f"avg_psnr = {config.avg_acc} -> {avg_psnr}"
-        )
-        if success_rate < args.threshold:
-            tqdm.write(
-                (
-                    "WARNING: config {} violates threshold on vaildation set; "
-                    "success_rate = {}, avg_psnr = {}"
-                ).format(config_path, success_rate, avg_psnr),
-                file=stderr
-            )
-            if dump_path is not None:
-                conf_name = config_path.name
-                copy(config_path.as_posix(), dump_path / conf_name)
-
-
-def remap_configs(args):
-    ops = read_list_of_ops(args.bench_info/"tuner_conf_template.txt")
-    costs = read_op_costs(args.bench_info/"op_cost.txt")
-    output_folder = args.configs.resolve().parent / "remapped"
-    if not output_folder.is_dir():
-        output_folder.mkdir()
-    for config_path in args.configs.iterdir():
-        config = Config.from_file(config_path, ops)
-        old_speedup = config.speedup
-        config = config.remap_to_fp16(costs)
-        print(f"speedup: {old_speedup} -> {config.speedup}")
-        output_path = output_folder / config_path.name
-        with output_path.open('w') as f:
-            f.write(str(config))
-    print(
-        "Finished.\n"
-        "Average psnr in files are not calibrated as it's impossible "
-        "without rerunning. Make sure to rerun the remapped configs.",
-        file=stderr
-    )
-
-
-def plot_compare_pareto(args):
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    org = TunerConf.many_from_file(args.original)
-    cali = TunerConf.many_from_file(args.calibrated)
-    org, cali = org[1:], cali[1:]  # remove baseline
-    if not org and not cali:
-        return
-    o_xs, o_ys = [tc.speedup for tc in org], [tc.acc for tc in org]
-    c_xs, c_ys = [tc.speedup for tc in cali], [tc.acc for tc in cali]
-
-    scale = 10
-    fig = plt.figure()
-
-    ax1 = fig.add_subplot(211)
-    ax1.scatter(o_xs, o_ys, c="red", label="predicted", s=scale)
-    ax1.scatter(c_xs, c_ys, c="green", label="calibrated", s=scale)
-    ax1.set_xlabel("speedup")
-    ax1.set_ylabel("avg_psnr")
-    ax1.legend()
-
-    ax2 = fig.add_subplot(212)
-    ax2.scatter(c_ys, np.array(c_xs) - np.array(o_xs), s=scale)
-    ax2.set_xlabel("avg_psnr")
-    ax2.set_ylabel("diff_speedup")
-
-    fig.savefig(args.output.as_posix(), dpi=200)
-
-
-def inv_translate(args):
-    tuner_confs = TunerConf.many_from_file(args.file)[1:]
-    configs = [Config.from_tuner_conf(tc) for tc in tuner_confs]
-    args.output_path.mkdir(exist_ok=True)
-    output = args.output_path/"high_confidence"
-    output.mkdir(exist_ok=True)
-    for i, conf in enumerate(configs):
-        with (output/f"{args.file.stem}_{i}").open('w') as f:
-            f.write(str(conf))
-
-
-def parse_args():
-    import argparse
-    from pathlib import Path
-
-    parser = argparse.ArgumentParser()
-    subparsers = parser.add_subparsers(
-        description="Valid subcommands", required=True, dest="subcommand"
-    )
-
-    stats_p = subparsers.add_parser(
-        "stats", help="Print out stats of a set of configs")
-    stats_p.add_argument(
-        "bench_info", type=Path,
-        help="Benchmark settings folder containing tuner_conf_template.txt"
-    )
-    stats_p.add_argument(
-        "configs", type=Path,
-        help="Configs folder. Should contain high_confidence (and optionally pareto) subfolders"
-    )
-    stats_p.set_defaults(func=print_stats)
-
-    cali_p = subparsers.add_parser(
-        "print_cali", help="Plot calibrated + original pareto curves")
-    cali_p.add_argument(
-        "original", type=Path, help="Original pareto curve"
-    )
-    cali_p.add_argument(
-        "calibrated", type=Path, help="Calibrated pareto curve"
-    )
-    cali_p.add_argument(
-        "-o", "--output", default="comparison.png",
-        type=Path, help="Path to output image"
-    )
-    cali_p.set_defaults(func=plot_compare_pareto)
-
-    ref_p = subparsers.add_parser(
-        "validation", help="Run validation on validation set(s)"
-    )
-    ref_p.add_argument(
-        "bench_info", type=Path,
-        help="Benchmark settings folder containing tuner_conf_template.txt"
-    )
-    ref_p.add_argument("binary", type=str, help="Path to binary")
-    ref_p.add_argument(
-        "configs", type=Path, help="Path to folder of configs to run"
-    )
-    ref_p.add_argument(
-        "-t", "--threshold", type=float, default=95.0,
-        help="Threshold of violation rate below which the test fails"
-    )
-    ref_p.add_argument(
-        "-o", "--dump_violation", type=Path, help="Place to dump violating configs"
-    )
-    ref_p.set_defaults(func=run_validation)
-
-    remap_p = subparsers.add_parser(
-        "remap", help="Remap fp32 to fp16"
-    )
-    remap_p.add_argument(
-        "bench_info", type=Path,
-        help="Benchmark settings folder containing tuner_conf_template.txt"
-    )
-    remap_p.add_argument(
-        "configs", type=Path, help="Path to folder of configs to remap"
-    )
-    remap_p.set_defaults(func=remap_configs)
-
-    trans_p = subparsers.add_parser(
-        "translate", help="Translate tuner conf back to autotuner format"
-    )
-    trans_p.add_argument(
-        "file", type=Path, help="Input file (one)"
-    )
-    trans_p.add_argument(
-        "output_path", type=Path, help="Output folder"
-    )
-    trans_p.set_defaults(func=inv_translate)
-
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-    args.func(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/hpvm/projects/hpvm-tensor-rt/bin/tuner_src b/hpvm/projects/hpvm-tensor-rt/bin/tuner_src
deleted file mode 120000
index f24dde48b6f885fd3783f453f514546e6e4a4ed1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/bin/tuner_src
+++ /dev/null
@@ -1 +0,0 @@
-../autotuner/tuner_driver_src/
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h b/hpvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h
index 500ff63bc86dce6cae0dee3f942639c07bf14ab3..5d1e0e66ad1a3402981682ed97e664ddcc173787 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h
@@ -13,9 +13,11 @@
 #include <tensor_runtime.h>
 #include <tensor.h>
 #include <cmath>
+#include <string.h>
 
 
 std::vector<float> run_accuracies;
+std::string model_params_path = "../../../build/model_params/";
 
 
 void printTensorInfo(void* tensor_ptr){
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_canny.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_canny.cc
deleted file mode 100644
index 628ce6616cde37a5eddde5ab6049001525203580..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_canny.cc
+++ /dev/null
@@ -1,255 +0,0 @@
-
-#include <stdio.h> 
-#include <stdlib.h> 
-#include <unistd.h> 
-#include <fcntl.h> 
-#include <sys/types.h> 
-#include <sys/stat.h>
-#include <vector>
-#include <string.h> 
-#include "tensor_runtime.h" 
-#include "utils.h" 
-
-#include "tensor_custom_ops_cpu.h"
-
-
-
-
-Tensor* gaussianFilter(float sigma, size_t w, size_t h, size_t n_chan) {
-  int64_t m = (w - 1) / 2, n = (h - 1) / 2;
-  auto *data = new float[w * h];
-  float sum = 0.0f;
-  for (int64_t i = -m; i <= m; i++)
-    for (int64_t j = -n; j <= n; j++) {
-      size_t idx = (i + m) * h + (j + n);
-      float exponent = -(i * i + j * j) / (2.0 * sigma * sigma);
-      data[idx] = exp(exponent);
-      sum += data[idx];
-    }
-  if (sum != 0.0f)
-    for (size_t i = 0; i < w * h; i++)
-      data[i] /= sum;
-  return (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, data, w, h, n_chan);
-}
-
-std::pair<Tensor*, Tensor*> getSobelKernels() {
-  std::vector<float> k1({-1, 0, 1, -2, 0, 2, -1, 0, 1});
-  std::vector<float> k2({1, 2, 1, 0, 0, 0, -1, -2, -1});
-  auto *t1 =
-      (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, k1.data(), 3, 3, 1);
-  auto *t2 =
-      (Tensor *)createFilterFromData(CUDNN_DATA_FLOAT, k2.data(), 3, 3, 1);
-  return std::make_pair(t1, t2);
-}
-
-/*** 
-
-TODOs:
-
-* Precision calculation?
-* tensorArgMax?
-* tensorSelect?
-* tensorContract
-* autotuning support for these functions
-* FP32 vs F16 versions of sampling perforation?
-* Need tensorRT version and a PROMISE API version
-* How to Profile? are profileEvent calls added
-* Pytorch version
-
-
-****/
-
-void* canny_filter(void* dataset) {
-
-  Tensor* gaussian = gaussianFilter(1.4, 5, 5, 1);
-  Tensor* kernel_x, *kernel_y;
-  std::tie(kernel_x, kernel_y) = getSobelKernels();
-
-  // 0. Grayscale
-  auto* summed_image = tensorReduce(dataset, 1, MathOp::Add);
-  auto* grayscale_image = tensorMap1(MathOp::Avg3, summed_image);
-  // 1. Denoise
-
-  auto* image2 = tensorConvolution(grayscale_image, gaussian,
-				   2, 2, // padding
-				   1, 1, // strides
-				   1, 0); // conv_mode, conv_groups
-				    
-  // 2. Get edge gradient / direction
-  auto *grad_x = tensorConvolution(image2, kernel_x,
-				   1, 1,
-				   1, 1,
-				   1, 0);
-   
-  auto *grad_y = tensorConvolution(image2, kernel_y,
-				   1, 1,
-				   1, 1,
-				   1, 0);
- 
-  auto *grad_mag = tensorMap2(MathOp::Hypot, grad_x, grad_y);
-  // 2.5. Normalize grad magnitude
-  auto *grad_max_1D = tensorReduce(grad_mag, 2, MathOp::Max);
-  auto *grad_max = tensorReduce(grad_max_1D, 3, MathOp::Max);
-  auto *grad_mag_norm = tensorMap2(MathOp::Div, grad_mag, grad_max);
-  return grad_mag_norm;
-}
-
-
-
-
-void* invoke_canny(void* input) {
-  
-  auto* result = canny_filter(input);
-
-  printf("Done with Canny \n");
-  
-  return result;
-}
-
-
-
-
-
-
-
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(0); 
-
-  std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/"); 
-  std::string input_path =  dir_prefix + std::string("norm_cifar_input.bin"); 
-  std::string canny_input_path =  dir_prefix + std::string("canny_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);  
- 
-
-  int test_input_size = 5000;
-  int batch_size = 500;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  // NOTE: Starting time profiling
-  startProfiling();  
-  startMemTracking();
-
-  int conv_mode = 1; // NOTE: using CROSS_CORRELATION
-  int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-  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* canny_input = readInputBatch(canny_input_path.c_str(), 0,start,end, 3, 128, 128);
-
-    void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
-				       conv_mode, conv_precision);
-    tensorAdd(conv1out, conv1_bias); 
-    void* conv1_tanh = tensorTanh(conv1out);
-    
-    // 2nd Layer
-    void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
-				       conv_mode, conv_precision);
-    tensorAdd(conv2out, conv2_bias); 
-    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,
-				       conv_mode, conv_precision);
-    tensorAdd(conv3out, conv3_bias); 
-    void* conv3_tanh = tensorTanh(conv3out);
-
-    // 4th Layer
-    void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
-				       conv_mode, conv_precision);
-    tensorAdd(conv4out, conv4_bias); 
-    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,
-				       conv_mode, conv_precision);
-    tensorAdd(conv5out, conv5_bias); 
-    void* conv5_tanh = tensorTanh(conv5out);
-
-    // 6th Layer
-    void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
-				       conv_mode, conv_precision);
-    tensorAdd(conv6out, conv6_bias); 
-  
-    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* result = tensorSoftmax(gemm1biasout);
-
-    uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end); 
-
-    float accuracy = computeAccuracy2(labels, batch_size, result); 
-    final_accuracy += accuracy;
-
-
-    std::vector<int> index_vector;
-    index_vector.push_back(1);
-    index_vector.push_back(2);
-    index_vector.push_back(3);
-    index_vector.push_back(4);
-    index_vector.push_back(5);
-    
-    
-    void* argmax_out = tensorArgMax(result);
-    void* select_out = tensorSelect2(argmax_out, index_vector);
-    void* reduced_input = tensorContract(canny_input, select_out);
-
-    invoke_canny(reduced_input);
-    
-
-    freeBatchMemory();    
-  }
-
-  stopProfiling();
-
-  final_accuracy = final_accuracy / batch_count;
-  dumpFinalAccuracy(final_accuracy);
-
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet2_cifar10_half.cc
similarity index 98%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet2_cifar10_half.cc
index 161cdd249cc1e94f0a739772e0b9b9ea86993be8..d93110945b1d1a70ec29c7788d9133dc16551ee5 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet2_cifar10_half.cc
@@ -18,7 +18,7 @@ void testCifarNet(){
 
   printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
  
-  std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/alexnet2_cifar10/"); 
   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");
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet_cifar10_half.cc
similarity index 98%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet_cifar10_half.cc
index 8a429862f34f95793dd9ca7caa619b10dbe568ab..b7695bbd7a24712e335f0cf8bbd25290f3261dea 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet_cifar10_half.cc
@@ -14,7 +14,7 @@ int main(){
   llvm_hpvm_initTensorRt(0); 
 
 
-  std::string dir_prefix = std::string("../model_params/alexnet_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/alexnet_cifar10/"); 
 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   std::string labels_path =  dir_prefix + std::string("labels.bin");
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_mnist_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
similarity index 97%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_mnist_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
index f04ec041644394e2258414575162b961f9849667..29f392c630a36a6044c5f804e5d3a7b252591831 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_mnist_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
@@ -21,7 +21,7 @@ void testLenetTanh(){
 
   int test_batch_size = 5000;
 
-  std::string dir_prefix = std::string("../model_params/lenet_mnist/");   
+  std::string dir_prefix = model_params_path + std::string("/lenet_mnist/");   
 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   std::string labels_path =  dir_prefix + std::string("labels.bin"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
index dabafd4345f29d00c7271c796a8497aba8b7772d..d662dc1584c7810d8d3631d5ac16c427c3ff8b02 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
@@ -14,7 +14,7 @@ int main(){
     llvm_hpvm_initTensorRt(0); 
 
 
-    std::string dir_prefix = std::string("../model_params/mobilenet/"); 
+    std::string dir_prefix = model_params_path + std::string("/mobilenet/"); 
     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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/resnet18_cifar10_half.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/resnet18_cifar10_half.cc
index 9779b95d865d1939244f50c3910d7ed770b0729d..741c4a443cc9a56c443ec5858aaed5a7d5705268 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/resnet18_cifar10_half.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(0); 
   
-  std::string dir_prefix = std::string("../model_params/resnet18_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/resnet18_cifar10/"); 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32); 
   std::string labels_path =  dir_prefix + std::string("labels.bin"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
index 7107defe9d154731a46efaf5c8ad244ceb69bad7..9ac1deea68c693f8baf2df2d9f2b626b3597ad7f 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
@@ -13,7 +13,7 @@ int main(){
 
     llvm_hpvm_initTensorRt(0); 
 
-    std::string dir_prefix = std::string("../model_params/vgg16_cifar100/"); 
+    std::string dir_prefix = model_params_path + std::string("/vgg16_cifar100/"); 
     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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
index 45e74fbe32e053e2d43c1dde0f90460c21ab0118..f92bac10e27162fe0bc59c07aa4f9ede542ccd6e 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(0); 
 
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/vgg16_cifar10/"); 
   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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet2_cifar10.cc
similarity index 98%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet2_cifar10.cc
index 5918f4f18ebdb7d4f2fa3e37c0982b8ed8d10932..50d9747f990d486c4543607d16d4a4ccb88b0517 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet2_cifar10.cc
@@ -19,7 +19,7 @@ void testCifarNet(){
   printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
  
 
-  std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/"); 
+  std::string dir_prefix = model_params_path +  std::string("/alexnet2_cifar10/"); 
   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");
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_cifar10.cc
similarity index 98%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_cifar10.cc
index 8129fbfafcdd3e991e67d33fd3013e1700da45c5..1a76f1ae8ba6059124117b82cd72e8ccd6cdeba6 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_cifar10.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(0); 
 
-  std::string dir_prefix = std::string("../model_params/alexnet_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/alexnet_cifar10/"); 
 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   std::string labels_path =  dir_prefix + std::string("labels.bin");
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_imagenet.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_imagenet.cc
similarity index 100%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_imagenet.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_imagenet.cc
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_mnist.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
similarity index 97%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_mnist.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
index c047ffe090a93711cb66973ef6622d46fccdcee3..7508f3119eeb469a164fad9741000308e3e8c031 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_mnist.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
@@ -22,7 +22,7 @@ void testLenetTanh(){
 
   int test_batch_size = 5000;
 
-  std::string dir_prefix = std::string("../model_params/lenet_mnist/");   
+  std::string dir_prefix = model_params_path + std::string("/lenet_mnist/");   
 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   std::string labels_path =  dir_prefix + std::string("labels.bin"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/mobilenet.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/mobilenet.cc
index 78ca2dac98435ea146da44a78bb2f7405af8c5ef..7c311a568647caa107112bed4982fb57254dc7b3 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/mobilenet.cc
@@ -15,7 +15,7 @@ int main(){
   llvm_hpvm_initTensorRt(0); 
 
 
-  std::string dir_prefix = std::string("../model_params/mobilenet/"); 
+  std::string dir_prefix = model_params_path + std::string("/mobilenet/"); 
   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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet18_cifar10.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet18_cifar10.cc
index b0c868085bae1abc2025364609114cc21c7d213a..87b8cd4156ed8d7f882ff7642420c995cd7c3a0f 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet18_cifar10.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(1); 
   
-  std::string dir_prefix = std::string("../model_params/resnet18_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/resnet18_cifar10/"); 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32); 
   std::string labels_path =  dir_prefix + std::string("labels.bin"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet50_imagenet.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet50_imagenet.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet50_imagenet.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet50_imagenet.cc
index 1192d04de200c8e8183c35861da2d04aa705e955..0914b3f70c353ee7e56c39ccf52f21914618301e 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet50_imagenet.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/resnet50_imagenet.cc
@@ -15,7 +15,7 @@ int main(){
   llvm_hpvm_initTensorRt(0); 
 
 
-  std::string dir_prefix = std::string("/shared/hsharif3/resnet50_imagenet_tune/"); 
+  std::string dir_prefix = model_params_path + std::string("/shared/hsharif3/resnet50_imagenet/"); 
   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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar10.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar10.cc
index e8469e8a4892f51337118e4699f09ae98c13bf71..a6dc7cbc11cf77357a749bff117489fc4b292941 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar10.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(0); 
 
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar10/"); 
+  std::string dir_prefix = model_params_path + std::string("/vgg16_cifar10/"); 
   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"); 
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar100.cc
similarity index 99%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar100.cc
index 0290a2782880c1aa8c1ea33f5564926665d968d6..2539f8d8722909724a9dc2890e82f4f98853f5cd 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_cifar100.cc
@@ -13,7 +13,7 @@ int main(){
 
   llvm_hpvm_initTensorRt(0); 
 
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar100/"); 
+  std::string dir_prefix = model_params_path + std::string("/vgg16_cifar100/"); 
   std::string input_path =  dir_prefix + std::string("input.bin"); 
   std::string labels_path =  dir_prefix + std::string("labels.bin");
   
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_imagenet.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_imagenet.cc
similarity index 100%
rename from hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_imagenet.cc
rename to hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/vgg16_imagenet.cc
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_half.cc
deleted file mode 100644
index 7ce9a90e10697c979adc470345244a2cc326f0cb..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_half.cc
+++ /dev/null
@@ -1,235 +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(int argc, char* argv[]){ 
-
-    llvm_hpvm_initTensorRt(0); 
-
-
-    std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
-    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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-    void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-    void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-    void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-    void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-    std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-    void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-    std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-    void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-    void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-    void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-    std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-    void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-    std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-    void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-    void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-    void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-    void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-    std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-    void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-    std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-    void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-    void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-    void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-    std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-    void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-    std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-    void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-    void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-    void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-    void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-    std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-    void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-    std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-    void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-    void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-    void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-    void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-    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(), 0,128,128,1,1); 
-    std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-    void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-    void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-    void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-    void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-    std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-    void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-    std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-    void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-    void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-    void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-    std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-    void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-    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(), 0,256,128,1,1); 
-    std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-    void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-    void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-    void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-    void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-    std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-    void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-    std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-    void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-    void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-    void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-    void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-    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(), 0,256,256,1,1); 
-    std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-    void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-    void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-    void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-    void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-    std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-    void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-    std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-    void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-    void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-    void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-    std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-    void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-    std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-    void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-    std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-    void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-    std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-    void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-    std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-    void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-    std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-    void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-    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); 
-
-
-    startMemTracking(); 
-
-    int test_input_size = 2000; 
-    int batch_size = 1000; 
-    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 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1); 
-        void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-        void* var_2 = tensorHalfRelu(var_1); 
-        void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-        void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-        void* var_6 = tensorHalfRelu(var_5); 
-        void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-        void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-        void* var_9 = tensorHalfRelu(var_8); 
-        void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-        void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-        void* var_13 = tensorHalfRelu(var_12); 
-        void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-        void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-        void* var_16 = tensorHalfRelu(var_15); 
-        void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-        void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-        void* var_20 = tensorHalfRelu(var_19); 
-        void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-        void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-        void* var_23 = tensorHalfRelu(var_22); 
-        void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-        void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-        void* var_28 = tensorHalfRelu(var_27); 
-        void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-        void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-        void* var_31 = tensorHalfRelu(var_30); 
-        void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-        void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-        void* var_35 = tensorHalfRelu(var_34); 
-        void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-        void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-        void* var_38 = tensorHalfRelu(var_37); 
-        void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-        void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-        void* var_43 = tensorHalfRelu(var_42); 
-        void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-        void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-        void* var_46 = tensorHalfRelu(var_45); 
-        void* var_47 = tensorHalfPooling(var_46,1,2,2,0,0,2,2); 
-        void* var_49 = tensorHalfGemmGPU(var_47, dense_1_w); 
-        void* var_50 = tensorHalfAdd(var_49, dense_1_b); 
-        void* var_51 = tensorSoftmax(var_50); 
-
-        uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-        float accuracy = computeAccuracy2(labels, batch_size, var_51); 
-        final_accuracy += accuracy; 
-        freeBatchMemory(); 
-
-    }
-
-    final_accuracy = final_accuracy / batch_count;
-    dumpFinalAccuracy(final_accuracy);
-
-    dumpExecutionAccuracies();
-
-    llvm_hpvm_cleanupTensorRt(); 
-
-    return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
deleted file mode 100644
index 82fe03247f36dbe6de31205a60344b7f44f85bad..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,169 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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"
-
-/* NOTE: Reference Architecture to use for profiling */
-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);  
- 
-  
-  int conv_mode = 1; // NOTE: using CROSS_CORRELATION
-  int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-
-  startMemTracking();
-
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  int total_runs = 10;
-
-  // NOTE: Starting time profiling
-  startProfiling();
-
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-  double total_energy = 0.0;
-
-  for (int i = 0; i < total_runs; i++){  
-	  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);
-
-      	profiler.resume_profiler();
-		
-		void* conv1out = tensorHalfConvolution(input, conv1_filter, 1, 1, 1, 1,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv1out, conv1_bias); 
-		void* conv1_tanh = tensorHalfTanh(conv1out);
-		
-		// 2nd Layer
-		void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv2out, conv2_bias); 
-		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,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv3out, conv3_bias); 
-		void* conv3_tanh = tensorHalfTanh(conv3out);
-
-		// 4th Layer
-		void* conv4out = tensorHalfConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv4out, conv4_bias); 
-		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,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv5out, conv5_bias); 
-		void* conv5_tanh = tensorHalfTanh(conv5out);
-
-		// 6th Layer
-		void* conv6out = tensorHalfConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
-						   conv_mode, conv_precision);
-		tensorHalfAdd(conv6out, conv6_bias); 
-	  
-		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* result = tensorSoftmax(gemm1biasout);
-
-		profiler.pause_profiler();
-		auto time_energy = profiler.get_time_energy();
-		total_time += time_energy.first;
-		total_energy += time_energy.second;
-
-        profiler.reset();
-
-		uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end); 
-
-		float accuracy = computeAccuracy2(labels, batch_size, result); 
-		final_accuracy += accuracy;
-		
-    	freeBatchMemory();
-    }
-  }
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"Average energy: " << total_energy / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  stopProfiling();
-
-  final_accuracy = final_accuracy / batch_count / total_runs;
-  dumpFinalAccuracy(final_accuracy);
-
-}
-
-
-int main(int argc, char* argv[]){
-
-  llvm_hpvm_initTensorRt(0);
-
-  testCifarNet();
-
-  llvm_hpvm_cleanupTensorRt();
-
-  return 0;
-}
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
deleted file mode 100644
index 965e3170ea5c9df7dec1abe13d06581fe56f3b21..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,126 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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("../model_params/alexnet_cifar10_front/"); 
-  std::string input_path =  dir_prefix + std::string("input.bin"); 
-  //void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-  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("conv0.bin"); 
-  void* conv2d_1_w =  readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11); 
-  std::string conv2d_1_b_path =  dir_prefix + std::string("conv_bias0.bin"); 
-  void* conv2d_1_b =  readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_2_w_path =  dir_prefix + std::string("conv3.bin"); 
-  void* conv2d_2_w =  readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5); 
-  std::string conv2d_2_b_path =  dir_prefix + std::string("conv_bias3.bin"); 
-  void* conv2d_2_b =  readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1); 
-  std::string conv2d_3_w_path =  dir_prefix + std::string("conv6.bin"); 
-  void* conv2d_3_w =  readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3); 
-  std::string conv2d_3_b_path =  dir_prefix + std::string("conv_bias6.bin"); 
-  void* conv2d_3_b =  readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1); 
-  std::string conv2d_4_w_path =  dir_prefix + std::string("conv7.bin"); 
-  void* conv2d_4_w =  readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3); 
-  std::string conv2d_4_b_path =  dir_prefix + std::string("conv_bias7.bin"); 
-  void* conv2d_4_b =  readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_5_w_path =  dir_prefix + std::string("conv8.bin"); 
-  void* conv2d_5_w =  readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_5_b_path =  dir_prefix + std::string("conv_bias8.bin"); 
-  void* conv2d_5_b =  readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1); 
-  std::string dense_1_w_path =  dir_prefix + std::string("fc12.bin"); 
-  void* dense_1_w =  readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10); 
-  std::string dense_1_b_path =  dir_prefix + std::string("fc_bias12.bin"); 
-  void* dense_1_b =  readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1); 
-
-
-  startMemTracking();
-
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  int total_runs = 10;
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-  double total_energy = 0.0;
-
-  // NOTE: Starting time profiling
-  startProfiling();
-  for (int i = 0; i < total_runs; i++){  
-      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);    
-
-        profiler.resume_profiler();
-        void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0); 
-        void* var_1 = tensorHalfAdd(var_0, conv2d_1_b); 
-        void* var_2 = tensorHalfTanh(var_1); 
-        void* var_3 = tensorHalfPooling(var_2,0,2,2,0,0,2,2); 
-        void* var_5 = tensorHalfConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0); 
-        void* var_6 = tensorHalfAdd(var_5, conv2d_2_b); 
-        void* var_7 = tensorHalfTanh(var_6); 
-        void* var_8 = tensorHalfPooling(var_7,0,2,2,0,0,2,2); 
-        void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-        void* var_11 = tensorHalfAdd(var_10, conv2d_3_b); 
-        void* var_12 = tensorHalfTanh(var_11); 
-        void* var_13 = tensorHalfConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-        void* var_14 = tensorHalfAdd(var_13, conv2d_4_b); 
-        void* var_15 = tensorHalfTanh(var_14); 
-        void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-        void* var_17 = tensorHalfAdd(var_16, conv2d_5_b); 
-        void* var_18 = tensorHalfTanh(var_17); 
-        void* var_19 = tensorHalfPooling(var_18,0,2,2,0,0,2,2); 
-        void* var_22 = tensorHalfGemmGPU(var_19, dense_1_w); 
-        void* var_23 = tensorHalfAdd(var_22, dense_1_b); 
-        void* var_24 = tensorSoftmax(var_23); 
-
-        profiler.pause_profiler();
-        auto time_energy = profiler.get_time_energy();
-        total_time += time_energy.first;
-        total_energy += time_energy.second;
-        profiler.reset();
-
-        uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end); 
-
-        float accuracy = computeAccuracy2(labels,batch_size,var_24); 
-        final_accuracy += accuracy;
-        
-        freeBatchMemory();
-      }
-  }
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"Average energy: " << total_energy / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  stopProfiling();
-
-  final_accuracy = final_accuracy / batch_count / total_runs;
-  dumpFinalAccuracy(final_accuracy);
-
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc
deleted file mode 100644
index e6ffd6b03de4901780511e56afdb5faac85bb807..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc
+++ /dev/null
@@ -1,186 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
-  int total_runs = 10;
-
-  
-  printf("********* Lenet-2 Architecture ********** \n");
-  // FIXIT: Extend this to batch of images - currently 5 images
-
-  int test_batch_size = 5000;
-
-  uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-  
-  void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
-				CUDNN_DATA_FLOAT,
-				test_batch_size, 1, 28, 28);
-
-  // NOTE: Filter descriptors do NOT have batch size
-  // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
-  // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
-  void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
-					  float_type, 32, 1, 5, 5);    
-  void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
-					float_type, 1, 32, 1, 1);  
-  void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
-					  float_type, 64, 32, 5, 5);  
-  void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
-					float_type, 1, 64, 1, 1);  
-  void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
-					 float_type, 1, 1, 7*7*64, 1024);  
-  void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
-				      float_type, 1, 1024, 1, 1);  
-  void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
-					 float_type, 1, 1, 1024, 10);  
-  void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
-				      float_type, 1, 10, 1, 1);  
-
-
-  
-  clearTensorMap();
- 
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-  float final_accuracy = 0.0;
-
-  for(int i = 0; i < total_runs; i++){
-
-    if(Opentuner_run){
-
-      char* myfifo = "/tmp/myfifo";
-      int fd = open(myfifo, O_RDONLY);
-
-      int ret_val = fcntl(fd, F_GETFD);
-      if(ret_val == -1){
-	printf("Invalid descriptor \n");
-	abort();
-      }
-
-      char str[100];
-      read(fd, str, 80);
-      if(strcmp(str, "stop_run") == 0){
-	abort();
-      }
-
-      close(fd);
-    }
-
-    
-    readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-    // Start power and performnce profiling 
-    profiler.resume_profiler();
-    startProfiling();
-
-    int conv_mode = 1; // NOTE: using CROSS_CORRELATION
-    int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-    // NOTE: 'SAME' convolution
-    void* conv1out = tensorHalfConvolution(input, conv1_filter, 2, 2, 1, 1,
-				       conv_mode, conv_precision);
-
-    // NOTE: For tensorAdd, the only dimension that MUST match is channels  
-    tensorHalfAdd(conv1out, conv1_bias); // NOTE: In place operation
-
-    void* pool1out = tensorHalfPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
-    void* conv1_tanh = tensorHalfTanh(pool1out);
-
-    // NOTE: input channels have to match between tensor op inputs and outputs 
-    void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
-				       conv_mode, conv_precision);
-    tensorHalfAdd(conv2out, conv2_bias); // NOTE: In place operation
-
-    void* pool2out = tensorHalfPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
-    void* conv2_tanh = tensorHalfTanh(pool2out);
-
-    void* gemm1out = tensorHalfGemm(conv2_tanh, fc1_weights);  
-
-    void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
-
-    void* tanh1out = tensorHalfTanh(gemm1biasout);
-  
-    void* gemm2out = tensorHalfGemm(tanh1out, fc2_weights);  
-  
-    void* gemm2_biasout = tensorHalfAdd(gemm2out, fc2_bias);
-
-    void* tanh2out = tensorHalfTanh(gemm2_biasout);
-  
-    void* result = tensorSoftmax(tanh2out);
-
-    profiler.pause_profiler();
-    auto time_energy = profiler.get_time_energy();
-    total_time += time_energy.first;
-    profiler.reset();
-
-    // End profiling and dump output to profile.txt
-    stopProfiling();
-  
-    float accuracy = computeAccuracy2(labels, test_batch_size, result);
-    final_accuracy += accuracy;
-    dumpAccuracyNorms();
-    freeOutputTensors();  
-
-    if(Opentuner_run){
-
-      char* myfifo = "/tmp/myfifo";
-      int fd_out = open(myfifo, O_WRONLY);
-      int ret_val = fcntl(fd_out, F_GETFD);
-      if(ret_val == -1){
-	printf("Invalid descriptor \n");
-	abort();
-      }
-      
-      const char* str = "completed***!\n\0";
-      write(fd_out, str, 80);
-      close(fd_out);
-    }
-    
-  }
-
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  final_accuracy = final_accuracy / total_runs;
-  dumpFinalAccuracy(final_accuracy);
-}
-
-
-int main(int argc, char* argv[]){
-
-  if(argc > 1)
-    Opentuner_run = true;
-
-  llvm_hpvm_initTensorRt(0);
-
-  testLenetTanh();
-
-  llvm_hpvm_cleanupTensorRt();
-
-  return 0;
-}
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
deleted file mode 100644
index 641047b50dc1219f1d02bbfb75e2014840c90d96..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
+++ /dev/null
@@ -1,416 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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("../model_params/mobilenet/"); 
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-  void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-  void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-  void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-  void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-  void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-  void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-  void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-  void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-  void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-  void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-  void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-  void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-  void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-  void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-  void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-  void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-  void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-  void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-  void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-  void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-  void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-  void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-  void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-  void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-  void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-  void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-  void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-  void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-  void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-  void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-  void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-  void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-  void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-  void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-  void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-  void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-  void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-  void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-  void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-  void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-  void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-  void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-  void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-  void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-  void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-  void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-  void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-  void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-  void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-  void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-  std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-  void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-  void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-  void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-  void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-  void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-  std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-  void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-  void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-  void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-  void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-  std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-  void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-  void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-  void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-  void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-  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(), 0,1,1,1024,10); 
-  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); 
-
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;  
-  int batch_count = test_input_size / batch_size; 
-
-  int total_runs = 10;
-  float final_accuracy = 0.0; 
-
-  for (int run_num = 0; run_num < total_runs; run_num++){
-      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 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1); 
-        void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-        void* var_2 = tensorHalfRelu(var_1); 
-        void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-        void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-        void* var_6 = tensorHalfRelu(var_5); 
-        void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-        void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-        void* var_9 = tensorHalfRelu(var_8); 
-        void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-        void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-        void* var_13 = tensorHalfRelu(var_12); 
-        void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-        void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-        void* var_16 = tensorHalfRelu(var_15); 
-        void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-        void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-        void* var_20 = tensorHalfRelu(var_19); 
-        void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-        void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-        void* var_23 = tensorHalfRelu(var_22); 
-        void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-        void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-        void* var_28 = tensorHalfRelu(var_27); 
-        void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-        void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-        void* var_31 = tensorHalfRelu(var_30); 
-        void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-        void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-        void* var_35 = tensorHalfRelu(var_34); 
-        void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-        void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-        void* var_38 = tensorHalfRelu(var_37); 
-        void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-        void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-        void* var_43 = tensorHalfRelu(var_42); 
-        void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-        void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-        void* var_46 = tensorHalfRelu(var_45); 
-        void* var_48 = tensorHalfConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-        void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-        void* var_50 = tensorHalfRelu(var_49); 
-        void* var_51 = tensorHalfConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1); 
-        void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-        void* var_53 = tensorHalfRelu(var_52); 
-        void* var_55 = tensorHalfConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-        void* var_56 = tensorHalfBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-        void* var_57 = tensorHalfRelu(var_56); 
-        void* var_58 = tensorHalfConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1); 
-        void* var_59 = tensorHalfBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-        void* var_60 = tensorHalfRelu(var_59); 
-        void* var_63 = tensorHalfConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-        void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-        void* var_65 = tensorHalfRelu(var_64); 
-        void* var_66 = tensorHalfConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1); 
-        void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-        void* var_68 = tensorHalfRelu(var_67); 
-        void* var_70 = tensorHalfConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-        void* var_71 = tensorHalfBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-        void* var_72 = tensorHalfRelu(var_71); 
-        void* var_73 = tensorHalfConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1); 
-        void* var_74 = tensorHalfBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-        void* var_75 = tensorHalfRelu(var_74); 
-        void* var_77 = tensorHalfConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-        void* var_78 = tensorHalfBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-        void* var_79 = tensorHalfRelu(var_78); 
-        void* var_80 = tensorHalfConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1); 
-        void* var_81 = tensorHalfBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-        void* var_82 = tensorHalfRelu(var_81); 
-        void* var_85 = tensorHalfConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-        void* var_86 = tensorHalfBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-        void* var_87 = tensorHalfRelu(var_86); 
-        void* var_88 = tensorHalfConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1); 
-        void* var_89 = tensorHalfBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-        void* var_90 = tensorHalfRelu(var_89); 
-        void* var_92 = tensorHalfConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-        void* var_93 = tensorHalfBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-        void* var_94 = tensorHalfRelu(var_93); 
-        void* var_95 = tensorHalfConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1); 
-        void* var_96 = tensorHalfBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-        void* var_97 = tensorHalfRelu(var_96); 
-        void* var_99 = tensorHalfPooling(var_97,1,2,2,0,0,2,2); 
-        void* var_101 = tensorHalfGemmGPU(var_99, dense_1_w); 
-        void* var_102 = tensorHalfAdd(var_101, dense_1_b); 
-        void* var_103 = tensorSoftmax(var_102); 
-
-        uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-        float accuracy = computeAccuracy2(labels, batch_size, var_103); 
-        final_accuracy += accuracy; 
-        freeBatchMemory(); 
-      }
-  }
-  final_accuracy = final_accuracy / batch_count; 
-  dumpFinalAccuracy(final_accuracy); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc
deleted file mode 100644
index 1c6a3955b1ad644363947106bb0f77d6b9a77050..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc
+++ /dev/null
@@ -1,438 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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("../model_params/mobilenet_quant/"); 
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-  void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-  void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-  void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-  void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-  void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-  void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-  void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-  void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-  void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-  void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-  void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-  void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-  void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-  void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-  void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-  void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-  void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-  void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-  void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-  void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-  void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-  void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-  void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-  void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-  void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-  void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-  void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-  void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-  void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-  void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-  void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-  void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-  void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-  void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-  void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-  void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-  void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-  void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-  void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-  void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-  void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-  void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-  void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-  void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-  void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-  void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-  void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-  void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-  void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-  void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-  std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-  void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-  void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-  void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-  void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-  void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-  std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-  void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-  void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-  void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-  void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-  std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-  void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-  void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-  void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-  void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-  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(), 0,1,1,1024,10); 
-  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); 
-
-
-  startMemTracking(); 
-
-  startProfiling();
-  
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size; 
-  float final_accuracy = 0.0; 
-
-  int total_runs = 10;
-
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-
-  for(int i = 0; i < total_runs; i++){
-  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); 
-
-    profiler.resume_profiler();
-    void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1); 
-    void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-    void* var_2 = tensorHalfRelu(var_1); 
-    void* var_4 = tensorHalfConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-    void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-    void* var_6 = tensorHalfRelu(var_5); 
-    void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-    void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-    void* var_9 = tensorHalfRelu(var_8); 
-    void* var_11 = tensorHalfConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-    void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-    void* var_13 = tensorHalfRelu(var_12); 
-    void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-    void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-    void* var_16 = tensorHalfRelu(var_15); 
-    void* var_18 = tensorHalfConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-    void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-    void* var_20 = tensorHalfRelu(var_19); 
-    void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-    void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-    void* var_23 = tensorHalfRelu(var_22); 
-    void* var_26 = tensorHalfConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-    void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-    void* var_28 = tensorHalfRelu(var_27); 
-    void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-    void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-    void* var_31 = tensorHalfRelu(var_30); 
-    void* var_33 = tensorHalfConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-    void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-    void* var_35 = tensorHalfRelu(var_34); 
-    void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-    void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-    void* var_38 = tensorHalfRelu(var_37); 
-    void* var_41 = tensorHalfConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-    void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-    void* var_43 = tensorHalfRelu(var_42); 
-    void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-    void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-    void* var_46 = tensorHalfRelu(var_45); 
-    void* var_48 = tensorHalfConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-    void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-    void* var_50 = tensorHalfRelu(var_49); 
-    void* var_51 = tensorHalfConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1); 
-    void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-    void* var_53 = tensorHalfRelu(var_52); 
-    void* var_55 = tensorHalfConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-    void* var_56 = tensorHalfBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-    void* var_57 = tensorHalfRelu(var_56); 
-    void* var_58 = tensorHalfConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1); 
-    void* var_59 = tensorHalfBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-    void* var_60 = tensorHalfRelu(var_59); 
-    void* var_63 = tensorHalfConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-    void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-    void* var_65 = tensorHalfRelu(var_64); 
-    void* var_66 = tensorHalfConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1); 
-    void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-    void* var_68 = tensorHalfRelu(var_67); 
-    void* var_70 = tensorHalfConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-    void* var_71 = tensorHalfBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-    void* var_72 = tensorHalfRelu(var_71); 
-    void* var_73 = tensorHalfConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1); 
-    void* var_74 = tensorHalfBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-    void* var_75 = tensorHalfRelu(var_74); 
-    void* var_77 = tensorHalfConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-    void* var_78 = tensorHalfBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-    void* var_79 = tensorHalfRelu(var_78); 
-    void* var_80 = tensorHalfConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1); 
-    void* var_81 = tensorHalfBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-    void* var_82 = tensorHalfRelu(var_81); 
-    void* var_85 = tensorHalfConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-    void* var_86 = tensorHalfBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-    void* var_87 = tensorHalfRelu(var_86); 
-    void* var_88 = tensorHalfConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1); 
-    void* var_89 = tensorHalfBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-    void* var_90 = tensorHalfRelu(var_89); 
-    void* var_92 = tensorHalfConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-    void* var_93 = tensorHalfBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-    void* var_94 = tensorHalfRelu(var_93); 
-    void* var_95 = tensorHalfConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1); 
-    void* var_96 = tensorHalfBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-    void* var_97 = tensorHalfRelu(var_96); 
-    void* var_99 = tensorHalfPooling(var_97,1,2,2,0,0,2,2); 
-    void* var_101 = tensorHalfGemmGPU(var_99, dense_1_w); 
-    void* var_102 = tensorHalfAdd(var_101, dense_1_b); 
-    void* var_103 = tensorSoftmax(var_102); 
-
-      profiler.pause_profiler();
-      auto time_energy = profiler.get_time_energy();
-      total_time += time_energy.first;
-      profiler.reset();
-
-    uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-    float accuracy = computeAccuracy2(labels, batch_size, var_103); 
-    final_accuracy += accuracy; 
-    freeBatchMemory(); 
- 
-  }
-  }
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  stopProfiling();
-  
-  final_accuracy = final_accuracy / batch_count / total_runs; 
-  dumpFinalAccuracy(final_accuracy); 
-
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
deleted file mode 100644
index f68eb1793b66b0579f2ed6dbff26a56677f2aa95..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
+++ /dev/null
@@ -1,249 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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(int argc, char* argv[]){ 
-
-  int total_runs = 10;
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
-  std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  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); 
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000; 
-  int batch_count = test_input_size / batch_size; 
-
-
-  float final_accuracy = 0.0;
-
-  for(int j = 0; j < total_runs; j++){    
-    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 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1); 
-      void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorHalfRelu(var_1); 
-      void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_6 = tensorHalfRelu(var_5); 
-      void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-      void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_9 = tensorHalfRelu(var_8); 
-      void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_13 = tensorHalfRelu(var_12); 
-      void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-      void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_16 = tensorHalfRelu(var_15); 
-      void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_20 = tensorHalfRelu(var_19); 
-      void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-      void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_23 = tensorHalfRelu(var_22); 
-      void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_28 = tensorHalfRelu(var_27); 
-      void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-      void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_31 = tensorHalfRelu(var_30); 
-      void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_35 = tensorHalfRelu(var_34); 
-      void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-      void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_38 = tensorHalfRelu(var_37); 
-      void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_43 = tensorHalfRelu(var_42); 
-      void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-      void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_46 = tensorHalfRelu(var_45); 
-      void* var_47 = tensorHalfPooling(var_46,1,2,2,0,0,2,2); 
-      void* var_49 = tensorHalfGemmGPU(var_47, dense_1_w); 
-      void* var_50 = tensorHalfAdd(var_49, dense_1_b); 
-      void* var_51 = tensorSoftmax(var_50); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_51); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
- 
-    }
-
-    //final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy / batch_count); 
-  }
-
-  final_accuracy = final_accuracy / batch_count / total_runs; 
-  dumpFinalAccuracy(final_accuracy);
-
-  dumpExecutionAccuracies();
-    
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc
deleted file mode 100644
index c641db1a05efe44d4801da1ebdcaf2ae8945e7f2..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc
+++ /dev/null
@@ -1,225 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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("../model_params/mobilenet_shallow/"); 
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,64,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,1,1,1024,10); 
-  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); 
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  int total_runs = 10;
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-
-  for(int i = 0; i < total_runs; i++){
-	  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); 
-
-        profiler.resume_profiler();
-
-		void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1); 
-		void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-		void* var_2 = tensorHalfRelu(var_1); 
-		void* var_4 = tensorHalfConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-		void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-		void* var_6 = tensorHalfRelu(var_5); 
-		void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-		void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-		void* var_9 = tensorHalfRelu(var_8); 
-		void* var_11 = tensorHalfConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-		void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-		void* var_13 = tensorHalfRelu(var_12); 
-		void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-		void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-		void* var_16 = tensorHalfRelu(var_15); 
-		void* var_18 = tensorHalfConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 2, 2, 1, 64); 
-		void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-		void* var_20 = tensorHalfRelu(var_19); 
-		void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-		void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-		void* var_23 = tensorHalfRelu(var_22); 
-		void* var_26 = tensorHalfConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-		void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-		void* var_28 = tensorHalfRelu(var_27); 
-		void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-		void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-		void* var_31 = tensorHalfRelu(var_30); 
-		void* var_33 = tensorHalfConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-		void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-		void* var_35 = tensorHalfRelu(var_34); 
-		void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-		void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-		void* var_38 = tensorHalfRelu(var_37); 
-		void* var_40 = tensorHalfPooling(var_38,1,2,2,0,0,2,2); 
-		void* var_42 = tensorHalfGemmGPU(var_40, dense_1_w); 
-		void* var_43 = tensorHalfAdd(var_42, dense_1_b); 
-		void* var_44 = tensorSoftmax(var_43); 
-
-        profiler.pause_profiler();
-        auto time_energy = profiler.get_time_energy();
-        total_time += time_energy.first;
-        profiler.reset();
-
-		uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-		float accuracy = computeAccuracy2(labels, batch_size, var_44); 
-		final_accuracy += accuracy; 
-		freeBatchMemory(); 
-	  } 
-  }
-
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  final_accuracy = final_accuracy / batch_count / total_runs; 
-  dumpFinalAccuracy(final_accuracy); 
-
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
deleted file mode 100644
index f91814e8390a400159467298a3702147cbf2f4b3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,242 +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"
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(0); 
-  
-  std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/"); 
-  std::string input_path =  dir_prefix + std::string("input.bin"); 
-  //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32); 
-  std::string labels_path =  dir_prefix + std::string("labels.bin"); 
-  //uint8_t* labels = readLabels(labels_path.c_str(), batch_size); 
-  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(), 0,16,3,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-  void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-  void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3); 
-  std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-  void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-  void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1); 
-  std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-  void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-  void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-  void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-  void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-  void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-  void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-  void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-  void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-  void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-  void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-  void* conv2d_21_b =  readTrainedWeights(conv2d_21_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(), 0,1,1,64,10); 
-  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); 
-
-
-  startMemTracking();
-
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  int total_runs = 10; //100;
-
-  // NOTE: Starting time profiling
-  startProfiling();
-
-  Profiler profiler;
-  profiler.start_profiler();
-  double total_time = 0.0;
-
-  for (int itrs = 0; itrs < total_runs; itrs++){ 
-      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);
-
-        profiler.resume_profiler();
-        
-        void* var_2 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0); 
-        void* var_3 = tensorHalfAdd(var_2, conv2d_1_b); 
-        void* var_4 = tensorHalfRelu(var_3); 
-        void* var_6 = tensorHalfConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-        void* var_7 = tensorHalfAdd(var_6, conv2d_2_b); 
-        void* var_8 = tensorHalfRelu(var_7); 
-        void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-        void* var_11 = tensorHalfAdd(var_10, conv2d_3_b); 
-        void* var_12 = tensorHalfAdd(var_4, var_11); 
-        void* var_13 = tensorHalfRelu(var_12); 
-        void* var_15 = tensorHalfConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-        void* var_16 = tensorHalfAdd(var_15, conv2d_4_b); 
-        void* var_17 = tensorHalfRelu(var_16); 
-        void* var_19 = tensorHalfConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-        void* var_20 = tensorHalfAdd(var_19, conv2d_5_b); 
-        void* var_21 = tensorHalfAdd(var_13, var_20); 
-        void* var_22 = tensorHalfRelu(var_21); 
-        void* var_24 = tensorHalfConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-        void* var_25 = tensorHalfAdd(var_24, conv2d_6_b); 
-        void* var_26 = tensorHalfRelu(var_25); 
-        void* var_28 = tensorHalfConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-        void* var_29 = tensorHalfAdd(var_28, conv2d_7_b); 
-        void* var_30 = tensorHalfAdd(var_22, var_29); 
-        void* var_31 = tensorHalfRelu(var_30); 
-        void* var_33 = tensorHalfConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0); 
-        void* var_34 = tensorHalfAdd(var_33, conv2d_8_b); 
-        void* var_35 = tensorHalfRelu(var_34); 
-        void* var_37 = tensorHalfConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-        void* var_38 = tensorHalfAdd(var_37, conv2d_9_b); 
-        void* var_40 = tensorHalfConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0); 
-        void* var_41 = tensorHalfAdd(var_40, conv2d_10_b); 
-        void* var_42 = tensorHalfAdd(var_41, var_38); 
-        void* var_43 = tensorHalfRelu(var_42); 
-        void* var_45 = tensorHalfConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-        void* var_46 = tensorHalfAdd(var_45, conv2d_11_b); 
-        void* var_47 = tensorHalfRelu(var_46); 
-        void* var_49 = tensorHalfConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-        void* var_50 = tensorHalfAdd(var_49, conv2d_12_b); 
-        void* var_51 = tensorHalfAdd(var_43, var_50); 
-        void* var_52 = tensorHalfRelu(var_51); 
-        void* var_54 = tensorHalfConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-        void* var_55 = tensorHalfAdd(var_54, conv2d_13_b); 
-        void* var_56 = tensorHalfRelu(var_55); 
-        void* var_58 = tensorHalfConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0); 
-        void* var_59 = tensorHalfAdd(var_58, conv2d_14_b); 
-        void* var_60 = tensorHalfAdd(var_52, var_59); 
-        void* var_61 = tensorHalfRelu(var_60); 
-        void* var_63 = tensorHalfConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0); 
-        void* var_64 = tensorHalfAdd(var_63, conv2d_15_b); 
-        void* var_65 = tensorHalfRelu(var_64); 
-        void* var_67 = tensorHalfConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0); 
-        void* var_68 = tensorHalfAdd(var_67, conv2d_16_b); 
-        void* var_70 = tensorHalfConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0); 
-        void* var_71 = tensorHalfAdd(var_70, conv2d_17_b); 
-        void* var_72 = tensorHalfAdd(var_71, var_68); 
-        void* var_73 = tensorHalfRelu(var_72); 
-        void* var_75 = tensorHalfConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0); 
-        void* var_76 = tensorHalfAdd(var_75, conv2d_18_b); 
-        void* var_77 = tensorHalfRelu(var_76); 
-        void* var_79 = tensorHalfConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0); 
-        void* var_80 = tensorHalfAdd(var_79, conv2d_19_b); 
-        void* var_81 = tensorHalfAdd(var_73, var_80); 
-        void* var_82 = tensorHalfRelu(var_81); 
-        void* var_84 = tensorHalfConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0); 
-        void* var_85 = tensorHalfAdd(var_84, conv2d_20_b); 
-        void* var_86 = tensorHalfRelu(var_85); 
-        void* var_88 = tensorHalfConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0); 
-        void* var_89 = tensorHalfAdd(var_88, conv2d_21_b); 
-        void* var_90 = tensorHalfAdd(var_82, var_89); 
-        void* var_91 = tensorHalfRelu(var_90); 
-        void* var_92 = tensorHalfPooling(var_91,1,8,8,0,0,8,8); 
-        void* var_94 = tensorHalfGemmGPU(var_92, dense_1_w); 
-        void* var_95 = tensorHalfAdd(var_94, dense_1_b); 
-        void* var_96 = tensorSoftmax(var_95); 
-
-        profiler.pause_profiler();
-        auto time_energy = profiler.get_time_energy();
-        total_time += time_energy.first;
-        profiler.reset();
-
-        uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end); 
-
-        float accuracy = computeAccuracy2(labels,batch_size,var_96); 
-        final_accuracy += accuracy;
-        
-        freeBatchMemory();
-      }
-  }
-  stopProfiling();
-
-  profiler.stop_profiler();
-
-  final_accuracy = final_accuracy / batch_count / total_runs;
-  dumpFinalAccuracy(final_accuracy);
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-  
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
deleted file mode 100644
index b778b1720c8a2db2f90230c3e57d0e0928f8665b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
+++ /dev/null
@@ -1,182 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#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("../model_params/vgg16_cifar100_front/"); 
-  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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,512,512); 
-  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(), 0,1,512,1,1); 
-  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(), 0,1,1,512,100); 
-  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(), 0,1,100,1,1); 
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000; 
-  int batch_count = test_input_size / batch_size; 
-  float final_accuracy = 0.0; 
-
-  int total_runs = 10;
-  Profiler profiler;
-  profiler.start_profiler();
-  double total_time = 0.0;
-
-  for (int i = 0; i < total_runs; i++){
-	  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); 
-
-        profiler.resume_profiler();
-
-		void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0); 
-		void* var_1 = tensorHalfAdd(var_0, conv2d_1_b); 
-		void* var_2 = tensorHalfRelu(var_1); 
-		void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-		void* var_5 = tensorHalfAdd(var_4, conv2d_2_b); 
-		void* var_6 = tensorHalfRelu(var_5); 
-		void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2); 
-		void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-		void* var_9 = tensorHalfAdd(var_8, conv2d_3_b); 
-		void* var_10 = tensorHalfRelu(var_9); 
-		void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-		void* var_13 = tensorHalfAdd(var_12, conv2d_4_b); 
-		void* var_14 = tensorHalfRelu(var_13); 
-		void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2); 
-		void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-		void* var_17 = tensorHalfAdd(var_16, conv2d_5_b); 
-		void* var_18 = tensorHalfRelu(var_17); 
-		void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-		void* var_21 = tensorHalfAdd(var_20, conv2d_6_b); 
-		void* var_22 = tensorHalfRelu(var_21); 
-		void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-		void* var_25 = tensorHalfAdd(var_24, conv2d_7_b); 
-		void* var_26 = tensorHalfRelu(var_25); 
-		void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2); 
-		void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0); 
-		void* var_29 = tensorHalfAdd(var_28, conv2d_8_b); 
-		void* var_30 = tensorHalfRelu(var_29); 
-		void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-		void* var_33 = tensorHalfAdd(var_32, conv2d_9_b); 
-		void* var_34 = tensorHalfRelu(var_33); 
-		void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0); 
-		void* var_37 = tensorHalfAdd(var_36, conv2d_10_b); 
-		void* var_38 = tensorHalfRelu(var_37); 
-		void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2); 
-		void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-		void* var_41 = tensorHalfAdd(var_40, conv2d_11_b); 
-		void* var_42 = tensorHalfRelu(var_41); 
-		void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-		void* var_45 = tensorHalfAdd(var_44, conv2d_12_b); 
-		void* var_46 = tensorHalfRelu(var_45); 
-		void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-		void* var_49 = tensorHalfAdd(var_48, conv2d_13_b); 
-		void* var_50 = tensorHalfRelu(var_49); 
-		void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2); 
-		void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w); 
-		void* var_55 = tensorHalfAdd(var_54, dense_1_b); 
-		void* var_56 = tensorHalfRelu(var_55); 
-		void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w); 
-		void* var_59 = tensorHalfAdd(var_58, dense_2_b); 
-		void* var_60 = tensorSoftmax(var_59); 
-
-        profiler.pause_profiler();
-        auto time_energy = profiler.get_time_energy();
-        total_time += time_energy.first;
-        profiler.reset();
-
-		uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-		float accuracy = computeAccuracy2(labels, batch_size, var_60, 100); 
-		final_accuracy += accuracy; 
-		freeBatchMemory(); 
-	 
-	  }
-  }
-
-  profiler.stop_profiler();
-
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  final_accuracy = final_accuracy / batch_count / total_runs; 
-  dumpFinalAccuracy(final_accuracy); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
deleted file mode 100644
index 3f97e5dbde3b6d124888a8c74d435880097a394c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,189 +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"
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(0); 
-
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/"); 
-  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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,512,512); 
-  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(), 0,1,512,1,1); 
-  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(), 0,1,1,512,10); 
-  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); 
-
-
-  startMemTracking();
-
-  int test_input_size = 5000;
-  int batch_size = 1000;
-  int batch_count = test_input_size / batch_size;
-  float final_accuracy = 0.0;
-
-  int total_runs = 10;
-
-  // NOTE: Starting time profiling
-  startProfiling();
-
-  Profiler profiler;
-  profiler.start_profiler();
-
-  double total_time = 0.0;
-  for (int itrs = 0; itrs < total_runs; itrs++){
-      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); 
-    
-        profiler.resume_profiler();
- 
-        void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0); 
-        void* var_1 = tensorHalfAdd(var_0, conv2d_1_b); 
-        void* var_2 = tensorHalfRelu(var_1); 
-        void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-        void* var_5 = tensorHalfAdd(var_4, conv2d_2_b); 
-        void* var_6 = tensorHalfRelu(var_5); 
-        void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2); 
-        void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-        void* var_9 = tensorHalfAdd(var_8, conv2d_3_b); 
-        void* var_10 = tensorHalfRelu(var_9); 
-        void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-        void* var_13 = tensorHalfAdd(var_12, conv2d_4_b); 
-        void* var_14 = tensorHalfRelu(var_13); 
-        void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2); 
-        void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-        void* var_17 = tensorHalfAdd(var_16, conv2d_5_b); 
-        void* var_18 = tensorHalfRelu(var_17); 
-        void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-        void* var_21 = tensorHalfAdd(var_20, conv2d_6_b); 
-        void* var_22 = tensorHalfRelu(var_21); 
-        void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-        void* var_25 = tensorHalfAdd(var_24, conv2d_7_b); 
-        void* var_26 = tensorHalfRelu(var_25); 
-        void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2); 
-        void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0); 
-        void* var_29 = tensorHalfAdd(var_28, conv2d_8_b); 
-        void* var_30 = tensorHalfRelu(var_29); 
-        void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-        void* var_33 = tensorHalfAdd(var_32, conv2d_9_b); 
-        void* var_34 = tensorHalfRelu(var_33); 
-        void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0); 
-        void* var_37 = tensorHalfAdd(var_36, conv2d_10_b); 
-        void* var_38 = tensorHalfRelu(var_37); 
-        void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2); 
-        void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-        void* var_41 = tensorHalfAdd(var_40, conv2d_11_b); 
-        void* var_42 = tensorHalfRelu(var_41); 
-        void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-        void* var_45 = tensorHalfAdd(var_44, conv2d_12_b); 
-        void* var_46 = tensorHalfRelu(var_45); 
-        void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-        void* var_49 = tensorHalfAdd(var_48, conv2d_13_b); 
-        void* var_50 = tensorHalfRelu(var_49); 
-        void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2); 
-        void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w); 
-        void* var_55 = tensorHalfAdd(var_54, dense_1_b); 
-        void* var_56 = tensorHalfRelu(var_55); 
-        void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w); 
-        void* var_59 = tensorHalfAdd(var_58, dense_2_b); 
-        void* var_60 = tensorSoftmax(var_59); 
-
-        profiler.pause_profiler();
-        auto time_energy = profiler.get_time_energy();
-        total_time += time_energy.first;
-        profiler.reset();
-
-        uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end); 
-
-        float accuracy = computeAccuracy2(labels,batch_size,var_60); 
-        final_accuracy += accuracy;
-
-        freeBatchMemory();
-      }
-  }
-  std::cout<<"---------------------------------------\n";
-  std::cout<<"Average time: " << total_time / total_runs << '\n';
-  std::cout<<"---------------------------------------\n";
-
-  profiler.stop_profiler();
-  // Start power and performance profiling 
-  stopProfiling();
-
-  final_accuracy = final_accuracy / batch_count / total_runs;
-  dumpFinalAccuracy(final_accuracy);
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc
deleted file mode 100644
index d30518216f76160e183a915a6e6da2018239ab60..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc
+++ /dev/null
@@ -1,240 +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(int argc, char* argv[]){ 
-
-  int total_runs = 1;
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  std::string dir_prefix = std::string("../model_params/mobilenet_shallow/"); 
-
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  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); 
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000; 
-  int batch_size = 2500; 
-  int batch_count = test_input_size / batch_size; 
-
-
-  for(int j = 0; j < total_runs; j++){    
-    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, 1, 1, 1, 1, 1, 1); 
-      void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorRelu(var_1); 
-      void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_6 = tensorRelu(var_5); 
-      void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-      void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_9 = tensorRelu(var_8); 
-      void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_13 = tensorRelu(var_12); 
-      void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-      void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_16 = tensorRelu(var_15); 
-      void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_20 = tensorRelu(var_19); 
-      void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-      void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_28 = tensorRelu(var_27); 
-      void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-      void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_31 = tensorRelu(var_30); 
-      void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_35 = tensorRelu(var_34); 
-      void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-      void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_43 = tensorRelu(var_42); 
-      void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-      void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_46 = tensorRelu(var_45); 
-      void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2); 
-      void* var_49 = tensorGemmGPU(var_47, dense_1_w); 
-      void* var_50 = tensorAdd(var_49, dense_1_b); 
-      void* var_51 = tensorSoftmax(var_50); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_51); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-  }
-
-  dumpExecutionAccuracies();
-    
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_piped.cc
deleted file mode 100644
index 653d2e7bdf7f7d006dc89fb99027ac58bd336c45..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_piped.cc
+++ /dev/null
@@ -1,172 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-  
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/"); 
-  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 missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    
-    if (missed >= to_skip){
-      break;           
-    }
-
-    startMemTracking(); 
-
-    
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.9998477, 0.99987465, 9); 
-      //      void* var_1 = ConvLayer_PROMISE(var_0, -0.9998477, 0.99987465, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-      void* var_1 = ConvLayer_PROMISE(var_0, -0.8, 0.8, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-      
-      void* var_2 = ConvLayer_PROMISE(var_1, -0.99997115, 1.0, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9999999, 1.0, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, -0.9999999, 1.0, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9999999, 0.99999934, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, -0.9999999, 0.99999934, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9); 
-      void* var_5 = ConvLayer_PROMISE(var_4, -1.0, 1.0, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.9999941, 0.9999964, 9); 
-      void* var_6 = FCLayer_PROMISE(var_5, -0.9999941, 0.9999964, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -140.6419, 16.402884, 9); 
-      void* var_7 = tensorSoftmax(var_6); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_7); 
-      final_accuracy += accuracy;
-
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_7, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc
deleted file mode 100644
index ab3a20dfafbd636b03e2f3496eb6d016cd57a394..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc
+++ /dev/null
@@ -1,167 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-  
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  int missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    if (missed >= to_skip){
-      break;           
-    }
-
-    startMemTracking(); 
-
-    
-    int batch_count = test_input_size / batch_size; 
-    float final_accuracy = 0.0; 
-
-
-    std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/"); 
-    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");
-
-
-    for(int i = 0; i < batch_count; i++){ 
-
-      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 start = i * batch_size + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.9998477, 0.99987465, 9); 
-      //      void* var_1 = ConvLayer_PROMISE(var_0, -0.9998477, 0.99987465, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-      void* var_1 = ConvLayer_PROMISE(var_0, -0.8, 0.8, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-      
-      void* var_2 = ConvLayer_PROMISE(var_1, -0.99997115, 1.0, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9999999, 1.0, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, -0.9999999, 1.0, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9999999, 0.99999934, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, -0.9999999, 0.99999934, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9); 
-      void* var_5 = ConvLayer_PROMISE(var_4, -1.0, 1.0, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.9999941, 0.9999964, 9); 
-      void* var_6 = FCLayer_PROMISE(var_5, -0.9999941, 0.9999964, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -140.6419, 16.402884, 9); 
-      void* var_7 = tensorSoftmax(var_6); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_7); 
-      final_accuracy += accuracy;
-
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_7, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-  }
-
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_piped.cc
deleted file mode 100644
index da2331c9654cedc49241d1cf573fdb4886469180..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_piped.cc
+++ /dev/null
@@ -1,106 +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.h" 
-#include "utils.h" 
-
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(1); 
-
-  int total_runs = 1; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    startMemTracking(); 
-
-    int test_input_size = 5000; 
-    int batch_size = 200; 
-    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("/shared/hsharif3/alexnet_imagenet_tune/"); 
-      std::string input_path =  dir_prefix + std::string("tune_input.bin"); 
-      std::string labels_path =  dir_prefix + std::string("tune_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(), 0,64,3,11,11); 
-      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(), 0,1,64,1,1); 
-      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(), 0,192,64,5,5); 
-      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(), 0,1,192,1,1); 
-      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(), 0,384,192,3,3); 
-      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(), 0,1,384,1,1); 
-      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(), 0,256,384,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,256,256,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,1,1,9216,4096); 
-      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(), 0,1,4096,1,1); 
-      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(), 0,1,1,4096,4096); 
-      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(), 0,1,4096,1,1); 
-      std::string dense_3_w_path =  dir_prefix + std::string("dense_3_w.bin"); 
-      void* dense_3_w =  readTrainedWeights(dense_3_w_path.c_str(), 0,1,1,4096,1000); 
-      std::string dense_3_b_path =  dir_prefix + std::string("dense_3_b.bin"); 
-      void* dense_3_b =  readTrainedWeights(dense_3_b_path.c_str(), 0,1,1000,1,1); 
-
-
-      int start = i * batch_size; 
-      int end = (i + 1) * batch_size; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,224,224); 
-
-      void* var_0 = ConvLayer_PROMISE2(input, 0.0, 255.0, conv2d_1_w, -0.5503702693581581, 0.5811487324237921, conv2d_1_b, -2.802485, 1.648145, 2, 2, 4, 4, 0, 3, 2, 1, 0.0, 1572.3096923828125, 9); 
-      void* var_1 = ConvLayer_PROMISE2(var_0, 0.0, 1572.3096923828125, conv2d_2_w, -0.2867645202279091, 0.26272463005783797, conv2d_2_b, -0.47985682, 0.501206, 2, 2, 1, 1, 0, 3, 2, 1, 0.0, 3183.7813264160477, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 3183.7813264160477, conv2d_3_w, -0.16606662392616273, 0.15785247704386754, conv2d_3_b, -0.42038992, 0.5545839, 1, 1, 1, 1, -1, 0, 1, 0.0, 1765.4451872558668, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1765.4451872558668, conv2d_4_w, -0.10464580833911895, 0.11035470351576919, conv2d_4_b, -1.4275751, 0.9042998, 1, 1, 1, 1, -1, 0, 1, 0.0, 1345.5418548586083, 9); 
-      void* var_4 = ConvLayer_PROMISE2(var_3, 0.0, 1345.5418548586083, conv2d_5_w, -0.09240880391001702, 0.10250756608694818, conv2d_5_b, -0.45662758, 2.4040315, 1, 1, 1, 1, 0, 3, 2, 1, 0.0, 1227.3563232421875, 9); 
-      void* var_5 = FCLayer_PROMISE(var_4, 0.0, 1227.3563232421875, dense_1_w, -0.030517672039568428, 0.02963459612801672, dense_1_b, -0.07124679, 0.09377053, 1, 0.0, 1034.5966391601676, 9); 
-      void* var_6 = FCLayer_PROMISE(var_5, 0.0, 1034.5966391601676, dense_2_w, -0.038392101023346184, 0.039147199764847845, dense_2_b, -0.050027702, 0.1841282, 1, 0.0, 839.0697069702154, 9); 
-      void* var_7 = FCLayer_PROMISE(var_6, 0.0, 839.0697069702154, dense_3_w, -0.05494491942599416, 0.08549865524470925, dense_3_b, -0.16314922, 0.15416704, -1, -608.3993963623047, 1082.8444653320819, 9); 
-      void* var_8 = tensorSoftmax(var_7); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_8); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();  
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_promise.cc
deleted file mode 100644
index c848c8614d4ea68fd9fbeb5ab8fd072f4aa15b19..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_imagenet_promise.cc
+++ /dev/null
@@ -1,102 +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.h" 
-#include "utils.h" 
-
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(1); 
-
-  int total_runs = 1; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-
-    startMemTracking(); 
-
-    int test_input_size = 5000; 
-    int batch_size = 200; 
-    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("/shared/hsharif3/alexnet_imagenet_tune/"); 
-      std::string input_path =  dir_prefix + std::string("tune_input.bin"); 
-      std::string labels_path =  dir_prefix + std::string("tune_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(), 0,64,3,11,11); 
-      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(), 0,1,64,1,1); 
-      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(), 0,192,64,5,5); 
-      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(), 0,1,192,1,1); 
-      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(), 0,384,192,3,3); 
-      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(), 0,1,384,1,1); 
-      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(), 0,256,384,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,256,256,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,1,1,9216,4096); 
-      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(), 0,1,4096,1,1); 
-      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(), 0,1,1,4096,4096); 
-      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(), 0,1,4096,1,1); 
-      std::string dense_3_w_path =  dir_prefix + std::string("dense_3_w.bin"); 
-      void* dense_3_w =  readTrainedWeights(dense_3_w_path.c_str(), 0,1,1,4096,1000); 
-      std::string dense_3_b_path =  dir_prefix + std::string("dense_3_b.bin"); 
-      void* dense_3_b =  readTrainedWeights(dense_3_b_path.c_str(), 0,1,1000,1,1); 
-
-
-      int start = i * batch_size; 
-      int end = (i + 1) * batch_size; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,224,224); 
-
-      void* var_0 = ConvLayer_PROMISE2(input, 0.0, 255.0, conv2d_1_w, -0.5503702693581581, 0.5811487324237921, conv2d_1_b, -2.802485, 1.648145, 2, 2, 4, 4, 0, 3, 2, 1, 0.0, 1572.3096923828125, 9); 
-      void* var_1 = ConvLayer_PROMISE2(var_0, 0.0, 1572.3096923828125, conv2d_2_w, -0.2867645202279091, 0.26272463005783797, conv2d_2_b, -0.47985682, 0.501206, 2, 2, 1, 1, 0, 3, 2, 1, 0.0, 3183.7813264160477, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 3183.7813264160477, conv2d_3_w, -0.16606662392616273, 0.15785247704386754, conv2d_3_b, -0.42038992, 0.5545839, 1, 1, 1, 1, -1, 0, 1, 0.0, 1765.4451872558668, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1765.4451872558668, conv2d_4_w, -0.10464580833911895, 0.11035470351576919, conv2d_4_b, -1.4275751, 0.9042998, 1, 1, 1, 1, -1, 0, 1, 0.0, 1345.5418548586083, 9); 
-      void* var_4 = ConvLayer_PROMISE2(var_3, 0.0, 1345.5418548586083, conv2d_5_w, -0.09240880391001702, 0.10250756608694818, conv2d_5_b, -0.45662758, 2.4040315, 1, 1, 1, 1, 0, 3, 2, 1, 0.0, 1227.3563232421875, 9); 
-      void* var_5 = FCLayer_PROMISE(var_4, 0.0, 1227.3563232421875, dense_1_w, -0.030517672039568428, 0.02963459612801672, dense_1_b, -0.07124679, 0.09377053, 1, 0.0, 1034.5966391601676, 9); 
-      void* var_6 = FCLayer_PROMISE(var_5, 0.0, 1034.5966391601676, dense_2_w, -0.038392101023346184, 0.039147199764847845, dense_2_b, -0.050027702, 0.1841282, 1, 0.0, 839.0697069702154, 9); 
-      void* var_7 = FCLayer_PROMISE(var_6, 0.0, 839.0697069702154, dense_3_w, -0.05494491942599416, 0.08549865524470925, dense_3_b, -0.16314922, 0.15416704, -1, -608.3993963623047, 1082.8444653320819, 9); 
-      void* var_8 = tensorSoftmax(var_7); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_8); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_piped.cc
deleted file mode 100644
index 22fe979cb5bbae5964ff33444ab0fbe9dec82cf1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_piped.cc
+++ /dev/null
@@ -1,167 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-  
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-
-  std::string dir_prefix = std::string("../model_params/alexnet_cifar10/");   
-  //std::string dir_prefix = std::string("../model_params/alexnet_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(), 0,64,3,11,11); 
-  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(), 0,1,64,1,1); 
-  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(), 0,192,64,5,5); 
-  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(), 0,1,192,1,1); 
-  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(), 0,384,192,3,3); 
-  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(), 0,1,384,1,1); 
-  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(), 0,256,384,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,1,1,4096,10); 
-  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 missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-    
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9); 
-      void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9); 
-      void* var_6 = tensorSoftmax(var_5); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_6); 
-      final_accuracy += accuracy; 
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_6, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-
-      freeBatchMemory();  
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-    
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-  dumpExecutionAccuracies();
-
-
-  
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc
deleted file mode 100644
index c67eb1153e6c29a0f478e495be2d36dbdafe1d56..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc
+++ /dev/null
@@ -1,160 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-  
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(1); 
-
-  
-  int missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-    
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-
-    int batch_count = test_input_size / batch_size; 
-    float final_accuracy = 0.0; 
-
-    std::string dir_prefix = std::string("../model_params/alexnet_cifar10/");   
-    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");
-    
-    for(int i = 0; i < batch_count; i++){
-      
-      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(), 0,64,3,11,11); 
-      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(), 0,1,64,1,1); 
-      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(), 0,192,64,5,5); 
-      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(), 0,1,192,1,1); 
-      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(), 0,384,192,3,3); 
-      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(), 0,1,384,1,1); 
-      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(), 0,256,384,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,256,256,3,3); 
-      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(), 0,1,256,1,1); 
-      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(), 0,1,1,4096,10); 
-      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 start = i * batch_size + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9); 
-      void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9); 
-      void* var_6 = tensorSoftmax(var_5); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_6); 
-      final_accuracy += accuracy; 
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_6, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-
-      freeBatchMemory();  
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-    
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-    
-  }
-
-  dumpExecutionAccuracies();
-
-
-  
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_piped.cc
deleted file mode 100644
index c246822a094faffebe01f58fa8fd2c15f004cea1..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_piped.cc
+++ /dev/null
@@ -1,175 +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.h"
-#include "utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int test_input_size = 5000; 
-int batch_size = 5000;
-int offset = 5000;
-
-
-bool shouldDumpClassConf = false;
-float* classConfs;
-int* predictedLabels;
-  
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
-  printf("********* Lenet-5 Architecture ********** \n");
-
-  std::string dir_prefix = std::string("../model_params/lenet_mnist/");   
-  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");
-
-
-  // Loading Weights
-  void* conv1_filter = readTrainedWeights("../model_params/lenet_mnist/conv1.bin",
-					  float_type, 32, 1, 5, 5);    
-  void* conv1_bias = readTrainedWeights("../model_params/lenet_mnist/conv1_bias.bin",
-					float_type, 1, 32, 1, 1);  
-  void* conv2_filter = readTrainedWeights("../model_params/lenet_mnist/conv2.bin",
-					  float_type, 64, 32, 5, 5);  
-  void* conv2_bias = readTrainedWeights("../model_params/lenet_mnist/conv2_bias.bin",
-					float_type, 1, 64, 1, 1);  
-  void* fc1_weights = readTrainedWeights("../model_params/lenet_mnist/fc1.bin",
-					 float_type, 1, 1, 7*7*64, 1024);  
-  void* fc1_bias = readTrainedWeights("../model_params/lenet_mnist/fc1_bias.bin",
-				      float_type, 1, 1024, 1, 1);  
-  void* fc2_weights = readTrainedWeights("../model_params/lenet_mnist/fc2.bin",
-					 float_type, 1, 1, 1024, 10);  
-  void* fc2_bias = readTrainedWeights("../model_params/lenet_mnist/fc2_bias.bin",
-				      float_type, 1, 10, 1, 1);  
-  
-  clearTensorMap();
-
-  int missed = 0;
-  for(int i = 0; i < total_runs; i++){
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    
-    if (missed >= to_skip){
-      break;           
-    }   
-
-    
-    int start = offset; 
-    int end = batch_size + offset; 
-
-    // Loading Input Batch
-    void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28); 
-
-    // Loading Weights
-    
-    // DNN Operations
-    void* conv1_out = ConvLayer_PROMISE(input, 0,1, conv1_filter, -1,1, conv1_bias, -1,1,
-					2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-    void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -1,1,
-					conv2_bias, -1,1,
-					2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-
-    void* fc1_out = FCLayer_PROMISE(conv2_out, -1,1, fc1_weights, -1,1, fc1_bias, -1,1,
-				    0, -1,1, 9);    
-    void* fc2_out = FCLayer_PROMISE(fc1_out, -1,1, fc2_weights, -1,1, fc2_bias, -1,1,
-				    0, -1,1, 9);
-
-    void* result = tensorSoftmax(fc2_out);
-
-    
-    uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-    float accuracy = computeAccuracy2(labels, batch_size, result);
-
-
-    if(shouldDumpClassConf){
-      int relative_start = start - offset;
-      int relative_end = end - offset;
-      copyClassConfsAndLabels(result, classConfs, predictedLabels, relative_start, relative_end);
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }    
-  
-        
-    freeOutputTensors();  
-
-    dumpFinalAccuracy(accuracy); 
-
-    if (accuracy < bench_acc)
-      missed += 1;
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-
-  }
-
-  dumpExecutionAccuracies(); 
-}
-
-
-int main(int argc, char* argv[]){
-
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-    
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);
-    batch_size = atoi(argv[4]);
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-
-  
-  llvm_hpvm_initTensorRt(0);
-
-  
-  testLenetTanh();
-
-  llvm_hpvm_cleanupTensorRt();
-
-  return 0;
-}
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc
deleted file mode 100644
index e1428589c3f48a5879b9f5c9c73980e4d3ca9ff0..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc
+++ /dev/null
@@ -1,167 +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.h"
-#include "utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int test_input_size = 5000; 
-int batch_size = 5000;
-int offset = 5000;
-
-
-bool shouldDumpClassConf = false;
-float* classConfs;
-int* predictedLabels;
-  
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
-  printf("********* Lenet-5 Architecture ********** \n");
-  
-  std::string dir_prefix = std::string("../model_params/lenet_mnist/");   
-  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");
-
-  
-  int missed = 0;
-  for(int i = 0; i < total_runs; i++){
-
-    if (missed >= to_skip){
-      break;           
-    }   
-
-    int start = offset; 
-    int end = batch_size + offset; 
-
-
-    startMemTracking(); 
-
-    // Loading Input Batch
-    void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28); 
-
-    // Loading Weights
-    void* conv1_filter = readTrainedWeights("../model_params/lenet_mnist/conv1.bin",
-					    float_type, 32, 1, 5, 5);    
-    void* conv1_bias = readTrainedWeights("../model_params/lenet_mnist/conv1_bias.bin",
-					  float_type, 1, 32, 1, 1);  
-    void* conv2_filter = readTrainedWeights("../model_params/lenet_mnist/conv2.bin",
-					    float_type, 64, 32, 5, 5);  
-    void* conv2_bias = readTrainedWeights("../model_params/lenet_mnist/conv2_bias.bin",
-					  float_type, 1, 64, 1, 1);  
-    void* fc1_weights = readTrainedWeights("../model_params/lenet_mnist/fc1.bin",
-					   float_type, 1, 1, 7*7*64, 1024);  
-    void* fc1_bias = readTrainedWeights("../model_params/lenet_mnist/fc1_bias.bin",
-					float_type, 1, 1024, 1, 1);  
-    void* fc2_weights = readTrainedWeights("../model_params/lenet_mnist/fc2.bin",
-					   float_type, 1, 1, 1024, 10);  
-    void* fc2_bias = readTrainedWeights("../model_params/lenet_mnist/fc2_bias.bin",
-					float_type, 1, 10, 1, 1);  
-
-    
-    // DNN Operations
-    void* conv1_out = ConvLayer_PROMISE(input, 0,1, conv1_filter, -1,1, conv1_bias, -1,1,
-					2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-    void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -1,1,
-					conv2_bias, -1,1,
-					2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-
-    void* fc1_out = FCLayer_PROMISE(conv2_out, -1,1, fc1_weights, -1,1, fc1_bias, -1,1,
-				    0, -1,1, 9);    
-    void* fc2_out = FCLayer_PROMISE(fc1_out, -1,1, fc2_weights, -1,1, fc2_bias, -1,1,
-				    0, -1,1, 9);
-
-    void* result = tensorSoftmax(fc2_out);
-
-    
-    uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-    float accuracy = computeAccuracy2(labels, batch_size, result);
-
-
-    if(shouldDumpClassConf){
-      int relative_start = start - offset;
-      int relative_end = end - offset;
-      copyClassConfsAndLabels(result, classConfs, predictedLabels, relative_start, relative_end);
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }    
-  
-    
-    freeBatchMemory();  
-
-    dumpFinalAccuracy(accuracy); 
-
-
-    if (accuracy < bench_acc)
-      missed += 1;
-        
-  }
-
-  dumpExecutionAccuracies(); 
-}
-
-
-int main(int argc, char* argv[]){
-
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-    
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);
-    batch_size = atoi(argv[4]);
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-
-  
-  llvm_hpvm_initTensorRt(1);
-
-  testLenetTanh();
-
-  llvm_hpvm_cleanupTensorRt();
-
-  return 0;
-}
-
-
-
-
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_piped.cc
deleted file mode 100644
index 8444e512f5f36de261065653be8a2bdf44885d5d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_piped.cc
+++ /dev/null
@@ -1,492 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-  
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  
-  std::string dir_prefix = std::string("../model_params/mobilenet/"); 
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-  void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-  void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-  void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-  void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-  void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-  void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-  void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-  void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-  void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-  void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-  void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-  void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-  void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-  void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-  void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-  void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-  void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-  void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-  void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-  void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-  void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-  void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-  void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-  void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-  void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-  void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-  void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-  void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-  void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-  void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-  void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-  void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-  void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-  void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-  void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-  void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-  void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-  void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-  void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-  void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-  void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-  std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-  void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-  void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-  void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-  void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-  std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-  void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-  std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-  void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-  void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-  void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-  void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-  std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-  void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-  void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-  void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-  void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-  void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-  std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-  void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-  void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-  void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-  void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-  std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-  void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-  void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-  void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-  void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-  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(), 0,1,1,1024,10); 
-  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 missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    if (missed >= to_skip){
-      break;           
-    }
-
-    startMemTracking(); 
-
-    
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9); 
-      void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorRelu(var_1); 
-      void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_4 = tensorHalfBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_5 = tensorRelu(var_4); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9); 
-      void* var_7 = tensorHalfBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_10 = tensorHalfBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_11 = tensorRelu(var_10); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9); 
-      void* var_13 = tensorHalfBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_14 = tensorRelu(var_13); 
-      void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_16 = tensorHalfBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9); 
-      void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_20 = tensorRelu(var_19); 
-      void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9); 
-      void* var_25 = tensorHalfBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_28 = tensorHalfBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9); 
-      void* var_31 = tensorHalfBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorRelu(var_31); 
-      void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_35 = tensorRelu(var_34); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9); 
-      void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-      void* var_40 = tensorHalfBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-      void* var_41 = tensorRelu(var_40); 
-      void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9); 
-      void* var_43 = tensorHalfBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-      void* var_44 = tensorRelu(var_43); 
-      void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-      void* var_46 = tensorHalfBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-      void* var_47 = tensorRelu(var_46); 
-      void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9); 
-      void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-      void* var_50 = tensorRelu(var_49); 
-      void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-      void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-      void* var_53 = tensorRelu(var_52); 
-      void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9); 
-      void* var_55 = tensorHalfBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-      void* var_56 = tensorRelu(var_55); 
-      void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-      void* var_58 = tensorHalfBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-      void* var_59 = tensorRelu(var_58); 
-      void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9); 
-      void* var_61 = tensorHalfBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-      void* var_62 = tensorRelu(var_61); 
-      void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-      void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-      void* var_65 = tensorRelu(var_64); 
-      void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9); 
-      void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-      void* var_68 = tensorRelu(var_67); 
-      void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-      void* var_70 = tensorHalfBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-      void* var_71 = tensorHalfRelu(var_70); 
-      void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9); 
-      void* var_73 = tensorHalfBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-      void* var_74 = tensorHalfRelu(var_73); 
-      void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-      void* var_76 = tensorHalfBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-      void* var_77 = tensorRelu(var_76); 
-      void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9); 
-      void* var_79 = tensorHalfBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-      void* var_80 = tensorHalfRelu(var_79); 
-      void* var_81 = tensorHalfPooling(var_80,1,2,2,0,0,2,2); 
-      void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9); 
-      void* var_83 = tensorSoftmax(var_82); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_83); 
-      final_accuracy += accuracy;
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_83, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory();  
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-    if (final_accuracy < bench_acc)
-     missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc
deleted file mode 100644
index 697cc5f1412ac012c344abbd5a25a8a79a2f1acd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc
+++ /dev/null
@@ -1,487 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-  
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-
-  int missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    if (missed >= to_skip){
-      break;           
-    }
-
-    startMemTracking(); 
-
-    
-    int batch_count = test_input_size / batch_size; 
-    float final_accuracy = 0.0; 
-
-
-    std::string dir_prefix = std::string("../model_params/mobilenet/"); 
-    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"); 
-
-    for(int i = 0; i < batch_count; i++){ 
-
-      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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-      void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-      void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-      void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-      void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-      std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-      void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-      std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-      void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-      void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-      void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-      void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-      std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-      void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-      void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-      void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-      void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-      std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-      void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-      std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-      void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-      void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-      void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-      void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-      std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-      void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-      void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-      void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-      void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-      std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-      void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-      std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-      void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-      void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-      void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-      void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-      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(), 0,128,128,1,1); 
-      std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-      void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-      void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-      void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-      void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-      std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-      void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-      std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-      void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-      void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-      void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-      void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-      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(), 0,256,128,1,1); 
-      std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-      void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-      void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-      void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-      void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-      std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-      void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-      std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-      void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-      void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-      void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-      void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-      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(), 0,256,256,1,1); 
-      std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-      void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-      void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-      void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-      void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-      std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-      void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-      std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-      void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-      void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-      void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-      void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-      void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-      std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-      void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-      void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-      void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-      void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-      void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-      void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-      void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-      void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-      void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-      void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-      std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-      void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-      void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-      void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-      void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-      void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-      void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-      void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-      void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-      void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-      void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-      std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-      void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-      void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-      void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-      void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-      void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-      void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-      void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-      void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-      void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-      void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-      std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-      void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-      void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-      void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-      void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-      void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-      void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-      void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-      void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-      void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-      void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-      std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-      void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-      void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-      void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-      void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-      void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-      void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-      void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-      void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-      void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-      void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-      std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-      void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-      void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-      void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-      void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-      std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-      void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-      std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-      void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-      void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-      void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-      void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-      void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-      std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-      void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-      void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-      void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-      void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-      void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-      std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-      void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-      void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-      void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-      void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-      void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-      std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-      void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-      void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-      void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-      void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-      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(), 0,1,1,1024,10); 
-      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 start = i * batch_size + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9); 
-      void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorRelu(var_1); 
-      void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_4 = tensorHalfBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_5 = tensorRelu(var_4); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9); 
-      void* var_7 = tensorHalfBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_10 = tensorHalfBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_11 = tensorRelu(var_10); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9); 
-      void* var_13 = tensorHalfBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_14 = tensorRelu(var_13); 
-      void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_16 = tensorHalfBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9); 
-      void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_20 = tensorRelu(var_19); 
-      void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9); 
-      void* var_25 = tensorHalfBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_28 = tensorHalfBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9); 
-      void* var_31 = tensorHalfBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorRelu(var_31); 
-      void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_35 = tensorRelu(var_34); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9); 
-      void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-      void* var_40 = tensorHalfBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-      void* var_41 = tensorRelu(var_40); 
-      void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9); 
-      void* var_43 = tensorHalfBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-      void* var_44 = tensorRelu(var_43); 
-      void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-      void* var_46 = tensorHalfBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-      void* var_47 = tensorRelu(var_46); 
-      void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9); 
-      void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-      void* var_50 = tensorRelu(var_49); 
-      void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-      void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-      void* var_53 = tensorRelu(var_52); 
-      void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9); 
-      void* var_55 = tensorHalfBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-      void* var_56 = tensorRelu(var_55); 
-      void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-      void* var_58 = tensorHalfBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-      void* var_59 = tensorRelu(var_58); 
-      void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9); 
-      void* var_61 = tensorHalfBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-      void* var_62 = tensorRelu(var_61); 
-      void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-      void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-      void* var_65 = tensorRelu(var_64); 
-      void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9); 
-      void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-      void* var_68 = tensorRelu(var_67); 
-      void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-      void* var_70 = tensorHalfBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-      void* var_71 = tensorHalfRelu(var_70); 
-      void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9); 
-      void* var_73 = tensorHalfBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-      void* var_74 = tensorHalfRelu(var_73); 
-      void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-      void* var_76 = tensorHalfBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-      void* var_77 = tensorRelu(var_76); 
-      void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9); 
-      void* var_79 = tensorHalfBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-      void* var_80 = tensorHalfRelu(var_79); 
-      void* var_81 = tensorHalfPooling(var_80,1,2,2,0,0,2,2); 
-      void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9); 
-      void* var_83 = tensorSoftmax(var_82); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_83); 
-      final_accuracy += accuracy;
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_83, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory();  
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-    if (final_accuracy < bench_acc)
-     missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_piped.cc
deleted file mode 100644
index 3dffdffcf16fdcf7071e3957cf7dc496fa0c3c50..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_piped.cc
+++ /dev/null
@@ -1,313 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-    
-  llvm_hpvm_initTensorRt(0); 
-
-  std::string dir_prefix = std::string("../model_params/mobilenet_shallow/"); 
-  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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-  std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-  void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-  std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-  std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-  std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-  void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-  void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,128,128,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-  std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-  void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-  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(), 0,256,128,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-  void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-
-  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(), 0,256,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-  void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-  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 missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-    
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9); 
-      void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorRelu(var_1); 
-      void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_5 = tensorRelu(var_4); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9); 
-      void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_11 = tensorRelu(var_10); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9); 
-      void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_14 = tensorRelu(var_13); 
-      void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9); 
-      void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_20 = tensorRelu(var_19); 
-      void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9); 
-      void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9); 
-      void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorRelu(var_31); 
-      void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_35 = tensorRelu(var_34); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9); 
-      void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2); 
-      void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9); 
-      void* var_41 = tensorSoftmax(var_40); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_41); 
-      final_accuracy += accuracy;
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_41, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-    
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc
deleted file mode 100644
index 757f950249566ce658a8e7e7289cc64ab034b396..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc
+++ /dev/null
@@ -1,304 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  int missed = 0;
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-    
-    int batch_count = test_input_size / batch_size; 
-    float final_accuracy = 0.0; 
-
-    std::string dir_prefix = std::string("../model_params/mobilenet_shallow/"); 
-    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"); 
-
-    for(int i = 0; i < batch_count; i++){ 
-
-      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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-      void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-      void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-      void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-      void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-      std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-      void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-      std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-      void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-      void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-      void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-      std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-      void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-      std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-      void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-      void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-      void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-      void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-      std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-      void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-      std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-      void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-      void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-      void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-      void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-      std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-      void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-      void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-      void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-      void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-      std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-      void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-      std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-      void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-      void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-      void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-      void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-      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(), 0,128,128,1,1); 
-      std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-      void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-      void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-      void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-      void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-      std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-      void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-      std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-      void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-      void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-      void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-      void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-      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(), 0,256,128,1,1); 
-      std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-      void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-      void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-      void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-      void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-      std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-      void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-      std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-      void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-      void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-      void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-      void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-      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(), 0,256,256,1,1); 
-      std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-      void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-      void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-      void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-      void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-      std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-      void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-      std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-      void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-      void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-      void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-      void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-      void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-      std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-      void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-      void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-      void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-      void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-      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 start = i * batch_size + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9); 
-      void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = tensorRelu(var_1); 
-      void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-      void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_5 = tensorRelu(var_4); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9); 
-      void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-      void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_11 = tensorRelu(var_10); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9); 
-      void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_14 = tensorRelu(var_13); 
-      void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-      void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9); 
-      void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_20 = tensorRelu(var_19); 
-      void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-      void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9); 
-      void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-      void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9); 
-      void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorRelu(var_31); 
-      void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-      void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_35 = tensorRelu(var_34); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9); 
-      void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2); 
-      void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9); 
-      void* var_41 = tensorSoftmax(var_40); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_41); 
-      final_accuracy += accuracy;
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_41, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-    
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_piped.cc
deleted file mode 100644
index 8446e1f2583c99758f9fdd84c71dd0c8d31cd182..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_piped.cc
+++ /dev/null
@@ -1,265 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 250;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-  
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  llvm_hpvm_initTensorRt(0); 
-
-
-  std::string dir_prefix = std::string("../model_params/resnet18_cifar10/");	   
-  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(), 0,16,3,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  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(), 0,16,16,3,3); 
-  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(), 0,1,16,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3); 
-  std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-  void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1); 
-  std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-  void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3); 
-  std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-  void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-  void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1); 
-  std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-  void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-  void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-  void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-  void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-  void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-  void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-  void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-  void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-  void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-  void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-  void* conv2d_21_b =  readTrainedWeights(conv2d_21_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(), 0,1,1,64,10); 
-  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 missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-    
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.8791630274057383, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 0.8791630274057383, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1859495645761484, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1.1859495645761484, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -2.2316832554340365, 2.266301159858699, 9); 
-      void* var_3 = tensorAdd(var_0, var_2); 
-      void* var_4 = tensorRelu(var_3); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.789569139480591, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3341254055499974, 9); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 1.3341254055499974, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -0.9912706619501114, 1.0245310074090952, 9); 
-      void* var_7 = tensorAdd(var_4, var_6); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 2.998989346027372, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2850778144597967, 9); 
-      void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1.2850778144597967, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -1.2940701305866242, 0.7332147359848022, 9); 
-      void* var_11 = tensorAdd(var_8, var_10); 
-      void* var_12 = tensorRelu(var_11); 
-      void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 3.6232483506202584, 9); 
-      void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 3.6232483506202584, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -6.319877154827118, 6.882811555862418, 9); 
-      void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -0.9930689406394959, 2.8721754658222096, 9); 
-      void* var_16 = tensorAdd(var_15, var_14); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 8.315858840942383, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.6893706333637226, 9); 
-      void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 2.6893706333637226, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -1.8801953810453416, 1.714934362173068, 9); 
-      void* var_20 = tensorAdd(var_17, var_19); 
-      void* var_21 = tensorRelu(var_20); 
-      void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 8.381670951843262, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.569231034517287, 9); 
-      void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 2.569231034517287, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -1.9701244848966597, 1.4661400413513093, 9); 
-      void* var_24 = tensorAdd(var_21, var_23); 
-      void* var_25 = tensorRelu(var_24); 
-      void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 12.411911067962677, 9); 
-      void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 12.411911067962677, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -25.407194147109987, 20.519153985977383, 9); 
-      void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -4.256520752906799, 5.730506427288059, 9); 
-      void* var_29 = tensorAdd(var_28, var_27); 
-      void* var_30 = tensorRelu(var_29); 
-      void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 22.350475664138983, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 23.93387042045599, 9); 
-      void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 23.93387042045599, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -35.37134181976318, 19.209569931030273, 9); 
-      void* var_33 = tensorAdd(var_30, var_32); 
-      void* var_34 = tensorRelu(var_33); 
-      void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 29.434949998855657, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 27.216757345199866, 9); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 27.216757345199866, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -42.31447326660156, 29.365212144852038, 9); 
-      void* var_37 = tensorAdd(var_34, var_36); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8); 
-      void* var_40 = FCLayer_PROMISE(var_39, 0.0, 13.736315393447876, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -45.52749088287353, 31.64324799537669, 9); 
-      void* var_41 = tensorSoftmax(var_40); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_41); 
-      final_accuracy += accuracy;
-
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_41, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-    
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc
deleted file mode 100644
index 2ade8b6090d69d733399a399619442cede2bfde9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc
+++ /dev/null
@@ -1,259 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-  
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-  
-
-  llvm_hpvm_initTensorRt(0); 
-
-  
-  int missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    if (missed >= to_skip){
-     break;           
-    }
-
-    startMemTracking(); 
-    
-    int batch_count = test_input_size / batch_size; 
-    float final_accuracy = 0.0; 
-
-
-    std::string dir_prefix = std::string("../model_params/resnet18_cifar10/");	   
-    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");
-    
-    for(int i = 0; i < batch_count; i++){ 
-
-      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(), 0,16,3,3,3); 
-      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(), 0,1,16,1,1); 
-      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(), 0,16,16,3,3); 
-      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(), 0,1,16,1,1); 
-      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(), 0,16,16,3,3); 
-      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(), 0,1,16,1,1); 
-      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(), 0,16,16,3,3); 
-      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(), 0,1,16,1,1); 
-      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(), 0,16,16,3,3); 
-      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(), 0,1,16,1,1); 
-      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(), 0,16,16,3,3); 
-      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(), 0,1,16,1,1); 
-      std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-      void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3); 
-      std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-      void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1); 
-      std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-      void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3); 
-      std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-      void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-      void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1); 
-      std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-      void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-      void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3); 
-      std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-      void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-      void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3); 
-      std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-      void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-      void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3); 
-      std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-      void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-      void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3); 
-      std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-      void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-      void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3); 
-      std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-      void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1); 
-      std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-      void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3); 
-      std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-      void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-      void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1); 
-      std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-      void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-      void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-      void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-      void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-      void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-      void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-      void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-      void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-      void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-      void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-      void* conv2d_21_b =  readTrainedWeights(conv2d_21_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(), 0,1,1,64,10); 
-      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 start = i * batch_size + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.8791630274057383, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 0.8791630274057383, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1859495645761484, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1.1859495645761484, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -2.2316832554340365, 2.266301159858699, 9); 
-      void* var_3 = tensorAdd(var_0, var_2); 
-      void* var_4 = tensorRelu(var_3); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.789569139480591, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3341254055499974, 9); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 1.3341254055499974, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -0.9912706619501114, 1.0245310074090952, 9); 
-      void* var_7 = tensorAdd(var_4, var_6); 
-      void* var_8 = tensorRelu(var_7); 
-      void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 2.998989346027372, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2850778144597967, 9); 
-      void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1.2850778144597967, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -1.2940701305866242, 0.7332147359848022, 9); 
-      void* var_11 = tensorAdd(var_8, var_10); 
-      void* var_12 = tensorRelu(var_11); 
-      void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 3.6232483506202584, 9); 
-      void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 3.6232483506202584, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -6.319877154827118, 6.882811555862418, 9); 
-      void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -0.9930689406394959, 2.8721754658222096, 9); 
-      void* var_16 = tensorAdd(var_15, var_14); 
-      void* var_17 = tensorRelu(var_16); 
-      void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 8.315858840942383, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.6893706333637226, 9); 
-      void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 2.6893706333637226, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -1.8801953810453416, 1.714934362173068, 9); 
-      void* var_20 = tensorAdd(var_17, var_19); 
-      void* var_21 = tensorRelu(var_20); 
-      void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 8.381670951843262, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.569231034517287, 9); 
-      void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 2.569231034517287, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -1.9701244848966597, 1.4661400413513093, 9); 
-      void* var_24 = tensorAdd(var_21, var_23); 
-      void* var_25 = tensorRelu(var_24); 
-      void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 12.411911067962677, 9); 
-      void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 12.411911067962677, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -25.407194147109987, 20.519153985977383, 9); 
-      void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -4.256520752906799, 5.730506427288059, 9); 
-      void* var_29 = tensorAdd(var_28, var_27); 
-      void* var_30 = tensorRelu(var_29); 
-      void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 22.350475664138983, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 23.93387042045599, 9); 
-      void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 23.93387042045599, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -35.37134181976318, 19.209569931030273, 9); 
-      void* var_33 = tensorAdd(var_30, var_32); 
-      void* var_34 = tensorRelu(var_33); 
-      void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 29.434949998855657, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 27.216757345199866, 9); 
-      void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 27.216757345199866, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -42.31447326660156, 29.365212144852038, 9); 
-      void* var_37 = tensorAdd(var_34, var_36); 
-      void* var_38 = tensorRelu(var_37); 
-      void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8); 
-      void* var_40 = FCLayer_PROMISE(var_39, 0.0, 13.736315393447876, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -45.52749088287353, 31.64324799537669, 9); 
-      void* var_41 = tensorSoftmax(var_40); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_41); 
-      final_accuracy += accuracy;
-
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_41, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-      
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_piped.cc
deleted file mode 100644
index 1e61f9e993e0de5678c203b4e09d570c15f4d63c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_piped.cc
+++ /dev/null
@@ -1,925 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  llvm_hpvm_initTensorRt(0); 
-
-
-  int total_runs = 1;
-  int offset = 0;
- 
-  int test_input_size = 2000; 
-  int batch_size = 50; 
-  int batch_count = test_input_size / batch_size; 
-  float final_accuracy = 0.0; 
-
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-
-
-
-  
-  std::string dir_prefix = std::string("/shared/hsharif3/resnet50_imagenet/"); 
-  std::string input_path =  dir_prefix + std::string("test_input.bin"); 
-  std::string labels_path =  dir_prefix + std::string("test_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(), 0,64,3,7,7); 
-  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(), 0,1,64,1,1); 
-  std::string batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-  void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-  void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-  void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-  void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,64,1,1); 
-  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(), 0,64,64,1,1); 
-  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 batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-  void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-  void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-  void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-  void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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 batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-  void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-  void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-  void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-  void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_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(), 0,256,64,1,1); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,64,1,1); 
-  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(), 0,1,256,1,1); 
-  std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-  void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-  void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-  void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-  void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-  void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-  void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-  void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-  void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,256,1,1); 
-  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(), 0,64,256,1,1); 
-  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(), 0,1,64,1,1); 
-  std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-  void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-  void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-  void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-  void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-  void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-  void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-  void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-  void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,256,64,1,1); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-  void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-  void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-  void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-  void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,64,256,1,1); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-  void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-  void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-  void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-  void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,64,64,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-  void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-  void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-  void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,64,1,1); 
-  std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-  void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,64,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,256,64,1,1); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-  void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-  void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-  void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-  void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,128,256,1,1); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-  void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-  void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-  void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-  void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,128,128,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-  void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-  void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-  void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-  void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-  void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,512,128,1,1); 
-  std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-  void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-  void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,512,256,1,1); 
-  std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-  void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-  void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-  void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-  void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-  void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-  void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-  void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-  void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-  void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-  void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,128,512,1,1); 
-  std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-  void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-  void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-  void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-  void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-  void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-  void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,128,128,3,3); 
-  std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-  void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-  void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-  void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-  void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-  void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-  void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,512,128,1,1); 
-  std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-  void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-  void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-  void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-  void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-  void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-  void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,128,512,1,1); 
-  std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-  void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-  void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-  void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-  void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-  void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-  void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,128,128,3,3); 
-  std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-  void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-  void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-  void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-  void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-  void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-  void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,512,128,1,1); 
-  std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-  void* conv2d_21_b =  readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-  void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-  void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-  void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-  void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_22_w_path =  dir_prefix + std::string("conv2d_22_w.bin"); 
-  void* conv2d_22_w =  readTrainedWeights(conv2d_22_w_path.c_str(), 0,128,512,1,1); 
-  std::string conv2d_22_b_path =  dir_prefix + std::string("conv2d_22_b.bin"); 
-  void* conv2d_22_b =  readTrainedWeights(conv2d_22_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-  void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-  void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-  void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-  void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_23_w_path =  dir_prefix + std::string("conv2d_23_w.bin"); 
-  void* conv2d_23_w =  readTrainedWeights(conv2d_23_w_path.c_str(), 0,128,128,3,3); 
-  std::string conv2d_23_b_path =  dir_prefix + std::string("conv2d_23_b.bin"); 
-  void* conv2d_23_b =  readTrainedWeights(conv2d_23_b_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-  void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-  void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-  void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,128,1,1); 
-  std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-  void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,128,1,1); 
-  std::string conv2d_24_w_path =  dir_prefix + std::string("conv2d_24_w.bin"); 
-  void* conv2d_24_w =  readTrainedWeights(conv2d_24_w_path.c_str(), 0,512,128,1,1); 
-  std::string conv2d_24_b_path =  dir_prefix + std::string("conv2d_24_b.bin"); 
-  void* conv2d_24_b =  readTrainedWeights(conv2d_24_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-  void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-  void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-  void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-  void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_25_w_path =  dir_prefix + std::string("conv2d_25_w.bin"); 
-  void* conv2d_25_w =  readTrainedWeights(conv2d_25_w_path.c_str(), 0,256,512,1,1); 
-  std::string conv2d_25_b_path =  dir_prefix + std::string("conv2d_25_b.bin"); 
-  void* conv2d_25_b =  readTrainedWeights(conv2d_25_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-  void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-  void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-  void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-  void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_26_w_path =  dir_prefix + std::string("conv2d_26_w.bin"); 
-  void* conv2d_26_w =  readTrainedWeights(conv2d_26_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_26_b_path =  dir_prefix + std::string("conv2d_26_b.bin"); 
-  void* conv2d_26_b =  readTrainedWeights(conv2d_26_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-  void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-  void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-  void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-  void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_27_w_path =  dir_prefix + std::string("conv2d_27_w.bin"); 
-  void* conv2d_27_w =  readTrainedWeights(conv2d_27_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_27_b_path =  dir_prefix + std::string("conv2d_27_b.bin"); 
-  void* conv2d_27_b =  readTrainedWeights(conv2d_27_b_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_28_w_path =  dir_prefix + std::string("conv2d_28_w.bin"); 
-  void* conv2d_28_w =  readTrainedWeights(conv2d_28_w_path.c_str(), 0,1024,512,1,1); 
-  std::string conv2d_28_b_path =  dir_prefix + std::string("conv2d_28_b.bin"); 
-  void* conv2d_28_b =  readTrainedWeights(conv2d_28_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-  void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-  void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-  void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-  void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_28_gamma_path =  dir_prefix + std::string("batch_normalization_28_gamma.bin"); 
-  void* batch_normalization_28_gamma =  readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_28_beta_path =  dir_prefix + std::string("batch_normalization_28_beta.bin"); 
-  void* batch_normalization_28_beta =  readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_28_mean_path =  dir_prefix + std::string("batch_normalization_28_mean.bin"); 
-  void* batch_normalization_28_mean =  readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_28_variance_path =  dir_prefix + std::string("batch_normalization_28_variance.bin"); 
-  void* batch_normalization_28_variance =  readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_29_w_path =  dir_prefix + std::string("conv2d_29_w.bin"); 
-  void* conv2d_29_w =  readTrainedWeights(conv2d_29_w_path.c_str(), 0,256,1024,1,1); 
-  std::string conv2d_29_b_path =  dir_prefix + std::string("conv2d_29_b.bin"); 
-  void* conv2d_29_b =  readTrainedWeights(conv2d_29_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_29_gamma_path =  dir_prefix + std::string("batch_normalization_29_gamma.bin"); 
-  void* batch_normalization_29_gamma =  readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_29_beta_path =  dir_prefix + std::string("batch_normalization_29_beta.bin"); 
-  void* batch_normalization_29_beta =  readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_29_mean_path =  dir_prefix + std::string("batch_normalization_29_mean.bin"); 
-  void* batch_normalization_29_mean =  readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_29_variance_path =  dir_prefix + std::string("batch_normalization_29_variance.bin"); 
-  void* batch_normalization_29_variance =  readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_30_w_path =  dir_prefix + std::string("conv2d_30_w.bin"); 
-  void* conv2d_30_w =  readTrainedWeights(conv2d_30_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_30_b_path =  dir_prefix + std::string("conv2d_30_b.bin"); 
-  void* conv2d_30_b =  readTrainedWeights(conv2d_30_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_30_gamma_path =  dir_prefix + std::string("batch_normalization_30_gamma.bin"); 
-  void* batch_normalization_30_gamma =  readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_30_beta_path =  dir_prefix + std::string("batch_normalization_30_beta.bin"); 
-  void* batch_normalization_30_beta =  readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_30_mean_path =  dir_prefix + std::string("batch_normalization_30_mean.bin"); 
-  void* batch_normalization_30_mean =  readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_30_variance_path =  dir_prefix + std::string("batch_normalization_30_variance.bin"); 
-  void* batch_normalization_30_variance =  readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_31_w_path =  dir_prefix + std::string("conv2d_31_w.bin"); 
-  void* conv2d_31_w =  readTrainedWeights(conv2d_31_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_31_b_path =  dir_prefix + std::string("conv2d_31_b.bin"); 
-  void* conv2d_31_b =  readTrainedWeights(conv2d_31_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_31_gamma_path =  dir_prefix + std::string("batch_normalization_31_gamma.bin"); 
-  void* batch_normalization_31_gamma =  readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_31_beta_path =  dir_prefix + std::string("batch_normalization_31_beta.bin"); 
-  void* batch_normalization_31_beta =  readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_31_mean_path =  dir_prefix + std::string("batch_normalization_31_mean.bin"); 
-  void* batch_normalization_31_mean =  readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_31_variance_path =  dir_prefix + std::string("batch_normalization_31_variance.bin"); 
-  void* batch_normalization_31_variance =  readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_32_w_path =  dir_prefix + std::string("conv2d_32_w.bin"); 
-  void* conv2d_32_w =  readTrainedWeights(conv2d_32_w_path.c_str(), 0,256,1024,1,1); 
-  std::string conv2d_32_b_path =  dir_prefix + std::string("conv2d_32_b.bin"); 
-  void* conv2d_32_b =  readTrainedWeights(conv2d_32_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_32_gamma_path =  dir_prefix + std::string("batch_normalization_32_gamma.bin"); 
-  void* batch_normalization_32_gamma =  readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_32_beta_path =  dir_prefix + std::string("batch_normalization_32_beta.bin"); 
-  void* batch_normalization_32_beta =  readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_32_mean_path =  dir_prefix + std::string("batch_normalization_32_mean.bin"); 
-  void* batch_normalization_32_mean =  readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_32_variance_path =  dir_prefix + std::string("batch_normalization_32_variance.bin"); 
-  void* batch_normalization_32_variance =  readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_33_w_path =  dir_prefix + std::string("conv2d_33_w.bin"); 
-  void* conv2d_33_w =  readTrainedWeights(conv2d_33_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_33_b_path =  dir_prefix + std::string("conv2d_33_b.bin"); 
-  void* conv2d_33_b =  readTrainedWeights(conv2d_33_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_33_gamma_path =  dir_prefix + std::string("batch_normalization_33_gamma.bin"); 
-  void* batch_normalization_33_gamma =  readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_33_beta_path =  dir_prefix + std::string("batch_normalization_33_beta.bin"); 
-  void* batch_normalization_33_beta =  readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_33_mean_path =  dir_prefix + std::string("batch_normalization_33_mean.bin"); 
-  void* batch_normalization_33_mean =  readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_33_variance_path =  dir_prefix + std::string("batch_normalization_33_variance.bin"); 
-  void* batch_normalization_33_variance =  readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_34_w_path =  dir_prefix + std::string("conv2d_34_w.bin"); 
-  void* conv2d_34_w =  readTrainedWeights(conv2d_34_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_34_b_path =  dir_prefix + std::string("conv2d_34_b.bin"); 
-  void* conv2d_34_b =  readTrainedWeights(conv2d_34_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_34_gamma_path =  dir_prefix + std::string("batch_normalization_34_gamma.bin"); 
-  void* batch_normalization_34_gamma =  readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_34_beta_path =  dir_prefix + std::string("batch_normalization_34_beta.bin"); 
-  void* batch_normalization_34_beta =  readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_34_mean_path =  dir_prefix + std::string("batch_normalization_34_mean.bin"); 
-  void* batch_normalization_34_mean =  readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_34_variance_path =  dir_prefix + std::string("batch_normalization_34_variance.bin"); 
-  void* batch_normalization_34_variance =  readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_35_w_path =  dir_prefix + std::string("conv2d_35_w.bin"); 
-  void* conv2d_35_w =  readTrainedWeights(conv2d_35_w_path.c_str(), 0,256,1024,1,1); 
-  std::string conv2d_35_b_path =  dir_prefix + std::string("conv2d_35_b.bin"); 
-  void* conv2d_35_b =  readTrainedWeights(conv2d_35_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_35_gamma_path =  dir_prefix + std::string("batch_normalization_35_gamma.bin"); 
-  void* batch_normalization_35_gamma =  readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_35_beta_path =  dir_prefix + std::string("batch_normalization_35_beta.bin"); 
-  void* batch_normalization_35_beta =  readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_35_mean_path =  dir_prefix + std::string("batch_normalization_35_mean.bin"); 
-  void* batch_normalization_35_mean =  readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_35_variance_path =  dir_prefix + std::string("batch_normalization_35_variance.bin"); 
-  void* batch_normalization_35_variance =  readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_36_w_path =  dir_prefix + std::string("conv2d_36_w.bin"); 
-  void* conv2d_36_w =  readTrainedWeights(conv2d_36_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_36_b_path =  dir_prefix + std::string("conv2d_36_b.bin"); 
-  void* conv2d_36_b =  readTrainedWeights(conv2d_36_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_36_gamma_path =  dir_prefix + std::string("batch_normalization_36_gamma.bin"); 
-  void* batch_normalization_36_gamma =  readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_36_beta_path =  dir_prefix + std::string("batch_normalization_36_beta.bin"); 
-  void* batch_normalization_36_beta =  readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_36_mean_path =  dir_prefix + std::string("batch_normalization_36_mean.bin"); 
-  void* batch_normalization_36_mean =  readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_36_variance_path =  dir_prefix + std::string("batch_normalization_36_variance.bin"); 
-  void* batch_normalization_36_variance =  readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_37_w_path =  dir_prefix + std::string("conv2d_37_w.bin"); 
-  void* conv2d_37_w =  readTrainedWeights(conv2d_37_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_37_b_path =  dir_prefix + std::string("conv2d_37_b.bin"); 
-  void* conv2d_37_b =  readTrainedWeights(conv2d_37_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_37_gamma_path =  dir_prefix + std::string("batch_normalization_37_gamma.bin"); 
-  void* batch_normalization_37_gamma =  readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_37_beta_path =  dir_prefix + std::string("batch_normalization_37_beta.bin"); 
-  void* batch_normalization_37_beta =  readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_37_mean_path =  dir_prefix + std::string("batch_normalization_37_mean.bin"); 
-  void* batch_normalization_37_mean =  readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_37_variance_path =  dir_prefix + std::string("batch_normalization_37_variance.bin"); 
-  void* batch_normalization_37_variance =  readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_38_w_path =  dir_prefix + std::string("conv2d_38_w.bin"); 
-  void* conv2d_38_w =  readTrainedWeights(conv2d_38_w_path.c_str(), 0,256,1024,1,1); 
-  std::string conv2d_38_b_path =  dir_prefix + std::string("conv2d_38_b.bin"); 
-  void* conv2d_38_b =  readTrainedWeights(conv2d_38_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_38_gamma_path =  dir_prefix + std::string("batch_normalization_38_gamma.bin"); 
-  void* batch_normalization_38_gamma =  readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_38_beta_path =  dir_prefix + std::string("batch_normalization_38_beta.bin"); 
-  void* batch_normalization_38_beta =  readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_38_mean_path =  dir_prefix + std::string("batch_normalization_38_mean.bin"); 
-  void* batch_normalization_38_mean =  readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_38_variance_path =  dir_prefix + std::string("batch_normalization_38_variance.bin"); 
-  void* batch_normalization_38_variance =  readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_39_w_path =  dir_prefix + std::string("conv2d_39_w.bin"); 
-  void* conv2d_39_w =  readTrainedWeights(conv2d_39_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_39_b_path =  dir_prefix + std::string("conv2d_39_b.bin"); 
-  void* conv2d_39_b =  readTrainedWeights(conv2d_39_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_39_gamma_path =  dir_prefix + std::string("batch_normalization_39_gamma.bin"); 
-  void* batch_normalization_39_gamma =  readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_39_beta_path =  dir_prefix + std::string("batch_normalization_39_beta.bin"); 
-  void* batch_normalization_39_beta =  readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_39_mean_path =  dir_prefix + std::string("batch_normalization_39_mean.bin"); 
-  void* batch_normalization_39_mean =  readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_39_variance_path =  dir_prefix + std::string("batch_normalization_39_variance.bin"); 
-  void* batch_normalization_39_variance =  readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_40_w_path =  dir_prefix + std::string("conv2d_40_w.bin"); 
-  void* conv2d_40_w =  readTrainedWeights(conv2d_40_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_40_b_path =  dir_prefix + std::string("conv2d_40_b.bin"); 
-  void* conv2d_40_b =  readTrainedWeights(conv2d_40_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_40_gamma_path =  dir_prefix + std::string("batch_normalization_40_gamma.bin"); 
-  void* batch_normalization_40_gamma =  readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_40_beta_path =  dir_prefix + std::string("batch_normalization_40_beta.bin"); 
-  void* batch_normalization_40_beta =  readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_40_mean_path =  dir_prefix + std::string("batch_normalization_40_mean.bin"); 
-  void* batch_normalization_40_mean =  readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_40_variance_path =  dir_prefix + std::string("batch_normalization_40_variance.bin"); 
-  void* batch_normalization_40_variance =  readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_41_w_path =  dir_prefix + std::string("conv2d_41_w.bin"); 
-  void* conv2d_41_w =  readTrainedWeights(conv2d_41_w_path.c_str(), 0,256,1024,1,1); 
-  std::string conv2d_41_b_path =  dir_prefix + std::string("conv2d_41_b.bin"); 
-  void* conv2d_41_b =  readTrainedWeights(conv2d_41_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_41_gamma_path =  dir_prefix + std::string("batch_normalization_41_gamma.bin"); 
-  void* batch_normalization_41_gamma =  readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_41_beta_path =  dir_prefix + std::string("batch_normalization_41_beta.bin"); 
-  void* batch_normalization_41_beta =  readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_41_mean_path =  dir_prefix + std::string("batch_normalization_41_mean.bin"); 
-  void* batch_normalization_41_mean =  readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_41_variance_path =  dir_prefix + std::string("batch_normalization_41_variance.bin"); 
-  void* batch_normalization_41_variance =  readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_42_w_path =  dir_prefix + std::string("conv2d_42_w.bin"); 
-  void* conv2d_42_w =  readTrainedWeights(conv2d_42_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_42_b_path =  dir_prefix + std::string("conv2d_42_b.bin"); 
-  void* conv2d_42_b =  readTrainedWeights(conv2d_42_b_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_42_gamma_path =  dir_prefix + std::string("batch_normalization_42_gamma.bin"); 
-  void* batch_normalization_42_gamma =  readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_42_beta_path =  dir_prefix + std::string("batch_normalization_42_beta.bin"); 
-  void* batch_normalization_42_beta =  readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_42_mean_path =  dir_prefix + std::string("batch_normalization_42_mean.bin"); 
-  void* batch_normalization_42_mean =  readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,256,1,1); 
-  std::string batch_normalization_42_variance_path =  dir_prefix + std::string("batch_normalization_42_variance.bin"); 
-  void* batch_normalization_42_variance =  readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_43_w_path =  dir_prefix + std::string("conv2d_43_w.bin"); 
-  void* conv2d_43_w =  readTrainedWeights(conv2d_43_w_path.c_str(), 0,1024,256,1,1); 
-  std::string conv2d_43_b_path =  dir_prefix + std::string("conv2d_43_b.bin"); 
-  void* conv2d_43_b =  readTrainedWeights(conv2d_43_b_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_43_gamma_path =  dir_prefix + std::string("batch_normalization_43_gamma.bin"); 
-  void* batch_normalization_43_gamma =  readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_43_beta_path =  dir_prefix + std::string("batch_normalization_43_beta.bin"); 
-  void* batch_normalization_43_beta =  readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_43_mean_path =  dir_prefix + std::string("batch_normalization_43_mean.bin"); 
-  void* batch_normalization_43_mean =  readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,1024,1,1); 
-  std::string batch_normalization_43_variance_path =  dir_prefix + std::string("batch_normalization_43_variance.bin"); 
-  void* batch_normalization_43_variance =  readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,1024,1,1); 
-  std::string conv2d_44_w_path =  dir_prefix + std::string("conv2d_44_w.bin"); 
-  void* conv2d_44_w =  readTrainedWeights(conv2d_44_w_path.c_str(), 0,512,1024,1,1); 
-  std::string conv2d_44_b_path =  dir_prefix + std::string("conv2d_44_b.bin"); 
-  void* conv2d_44_b =  readTrainedWeights(conv2d_44_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_44_gamma_path =  dir_prefix + std::string("batch_normalization_44_gamma.bin"); 
-  void* batch_normalization_44_gamma =  readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_44_beta_path =  dir_prefix + std::string("batch_normalization_44_beta.bin"); 
-  void* batch_normalization_44_beta =  readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_44_mean_path =  dir_prefix + std::string("batch_normalization_44_mean.bin"); 
-  void* batch_normalization_44_mean =  readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_44_variance_path =  dir_prefix + std::string("batch_normalization_44_variance.bin"); 
-  void* batch_normalization_44_variance =  readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_45_w_path =  dir_prefix + std::string("conv2d_45_w.bin"); 
-  void* conv2d_45_w =  readTrainedWeights(conv2d_45_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_45_b_path =  dir_prefix + std::string("conv2d_45_b.bin"); 
-  void* conv2d_45_b =  readTrainedWeights(conv2d_45_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_45_gamma_path =  dir_prefix + std::string("batch_normalization_45_gamma.bin"); 
-  void* batch_normalization_45_gamma =  readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_45_beta_path =  dir_prefix + std::string("batch_normalization_45_beta.bin"); 
-  void* batch_normalization_45_beta =  readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_45_mean_path =  dir_prefix + std::string("batch_normalization_45_mean.bin"); 
-  void* batch_normalization_45_mean =  readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_45_variance_path =  dir_prefix + std::string("batch_normalization_45_variance.bin"); 
-  void* batch_normalization_45_variance =  readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_46_w_path =  dir_prefix + std::string("conv2d_46_w.bin"); 
-  void* conv2d_46_w =  readTrainedWeights(conv2d_46_w_path.c_str(), 0,2048,512,1,1); 
-  std::string conv2d_46_b_path =  dir_prefix + std::string("conv2d_46_b.bin"); 
-  void* conv2d_46_b =  readTrainedWeights(conv2d_46_b_path.c_str(), 0,1,2048,1,1); 
-  std::string conv2d_47_w_path =  dir_prefix + std::string("conv2d_47_w.bin"); 
-  void* conv2d_47_w =  readTrainedWeights(conv2d_47_w_path.c_str(), 0,2048,1024,1,1); 
-  std::string conv2d_47_b_path =  dir_prefix + std::string("conv2d_47_b.bin"); 
-  void* conv2d_47_b =  readTrainedWeights(conv2d_47_b_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_46_gamma_path =  dir_prefix + std::string("batch_normalization_46_gamma.bin"); 
-  void* batch_normalization_46_gamma =  readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_46_beta_path =  dir_prefix + std::string("batch_normalization_46_beta.bin"); 
-  void* batch_normalization_46_beta =  readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_46_mean_path =  dir_prefix + std::string("batch_normalization_46_mean.bin"); 
-  void* batch_normalization_46_mean =  readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_46_variance_path =  dir_prefix + std::string("batch_normalization_46_variance.bin"); 
-  void* batch_normalization_46_variance =  readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_47_gamma_path =  dir_prefix + std::string("batch_normalization_47_gamma.bin"); 
-  void* batch_normalization_47_gamma =  readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_47_beta_path =  dir_prefix + std::string("batch_normalization_47_beta.bin"); 
-  void* batch_normalization_47_beta =  readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_47_mean_path =  dir_prefix + std::string("batch_normalization_47_mean.bin"); 
-  void* batch_normalization_47_mean =  readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_47_variance_path =  dir_prefix + std::string("batch_normalization_47_variance.bin"); 
-  void* batch_normalization_47_variance =  readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,2048,1,1); 
-  std::string conv2d_48_w_path =  dir_prefix + std::string("conv2d_48_w.bin"); 
-  void* conv2d_48_w =  readTrainedWeights(conv2d_48_w_path.c_str(), 0,512,2048,1,1); 
-  std::string conv2d_48_b_path =  dir_prefix + std::string("conv2d_48_b.bin"); 
-  void* conv2d_48_b =  readTrainedWeights(conv2d_48_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_48_gamma_path =  dir_prefix + std::string("batch_normalization_48_gamma.bin"); 
-  void* batch_normalization_48_gamma =  readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_48_beta_path =  dir_prefix + std::string("batch_normalization_48_beta.bin"); 
-  void* batch_normalization_48_beta =  readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_48_mean_path =  dir_prefix + std::string("batch_normalization_48_mean.bin"); 
-  void* batch_normalization_48_mean =  readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_48_variance_path =  dir_prefix + std::string("batch_normalization_48_variance.bin"); 
-  void* batch_normalization_48_variance =  readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_49_w_path =  dir_prefix + std::string("conv2d_49_w.bin"); 
-  void* conv2d_49_w =  readTrainedWeights(conv2d_49_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_49_b_path =  dir_prefix + std::string("conv2d_49_b.bin"); 
-  void* conv2d_49_b =  readTrainedWeights(conv2d_49_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_49_gamma_path =  dir_prefix + std::string("batch_normalization_49_gamma.bin"); 
-  void* batch_normalization_49_gamma =  readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_49_beta_path =  dir_prefix + std::string("batch_normalization_49_beta.bin"); 
-  void* batch_normalization_49_beta =  readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_49_mean_path =  dir_prefix + std::string("batch_normalization_49_mean.bin"); 
-  void* batch_normalization_49_mean =  readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_49_variance_path =  dir_prefix + std::string("batch_normalization_49_variance.bin"); 
-  void* batch_normalization_49_variance =  readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_50_w_path =  dir_prefix + std::string("conv2d_50_w.bin"); 
-  void* conv2d_50_w =  readTrainedWeights(conv2d_50_w_path.c_str(), 0,2048,512,1,1); 
-  std::string conv2d_50_b_path =  dir_prefix + std::string("conv2d_50_b.bin"); 
-  void* conv2d_50_b =  readTrainedWeights(conv2d_50_b_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_50_gamma_path =  dir_prefix + std::string("batch_normalization_50_gamma.bin"); 
-  void* batch_normalization_50_gamma =  readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_50_beta_path =  dir_prefix + std::string("batch_normalization_50_beta.bin"); 
-  void* batch_normalization_50_beta =  readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_50_mean_path =  dir_prefix + std::string("batch_normalization_50_mean.bin"); 
-  void* batch_normalization_50_mean =  readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_50_variance_path =  dir_prefix + std::string("batch_normalization_50_variance.bin"); 
-  void* batch_normalization_50_variance =  readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,2048,1,1); 
-  std::string conv2d_51_w_path =  dir_prefix + std::string("conv2d_51_w.bin"); 
-  void* conv2d_51_w =  readTrainedWeights(conv2d_51_w_path.c_str(), 0,512,2048,1,1); 
-  std::string conv2d_51_b_path =  dir_prefix + std::string("conv2d_51_b.bin"); 
-  void* conv2d_51_b =  readTrainedWeights(conv2d_51_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_51_gamma_path =  dir_prefix + std::string("batch_normalization_51_gamma.bin"); 
-  void* batch_normalization_51_gamma =  readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_51_beta_path =  dir_prefix + std::string("batch_normalization_51_beta.bin"); 
-  void* batch_normalization_51_beta =  readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_51_mean_path =  dir_prefix + std::string("batch_normalization_51_mean.bin"); 
-  void* batch_normalization_51_mean =  readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_51_variance_path =  dir_prefix + std::string("batch_normalization_51_variance.bin"); 
-  void* batch_normalization_51_variance =  readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_52_w_path =  dir_prefix + std::string("conv2d_52_w.bin"); 
-  void* conv2d_52_w =  readTrainedWeights(conv2d_52_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_52_b_path =  dir_prefix + std::string("conv2d_52_b.bin"); 
-  void* conv2d_52_b =  readTrainedWeights(conv2d_52_b_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_52_gamma_path =  dir_prefix + std::string("batch_normalization_52_gamma.bin"); 
-  void* batch_normalization_52_gamma =  readTrainedWeights(batch_normalization_52_gamma_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_52_beta_path =  dir_prefix + std::string("batch_normalization_52_beta.bin"); 
-  void* batch_normalization_52_beta =  readTrainedWeights(batch_normalization_52_beta_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_52_mean_path =  dir_prefix + std::string("batch_normalization_52_mean.bin"); 
-  void* batch_normalization_52_mean =  readTrainedWeights(batch_normalization_52_mean_path.c_str(), 0,1,512,1,1); 
-  std::string batch_normalization_52_variance_path =  dir_prefix + std::string("batch_normalization_52_variance.bin"); 
-  void* batch_normalization_52_variance =  readTrainedWeights(batch_normalization_52_variance_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_53_w_path =  dir_prefix + std::string("conv2d_53_w.bin"); 
-  void* conv2d_53_w =  readTrainedWeights(conv2d_53_w_path.c_str(), 0,2048,512,1,1); 
-  std::string conv2d_53_b_path =  dir_prefix + std::string("conv2d_53_b.bin"); 
-  void* conv2d_53_b =  readTrainedWeights(conv2d_53_b_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_53_gamma_path =  dir_prefix + std::string("batch_normalization_53_gamma.bin"); 
-  void* batch_normalization_53_gamma =  readTrainedWeights(batch_normalization_53_gamma_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_53_beta_path =  dir_prefix + std::string("batch_normalization_53_beta.bin"); 
-  void* batch_normalization_53_beta =  readTrainedWeights(batch_normalization_53_beta_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_53_mean_path =  dir_prefix + std::string("batch_normalization_53_mean.bin"); 
-  void* batch_normalization_53_mean =  readTrainedWeights(batch_normalization_53_mean_path.c_str(), 0,1,2048,1,1); 
-  std::string batch_normalization_53_variance_path =  dir_prefix + std::string("batch_normalization_53_variance.bin"); 
-  void* batch_normalization_53_variance =  readTrainedWeights(batch_normalization_53_variance_path.c_str(), 0,1,2048,1,1); 
-  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(), 0,1,1,2048,1000); 
-  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(), 0,1,1000,1,1); 
-
-
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    startMemTracking(); 
-
-    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,3,224,224); 
-
-      // NOTE: The pooling stride is 3*3 while it should be 2*2 -- interface itself needs fixing -- fix this manually in this case
-      void* var_0 = ConvLayer_PROMISE2(input, -123.68, 151.061, conv2d_1_w, -0.574422012090683, 0.5646807488203113, conv2d_1_b, -0.004829655, 0.014784645, 3, 3, 2, 2, 0, 3, 2, 1, 0.0, 689.7822875976562, 9); 
-      void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = ConvLayer_PROMISE(var_1, -4.952117443084717, 12.02118032741582, conv2d_2_w, -0.5448235973715783, 0.2447893574833928, conv2d_2_b, -0.0001412337, 0.00017318528, 0, 0, 1, 1, -1, 0, -1, -9.212617980003357, 8.107657526016425, 9); 
-      void* var_3 = tensorBatchNorm(var_2, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_4 = tensorRelu(var_3); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 5.801381123542811, conv2d_3_w, -0.18028786177933215, 0.21247629988193606, conv2d_3_b, -7.8663266e-05, 0.00018541634, 1, 1, 1, 1, -1, 0, -1, -6.834556140899658, 8.541351353645396, 9); 
-      void* var_6 = tensorBatchNorm(var_5, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_7 = tensorRelu(var_6); 
-      void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 9.866454153060971, conv2d_4_w, -0.2255178820490837, 0.2254851074665791, conv2d_4_b, -0.00017080337, 0.00021038808, 0, 0, 1, 1, -1, 0, -1, -3.595476400852203, 3.637018930196785, 9); 
-      void* var_9 = tensorBatchNorm(var_8, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_10 = ConvLayer_PROMISE(var_1, -4.952117443084717, 12.02118032741582, conv2d_5_w, -0.43272915667295453, 0.29589187785983095, conv2d_5_b, -0.000107640364, 0.00013177324, 0, 0, 1, 1, -1, 0, -1, -7.581318395137787, 7.8835730876923265, 9); 
-      void* var_11 = tensorBatchNorm(var_10, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_12 = tensorAdd(var_9, var_11); 
-      void* var_13 = tensorRelu(var_12); 
-      void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 5.885549548625953, conv2d_6_w, -0.17062100511789324, 0.1432653286457067, conv2d_6_b, -7.950033e-05, 0.000104833845, 0, 0, 1, 1, -1, 0, -1, -5.310503073692322, 3.8418860490322224, 9); 
-      void* var_15 = tensorBatchNorm(var_14, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_16 = tensorRelu(var_15); 
-      void* var_17 = ConvLayer_PROMISE(var_16, 0.0, 4.006655237674757, conv2d_7_w, -0.15594010630249977, 0.15720265829563249, conv2d_7_b, -6.419372e-05, 6.503685e-05, 1, 1, 1, 1, -1, 0, -1, -3.4114532544612883, 3.075598966121696, 9); 
-      void* var_18 = tensorBatchNorm(var_17, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_19 = tensorRelu(var_18); 
-      void* var_20 = ConvLayer_PROMISE(var_19, 0.0, 4.186545849800112, conv2d_8_w, -0.1599232355505228, 0.17352246379853484, conv2d_8_b, -8.235522e-05, 0.000105946136, 0, 0, 1, 1, -1, 0, -1, -1.5299443051815034, 1.425760628223422, 9); 
-      void* var_21 = tensorBatchNorm(var_20, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_22 = tensorAdd(var_21, var_13); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 6.36634494018557, conv2d_9_w, -0.14470596650242806, 0.14421831880510708, conv2d_9_b, -3.4270335e-05, 4.177745e-05, 0, 0, 1, 1, -1, 0, -1, -4.584994326114654, 3.8648653411866007, 9); 
-      void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 3.3001420612335437, conv2d_10_w, -0.12276832074671984, 0.12627632835507407, conv2d_10_b, -5.8183014e-05, 3.3546e-05, 1, 1, 1, 1, -1, 0, -1, -2.828902014493942, 3.0918669717311893, 9); 
-      void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 5.313344509124818, conv2d_11_w, -0.1685639199912548, 0.16309838759899448, conv2d_11_b, -5.3248757e-05, 5.70645e-05, 0, 0, 1, 1, -1, 0, -1, -1.838510752558708, 1.3678752244711045, 9); 
-      void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorAdd(var_31, var_23); 
-      void* var_33 = tensorRelu(var_32); 
-      void* var_34 = ConvLayer_PROMISE(var_33, 0.0, 6.605899341106429, conv2d_12_w, -0.149728477448225, 0.13948052291572155, conv2d_12_b, -2.5221272e-05, 3.551765e-05, 0, 0, 2, 2, -1, 0, -1, -5.011460402488709, 3.915426737308551, 9); 
-      void* var_35 = tensorBatchNorm(var_34, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_36 = tensorRelu(var_35); 
-      void* var_37 = ConvLayer_PROMISE(var_36, 0.0, 3.794741600990312, conv2d_13_w, -0.09761696971952916, 0.11394361693412249, conv2d_13_b, -3.715329e-05, 2.9298411e-05, 1, 1, 1, 1, -1, 0, -1, -5.206686987876893, 4.520638871669791, 9); 
-      void* var_38 = tensorBatchNorm(var_37, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_39 = tensorRelu(var_38); 
-      void* var_40 = ConvLayer_PROMISE(var_39, 0.0, 3.7149479997158603, conv2d_14_w, -0.14844063371419908, 0.14925702929496953, conv2d_14_b, -6.0864673e-05, 5.4444306e-05, 0, 0, 1, 1, -1, 0, -1, -1.5011818276643754, 1.40834725618366, 9); 
-      void* var_41 = tensorBatchNorm(var_40, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-      void* var_42 = ConvLayer_PROMISE(var_33, 0.0, 6.605899341106429, conv2d_15_w, -0.1642171936035156, 0.16866817833483497, conv2d_15_b, -2.4068044e-05, 2.5504653e-05, 0, 0, 2, 2, -1, 0, -1, -4.410076716423035, 4.014970501422923, 9); 
-      void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-      void* var_44 = tensorAdd(var_41, var_43); 
-      void* var_45 = tensorRelu(var_44); 
-      void* var_46 = ConvLayer_PROMISE(var_45, 0.0, 6.518892978191488, conv2d_16_w, -0.09702376063913107, 0.1054209597408773, conv2d_16_b, -1.47610735e-05, 1.7075112e-05, 0, 0, 1, 1, -1, 0, -1, -4.87446900844574, 3.7661991298198862, 9); 
-      void* var_47 = tensorBatchNorm(var_46, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-      void* var_48 = tensorRelu(var_47); 
-      void* var_49 = ConvLayer_PROMISE(var_48, 0.0, 3.259194364786183, conv2d_17_w, -0.08665236312896013, 0.0898308474570517, conv2d_17_b, -3.9163042e-05, 4.2771928e-05, 1, 1, 1, 1, -1, 0, -1, -2.673636848211288, 2.3574042041302774, 9); 
-      void* var_50 = tensorBatchNorm(var_49, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-      void* var_51 = tensorRelu(var_50); 
-      void* var_52 = ConvLayer_PROMISE(var_51, 0.0, 3.641261647939746, conv2d_18_w, -0.12198246002197266, 0.1347003544867095, conv2d_18_b, -5.3173797e-05, 4.8076203e-05, 0, 0, 1, 1, -1, 0, -1, -1.0623184064626694, 0.916913630664359, 9); 
-      void* var_53 = tensorBatchNorm(var_52, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-      void* var_54 = tensorAdd(var_53, var_45); 
-      void* var_55 = tensorRelu(var_54); 
-      void* var_56 = ConvLayer_PROMISE(var_55, 0.0, 6.852215012073557, conv2d_19_w, -0.1122598509863019, 0.1435348897427337, conv2d_19_b, -1.20778e-05, 2.599136e-05, 0, 0, 1, 1, -1, 0, -1, -6.0281127138137816, 6.227049376964593, 9); 
-      void* var_57 = tensorBatchNorm(var_56, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-      void* var_58 = tensorRelu(var_57); 
-      void* var_59 = ConvLayer_PROMISE(var_58, 0.0, 3.397107238292711, conv2d_20_w, -0.1049889962002635, 0.1349111200869117, conv2d_20_b, -2.7412994e-05, 3.9722e-05, 1, 1, 1, 1, -1, 0, -1, -4.057081372261047, 4.329259678363884, 9); 
-      void* var_60 = tensorBatchNorm(var_59, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-      void* var_61 = tensorRelu(var_60); 
-      void* var_62 = ConvLayer_PROMISE(var_61, 0.0, 3.6484641625881262, conv2d_21_w, -0.1401274445652962, 0.12122062336653527, conv2d_21_b, -5.5854776e-05, 7.8164114e-05, 0, 0, 1, 1, -1, 0, -1, -1.626526164531708, 0.8401960272193048, 9); 
-      void* var_63 = tensorBatchNorm(var_62, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-      void* var_64 = tensorAdd(var_63, var_55); 
-      void* var_65 = tensorRelu(var_64); 
-      void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 6.820035747528095, conv2d_22_w, -0.16039140529930593, 0.18889211259782335, conv2d_22_b, -4.6078047e-05, 3.3613425e-05, 0, 0, 1, 1, -1, 0, -1, -4.6271090393066405, 4.527790556430912, 9); 
-      void* var_67 = tensorBatchNorm(var_66, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-      void* var_68 = tensorRelu(var_67); 
-      void* var_69 = ConvLayer_PROMISE(var_68, 0.0, 4.432856665611537, conv2d_23_w, -0.11397356178611517, 0.10787127982825667, conv2d_23_b, -3.6726604e-05, 2.4220695e-05, 1, 1, 1, 1, -1, 0, -1, -3.697339488506317, 3.1427979104519426, 9); 
-      void* var_70 = tensorBatchNorm(var_69, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-      void* var_71 = tensorRelu(var_70); 
-      void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 4.711423307418915, conv2d_24_w, -0.11341997660696507, 0.1437816035747536, conv2d_24_b, -2.7102393e-05, 3.091236e-05, 0, 0, 1, 1, -1, 0, -1, -1.4133628906011582, 1.2987316379547167, 9); 
-      void* var_73 = tensorBatchNorm(var_72, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-      void* var_74 = tensorAdd(var_73, var_65); 
-      void* var_75 = tensorRelu(var_74); 
-      void* var_76 = ConvLayer_PROMISE(var_75, 0.0, 7.624651549339404, conv2d_25_w, -0.10495923960208893, 0.12068889104576047, conv2d_25_b, -1.0208429e-05, 1.1486276e-05, 0, 0, 2, 2, -1, 0, -1, -3.87531214427948, 3.676609352588745, 9); 
-      void* var_77 = tensorBatchNorm(var_76, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-      void* var_78 = tensorRelu(var_77); 
-      void* var_79 = ConvLayer_PROMISE(var_78, 0.0, 4.044620439529737, conv2d_26_w, -0.07615160812437534, 0.07977425544709099, conv2d_26_b, -2.4272886e-05, 1.6434806e-05, 1, 1, 1, 1, -1, 0, -1, -6.102653044223786, 4.761939919948585, 9); 
-      void* var_80 = tensorBatchNorm(var_79, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-      void* var_81 = tensorRelu(var_80); 
-      void* var_82 = ConvLayer_PROMISE(var_81, 0.0, 3.4468260111809705, conv2d_27_w, -0.11533496034890414, 0.10714908299595141, conv2d_27_b, -3.225456e-05, 4.8422902e-05, 0, 0, 1, 1, -1, 0, -1, -1.319659793496132, 1.0189965035915467, 9); 
-      void* var_83 = tensorBatchNorm(var_82, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-      void* var_84 = ConvLayer_PROMISE(var_75, 0.0, 7.624651549339404, conv2d_28_w, -0.0966497472524643, 0.10240990699082783, conv2d_28_b, -1.4815519e-05, 1.554276e-05, 0, 0, 2, 2, -1, 0, -1, -3.9412443549633025, 3.863056869030064, 9); 
-      void* var_85 = tensorBatchNorm(var_84, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, 0.001); 
-      void* var_86 = tensorAdd(var_83, var_85); 
-      void* var_87 = tensorRelu(var_86); 
-      void* var_88 = ConvLayer_PROMISE(var_87, 0.0, 6.879177100658442, conv2d_29_w, -0.06468586190789938, 0.08113565444201333, conv2d_29_b, -7.4607115e-06, 6.926009e-06, 0, 0, 1, 1, -1, 0, -1, -7.112777866363525, 4.633408185959027, 9); 
-      void* var_89 = tensorBatchNorm(var_88, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, 0.001); 
-      void* var_90 = tensorRelu(var_89); 
-      void* var_91 = ConvLayer_PROMISE(var_90, 0.0, 3.2354076790810105, conv2d_30_w, -0.06493933162838221, 0.07104272978752861, conv2d_30_b, -1.9349398e-05, 2.0178473e-05, 1, 1, 1, 1, -1, 0, -1, -3.226332322359085, 2.5138739056587447, 9); 
-      void* var_92 = tensorBatchNorm(var_91, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, 0.001); 
-      void* var_93 = tensorRelu(var_92); 
-      void* var_94 = ConvLayer_PROMISE(var_93, 0.0, 3.003848925829006, conv2d_31_w, -0.0918996930718422, 0.08853508594632167, conv2d_31_b, -4.2279236e-05, 5.5378885e-05, 0, 0, 1, 1, -1, 0, -1, -0.9247466986179351, 0.572747143149404, 9); 
-      void* var_95 = tensorBatchNorm(var_94, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, 0.001); 
-      void* var_96 = tensorAdd(var_95, var_87); 
-      void* var_97 = tensorRelu(var_96); 
-      void* var_98 = ConvLayer_PROMISE(var_97, 0.0, 6.566591289043519, conv2d_32_w, -0.07145480328053236, 0.09098157961666606, conv2d_32_b, -1.0478255e-05, 1.4408147e-05, 0, 0, 1, 1, -1, 0, -1, -4.183038790225982, 3.5941159300804166, 9); 
-      void* var_99 = tensorBatchNorm(var_98, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, 0.001); 
-      void* var_100 = tensorRelu(var_99); 
-      void* var_101 = ConvLayer_PROMISE(var_100, 0.0, 3.0348211803436556, conv2d_33_w, -0.056237234909087414, 0.06478620118647821, conv2d_33_b, -2.2639133e-05, 2.6081116e-05, 1, 1, 1, 1, -1, 0, -1, -2.098393235206604, 1.706788736581844, 9); 
-      void* var_102 = tensorBatchNorm(var_101, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, 0.001); 
-      void* var_103 = tensorRelu(var_102); 
-      void* var_104 = ConvLayer_PROMISE(var_103, 0.0, 3.248518852949145, conv2d_34_w, -0.07141499005258084, 0.08281665176153225, conv2d_34_b, -3.221229e-05, 4.569047e-05, 0, 0, 1, 1, -1, 0, -1, -0.8273181943893433, 0.7378616912961369, 9); 
-      void* var_105 = tensorBatchNorm(var_104, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, 0.001); 
-      void* var_106 = tensorAdd(var_105, var_97); 
-      void* var_107 = tensorRelu(var_106); 
-      void* var_108 = ConvLayer_PROMISE(var_107, 0.0, 6.7038991017341765, conv2d_35_w, -0.06838216692209244, 0.09303134681284767, conv2d_35_b, -1.047402e-05, 1.0168567e-05, 0, 0, 1, 1, -1, 0, -1, -4.168091129779816, 3.5077465448380494, 9); 
-      void* var_109 = tensorBatchNorm(var_108, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, 0.001); 
-      void* var_110 = tensorRelu(var_109); 
-      void* var_111 = ConvLayer_PROMISE(var_110, 0.0, 2.8976624414922814, conv2d_36_w, -0.05521866928786039, 0.06331418491154919, conv2d_36_b, -3.86494e-05, 2.5999781e-05, 1, 1, 1, 1, -1, 0, -1, -2.182177306175232, 2.0366714165211324, 9); 
-      void* var_112 = tensorBatchNorm(var_111, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, 0.001); 
-      void* var_113 = tensorRelu(var_112); 
-      void* var_114 = ConvLayer_PROMISE(var_113, 0.0, 3.1310220296382933, conv2d_37_w, -0.07256266868114472, 0.08391195811331292, conv2d_37_b, -4.8211587e-05, 4.7546604e-05, 0, 0, 1, 1, -1, 0, -1, -1.1372777166366577, 0.5528145518899268, 9); 
-      void* var_115 = tensorBatchNorm(var_114, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, 0.001); 
-      void* var_116 = tensorAdd(var_115, var_107); 
-      void* var_117 = tensorRelu(var_116); 
-      void* var_118 = ConvLayer_PROMISE(var_117, 0.0, 6.625923678875129, conv2d_38_w, -0.06549047549813986, 0.10113389839232205, conv2d_38_b, -1.2351429e-05, 9.263066e-06, 0, 0, 1, 1, -1, 0, -1, -3.846879935503006, 3.639795066118241, 9); 
-      void* var_119 = tensorBatchNorm(var_118, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, 0.001); 
-      void* var_120 = tensorRelu(var_119); 
-      void* var_121 = ConvLayer_PROMISE(var_120, 0.0, 3.200671393632918, conv2d_39_w, -0.05184716333821415, 0.06296417640149599, conv2d_39_b, -2.4313656e-05, 3.812053e-05, 1, 1, 1, 1, -1, 0, -1, -1.9442583957910538, 1.5269825316667864, 9); 
-      void* var_122 = tensorBatchNorm(var_121, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, 0.001); 
-      void* var_123 = tensorRelu(var_122); 
-      void* var_124 = ConvLayer_PROMISE(var_123, 0.0, 4.040827783107826, conv2d_40_w, -0.0670140995979309, 0.0777734544128187, conv2d_40_b, -3.378767e-05, 2.5727571e-05, 0, 0, 1, 1, -1, 0, -1, -1.3243955926895141, 0.9261298480034093, 9); 
-      void* var_125 = tensorBatchNorm(var_124, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, 0.001); 
-      void* var_126 = tensorAdd(var_125, var_117); 
-      void* var_127 = tensorRelu(var_126); 
-      void* var_128 = ConvLayer_PROMISE(var_127, 0.0, 6.8198375024796505, conv2d_41_w, -0.0710306192561984, 0.10828035335987954, conv2d_41_b, -1.3110192e-05, 1.5449377e-05, 0, 0, 1, 1, -1, 0, -1, -3.2434056091308596, 5.530628140926378, 9); 
-      void* var_129 = tensorBatchNorm(var_128, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, 0.001); 
-      void* var_130 = tensorRelu(var_129); 
-      void* var_131 = ConvLayer_PROMISE(var_130, 0.0, 4.811174154282, conv2d_42_w, -0.056100725468248125, 0.06774817473441476, conv2d_42_b, -2.7899796e-05, 3.0695155e-05, 1, 1, 1, 1, -1, 0, -1, -3.553957043647766, 3.0058912243844595, 9); 
-      void* var_132 = tensorBatchNorm(var_131, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, 0.001); 
-      void* var_133 = tensorRelu(var_132); 
-      void* var_134 = ConvLayer_PROMISE(var_133, 0.0, 6.503577950477883, conv2d_43_w, -0.06820484285801648, 0.0836490480080298, conv2d_43_b, -2.2592936e-05, 2.3876093e-05, 0, 0, 1, 1, -1, 0, -1, -2.760284422159195, 1.1501846584081763, 9); 
-      void* var_135 = tensorBatchNorm(var_134, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, 0.001); 
-      void* var_136 = tensorAdd(var_135, var_127); 
-      void* var_137 = tensorRelu(var_136); 
-      void* var_138 = ConvLayer_PROMISE(var_137, 0.0, 7.423539982796591, conv2d_44_w, -0.06768814034759998, 0.07900290366262253, conv2d_44_b, -1.0954906e-05, 1.2313803e-05, 0, 0, 2, 2, -1, 0, -1, -3.8250768241882325, 3.133637444972998, 9); 
-      void* var_139 = tensorBatchNorm(var_138, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, 0.001); 
-      void* var_140 = tensorRelu(var_139); 
-      void* var_141 = ConvLayer_PROMISE(var_140, 0.0, 3.234270730257073, conv2d_45_w, -0.04219715926796198, 0.04603923132643117, conv2d_45_b, -1.9525614e-05, 2.6300824e-05, 1, 1, 1, 1, -1, 0, -1, -3.2753402066230777, 1.8960905054807824, 9); 
-      void* var_142 = tensorBatchNorm(var_141, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, 0.001); 
-      void* var_143 = tensorRelu(var_142); 
-      void* var_144 = ConvLayer_PROMISE(var_143, 0.0, 2.675833512783051, conv2d_46_w, -0.051137199997901915, 0.07428906522691328, conv2d_46_b, -2.6416203e-05, 3.079251e-05, 0, 0, 1, 1, -1, 0, -1, -0.6374539139270782, 0.6678488029241574, 9); 
-      void* var_145 = tensorBatchNorm(var_144, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, 0.001); 
-      void* var_146 = ConvLayer_PROMISE(var_137, 0.0, 7.423539982796591, conv2d_47_w, -0.047168924897909165, 0.06949675244092963, conv2d_47_b, -1.2322937e-05, 2.1868867e-05, 0, 0, 2, 2, -1, 0, -1, -1.8896190267801285, 2.387520755291127, 9); 
-      void* var_147 = tensorBatchNorm(var_146, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, 0.001); 
-      void* var_148 = tensorAdd(var_145, var_147); 
-      void* var_149 = tensorRelu(var_148); 
-      void* var_150 = ConvLayer_PROMISE(var_149, 0.0, 12.392736603737378, conv2d_48_w, -0.04417608780786395, 0.06200448917225007, conv2d_48_b, -6.6323187e-06, 7.1494946e-06, 0, 0, 1, 1, -1, 0, -1, -9.068103209495545, 5.912482521057253, 9); 
-      void* var_151 = tensorBatchNorm(var_150, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, 0.001); 
-      void* var_152 = tensorRelu(var_151); 
-      void* var_153 = ConvLayer_PROMISE(var_152, 0.0, 2.565971518278122, conv2d_49_w, -0.036550714168697596, 0.042889032773673605, conv2d_49_b, -3.1749918e-05, 3.1403273e-05, 1, 1, 1, 1, -1, 0, -1, -2.0715825698375703, 1.4426317431927056, 9); 
-      void* var_154 = tensorBatchNorm(var_153, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, 0.001); 
-      void* var_155 = tensorRelu(var_154); 
-      void* var_156 = ConvLayer_PROMISE(var_155, 0.0, 2.2121606218814973, conv2d_50_w, -0.04563436089083552, 0.07235725801438761, conv2d_50_b, -5.138708e-05, 5.6959605e-05, 0, 0, 1, 1, -1, 0, -1, -0.5048498404622078, 0.4972966857850613, 9); 
-      void* var_157 = tensorBatchNorm(var_156, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, 0.001); 
-      void* var_158 = tensorAdd(var_157, var_149); 
-      void* var_159 = tensorRelu(var_158); 
-      void* var_160 = ConvLayer_PROMISE(var_159, 0.0, 12.996321228027455, conv2d_51_w, -0.051894455961883065, 0.07700131461024579, conv2d_51_b, -8.893526e-06, 7.6235174e-06, 0, 0, 1, 1, -1, 0, -1, -7.534810958862305, 7.1688279371266015, 9); 
-      void* var_161 = tensorBatchNorm(var_160, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, 0.001); 
-      void* var_162 = tensorRelu(var_161); 
-      void* var_163 = ConvLayer_PROMISE(var_162, 0.0, 2.806837086677553, conv2d_52_w, -0.032556386385113004, 0.038920990321785316, conv2d_52_b, -3.1544037e-05, 4.5056524e-05, 1, 1, 1, 1, -1, 0, -1, -1.6795331789255141, 0.9551341712474886, 9); 
-      void* var_164 = tensorBatchNorm(var_163, batch_normalization_52_gamma, batch_normalization_52_beta, batch_normalization_52_mean, batch_normalization_52_variance, 0.001); 
-      void* var_165 = tensorRelu(var_164); 
-      void* var_166 = ConvLayer_PROMISE(var_165, 0.0, 2.7935527668000724, conv2d_53_w, -0.04313115822151303, 0.0774340439587877, conv2d_53_b, -2.8713988e-05, 4.1641888e-05, 0, 0, 1, 1, -1, 0, -1, -0.5173906384706497, 0.5710835611820362, 9); 
-      void* var_167 = tensorBatchNorm(var_166, batch_normalization_53_gamma, batch_normalization_53_beta, batch_normalization_53_mean, batch_normalization_53_variance, 0.001); 
-      void* var_168 = tensorAdd(var_167, var_159); 
-      void* var_169 = tensorRelu(var_168); 
-      void* var_170 = tensorPooling(var_169,1,7,7,0,0,7,7); 
-      void* var_171 = FCLayer_PROMISE(var_170, 0.0, 5.305631495475859, dense_1_w, -0.09220413094758988, 0.24919447432458666, dense_1_b, -0.024729362, 0.028545722, -1, -6.579668023586273, 7.794472872257277, 9); 
-      void* var_172 = tensorSoftmax(var_171); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_172); 
-      final_accuracy += accuracy;
-
-      dumpAccuracyNorms();
-
-      
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();  
-  }
-
-  
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_promise.cc
deleted file mode 100644
index 8355c78cb553926759201fd070ced79e6a59f0b9..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet50_imagenet_promise.cc
+++ /dev/null
@@ -1,874 +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.h" 
-#include "utils.h" 
-
-
-int main(){ 
-
-  llvm_hpvm_initTensorRt(1); 
-
-  int total_runs = 1; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    startMemTracking(); 
-
-    int test_input_size = 2000; 
-    int batch_size = 100; 
-    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("/shared/hsharif3/resnet50_imagenet/"); 
-      std::string input_path =  dir_prefix + std::string("test_input.bin"); 
-      std::string labels_path =  dir_prefix + std::string("test_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(), 0,64,3,7,7); 
-      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(), 0,1,64,1,1); 
-      std::string batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-      void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-      void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-      void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-      void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,64,1,1); 
-      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(), 0,64,64,1,1); 
-      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 batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-      void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-      void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-      void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-      void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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 batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-      void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-      void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-      void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-      void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_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(), 0,256,64,1,1); 
-      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(), 0,1,256,1,1); 
-      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(), 0,256,64,1,1); 
-      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(), 0,1,256,1,1); 
-      std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-      void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-      void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-      void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-      void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-      void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-      void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-      void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-      void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,256,1,1); 
-      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(), 0,64,256,1,1); 
-      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(), 0,1,64,1,1); 
-      std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-      void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-      void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-      void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-      void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-      void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-      void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-      void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-      void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-      void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-      void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-      void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,256,64,1,1); 
-      std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-      void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-      void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-      void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-      void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-      void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-      void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,64,256,1,1); 
-      std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-      void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-      void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-      void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-      void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-      void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-      void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,64,64,3,3); 
-      std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-      void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-      void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-      void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-      void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,64,1,1); 
-      std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-      void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,64,1,1); 
-      std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-      void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,256,64,1,1); 
-      std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-      void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-      void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-      void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-      void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-      void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-      void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,128,256,1,1); 
-      std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-      void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-      void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-      void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-      void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-      void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-      void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,128,128,3,3); 
-      std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-      void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-      void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-      void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-      void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-      void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-      void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,512,128,1,1); 
-      std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-      void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-      void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,512,256,1,1); 
-      std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-      void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-      void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-      void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-      void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-      void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-      void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-      void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-      void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-      void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-      void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,128,512,1,1); 
-      std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-      void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-      void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-      void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-      void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-      void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-      void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,128,128,3,3); 
-      std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-      void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-      void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-      void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-      void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-      void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-      void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,512,128,1,1); 
-      std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-      void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-      void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-      void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-      void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-      void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-      void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,128,512,1,1); 
-      std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-      void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-      void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-      void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-      void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-      void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-      void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,128,128,3,3); 
-      std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-      void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-      void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-      void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-      void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-      void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-      void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,512,128,1,1); 
-      std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-      void* conv2d_21_b =  readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-      void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-      void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-      void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-      void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_22_w_path =  dir_prefix + std::string("conv2d_22_w.bin"); 
-      void* conv2d_22_w =  readTrainedWeights(conv2d_22_w_path.c_str(), 0,128,512,1,1); 
-      std::string conv2d_22_b_path =  dir_prefix + std::string("conv2d_22_b.bin"); 
-      void* conv2d_22_b =  readTrainedWeights(conv2d_22_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-      void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-      void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-      void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-      void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_23_w_path =  dir_prefix + std::string("conv2d_23_w.bin"); 
-      void* conv2d_23_w =  readTrainedWeights(conv2d_23_w_path.c_str(), 0,128,128,3,3); 
-      std::string conv2d_23_b_path =  dir_prefix + std::string("conv2d_23_b.bin"); 
-      void* conv2d_23_b =  readTrainedWeights(conv2d_23_b_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-      void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-      void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-      void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,128,1,1); 
-      std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-      void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,128,1,1); 
-      std::string conv2d_24_w_path =  dir_prefix + std::string("conv2d_24_w.bin"); 
-      void* conv2d_24_w =  readTrainedWeights(conv2d_24_w_path.c_str(), 0,512,128,1,1); 
-      std::string conv2d_24_b_path =  dir_prefix + std::string("conv2d_24_b.bin"); 
-      void* conv2d_24_b =  readTrainedWeights(conv2d_24_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-      void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-      void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-      void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-      void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_25_w_path =  dir_prefix + std::string("conv2d_25_w.bin"); 
-      void* conv2d_25_w =  readTrainedWeights(conv2d_25_w_path.c_str(), 0,256,512,1,1); 
-      std::string conv2d_25_b_path =  dir_prefix + std::string("conv2d_25_b.bin"); 
-      void* conv2d_25_b =  readTrainedWeights(conv2d_25_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-      void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-      void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-      void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-      void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_26_w_path =  dir_prefix + std::string("conv2d_26_w.bin"); 
-      void* conv2d_26_w =  readTrainedWeights(conv2d_26_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_26_b_path =  dir_prefix + std::string("conv2d_26_b.bin"); 
-      void* conv2d_26_b =  readTrainedWeights(conv2d_26_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-      void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-      void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-      void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-      void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_27_w_path =  dir_prefix + std::string("conv2d_27_w.bin"); 
-      void* conv2d_27_w =  readTrainedWeights(conv2d_27_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_27_b_path =  dir_prefix + std::string("conv2d_27_b.bin"); 
-      void* conv2d_27_b =  readTrainedWeights(conv2d_27_b_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_28_w_path =  dir_prefix + std::string("conv2d_28_w.bin"); 
-      void* conv2d_28_w =  readTrainedWeights(conv2d_28_w_path.c_str(), 0,1024,512,1,1); 
-      std::string conv2d_28_b_path =  dir_prefix + std::string("conv2d_28_b.bin"); 
-      void* conv2d_28_b =  readTrainedWeights(conv2d_28_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-      void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-      void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-      void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-      void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_28_gamma_path =  dir_prefix + std::string("batch_normalization_28_gamma.bin"); 
-      void* batch_normalization_28_gamma =  readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_28_beta_path =  dir_prefix + std::string("batch_normalization_28_beta.bin"); 
-      void* batch_normalization_28_beta =  readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_28_mean_path =  dir_prefix + std::string("batch_normalization_28_mean.bin"); 
-      void* batch_normalization_28_mean =  readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_28_variance_path =  dir_prefix + std::string("batch_normalization_28_variance.bin"); 
-      void* batch_normalization_28_variance =  readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_29_w_path =  dir_prefix + std::string("conv2d_29_w.bin"); 
-      void* conv2d_29_w =  readTrainedWeights(conv2d_29_w_path.c_str(), 0,256,1024,1,1); 
-      std::string conv2d_29_b_path =  dir_prefix + std::string("conv2d_29_b.bin"); 
-      void* conv2d_29_b =  readTrainedWeights(conv2d_29_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_29_gamma_path =  dir_prefix + std::string("batch_normalization_29_gamma.bin"); 
-      void* batch_normalization_29_gamma =  readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_29_beta_path =  dir_prefix + std::string("batch_normalization_29_beta.bin"); 
-      void* batch_normalization_29_beta =  readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_29_mean_path =  dir_prefix + std::string("batch_normalization_29_mean.bin"); 
-      void* batch_normalization_29_mean =  readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_29_variance_path =  dir_prefix + std::string("batch_normalization_29_variance.bin"); 
-      void* batch_normalization_29_variance =  readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_30_w_path =  dir_prefix + std::string("conv2d_30_w.bin"); 
-      void* conv2d_30_w =  readTrainedWeights(conv2d_30_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_30_b_path =  dir_prefix + std::string("conv2d_30_b.bin"); 
-      void* conv2d_30_b =  readTrainedWeights(conv2d_30_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_30_gamma_path =  dir_prefix + std::string("batch_normalization_30_gamma.bin"); 
-      void* batch_normalization_30_gamma =  readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_30_beta_path =  dir_prefix + std::string("batch_normalization_30_beta.bin"); 
-      void* batch_normalization_30_beta =  readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_30_mean_path =  dir_prefix + std::string("batch_normalization_30_mean.bin"); 
-      void* batch_normalization_30_mean =  readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_30_variance_path =  dir_prefix + std::string("batch_normalization_30_variance.bin"); 
-      void* batch_normalization_30_variance =  readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_31_w_path =  dir_prefix + std::string("conv2d_31_w.bin"); 
-      void* conv2d_31_w =  readTrainedWeights(conv2d_31_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_31_b_path =  dir_prefix + std::string("conv2d_31_b.bin"); 
-      void* conv2d_31_b =  readTrainedWeights(conv2d_31_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_31_gamma_path =  dir_prefix + std::string("batch_normalization_31_gamma.bin"); 
-      void* batch_normalization_31_gamma =  readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_31_beta_path =  dir_prefix + std::string("batch_normalization_31_beta.bin"); 
-      void* batch_normalization_31_beta =  readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_31_mean_path =  dir_prefix + std::string("batch_normalization_31_mean.bin"); 
-      void* batch_normalization_31_mean =  readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_31_variance_path =  dir_prefix + std::string("batch_normalization_31_variance.bin"); 
-      void* batch_normalization_31_variance =  readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_32_w_path =  dir_prefix + std::string("conv2d_32_w.bin"); 
-      void* conv2d_32_w =  readTrainedWeights(conv2d_32_w_path.c_str(), 0,256,1024,1,1); 
-      std::string conv2d_32_b_path =  dir_prefix + std::string("conv2d_32_b.bin"); 
-      void* conv2d_32_b =  readTrainedWeights(conv2d_32_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_32_gamma_path =  dir_prefix + std::string("batch_normalization_32_gamma.bin"); 
-      void* batch_normalization_32_gamma =  readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_32_beta_path =  dir_prefix + std::string("batch_normalization_32_beta.bin"); 
-      void* batch_normalization_32_beta =  readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_32_mean_path =  dir_prefix + std::string("batch_normalization_32_mean.bin"); 
-      void* batch_normalization_32_mean =  readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_32_variance_path =  dir_prefix + std::string("batch_normalization_32_variance.bin"); 
-      void* batch_normalization_32_variance =  readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_33_w_path =  dir_prefix + std::string("conv2d_33_w.bin"); 
-      void* conv2d_33_w =  readTrainedWeights(conv2d_33_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_33_b_path =  dir_prefix + std::string("conv2d_33_b.bin"); 
-      void* conv2d_33_b =  readTrainedWeights(conv2d_33_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_33_gamma_path =  dir_prefix + std::string("batch_normalization_33_gamma.bin"); 
-      void* batch_normalization_33_gamma =  readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_33_beta_path =  dir_prefix + std::string("batch_normalization_33_beta.bin"); 
-      void* batch_normalization_33_beta =  readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_33_mean_path =  dir_prefix + std::string("batch_normalization_33_mean.bin"); 
-      void* batch_normalization_33_mean =  readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_33_variance_path =  dir_prefix + std::string("batch_normalization_33_variance.bin"); 
-      void* batch_normalization_33_variance =  readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_34_w_path =  dir_prefix + std::string("conv2d_34_w.bin"); 
-      void* conv2d_34_w =  readTrainedWeights(conv2d_34_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_34_b_path =  dir_prefix + std::string("conv2d_34_b.bin"); 
-      void* conv2d_34_b =  readTrainedWeights(conv2d_34_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_34_gamma_path =  dir_prefix + std::string("batch_normalization_34_gamma.bin"); 
-      void* batch_normalization_34_gamma =  readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_34_beta_path =  dir_prefix + std::string("batch_normalization_34_beta.bin"); 
-      void* batch_normalization_34_beta =  readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_34_mean_path =  dir_prefix + std::string("batch_normalization_34_mean.bin"); 
-      void* batch_normalization_34_mean =  readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_34_variance_path =  dir_prefix + std::string("batch_normalization_34_variance.bin"); 
-      void* batch_normalization_34_variance =  readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_35_w_path =  dir_prefix + std::string("conv2d_35_w.bin"); 
-      void* conv2d_35_w =  readTrainedWeights(conv2d_35_w_path.c_str(), 0,256,1024,1,1); 
-      std::string conv2d_35_b_path =  dir_prefix + std::string("conv2d_35_b.bin"); 
-      void* conv2d_35_b =  readTrainedWeights(conv2d_35_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_35_gamma_path =  dir_prefix + std::string("batch_normalization_35_gamma.bin"); 
-      void* batch_normalization_35_gamma =  readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_35_beta_path =  dir_prefix + std::string("batch_normalization_35_beta.bin"); 
-      void* batch_normalization_35_beta =  readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_35_mean_path =  dir_prefix + std::string("batch_normalization_35_mean.bin"); 
-      void* batch_normalization_35_mean =  readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_35_variance_path =  dir_prefix + std::string("batch_normalization_35_variance.bin"); 
-      void* batch_normalization_35_variance =  readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_36_w_path =  dir_prefix + std::string("conv2d_36_w.bin"); 
-      void* conv2d_36_w =  readTrainedWeights(conv2d_36_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_36_b_path =  dir_prefix + std::string("conv2d_36_b.bin"); 
-      void* conv2d_36_b =  readTrainedWeights(conv2d_36_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_36_gamma_path =  dir_prefix + std::string("batch_normalization_36_gamma.bin"); 
-      void* batch_normalization_36_gamma =  readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_36_beta_path =  dir_prefix + std::string("batch_normalization_36_beta.bin"); 
-      void* batch_normalization_36_beta =  readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_36_mean_path =  dir_prefix + std::string("batch_normalization_36_mean.bin"); 
-      void* batch_normalization_36_mean =  readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_36_variance_path =  dir_prefix + std::string("batch_normalization_36_variance.bin"); 
-      void* batch_normalization_36_variance =  readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_37_w_path =  dir_prefix + std::string("conv2d_37_w.bin"); 
-      void* conv2d_37_w =  readTrainedWeights(conv2d_37_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_37_b_path =  dir_prefix + std::string("conv2d_37_b.bin"); 
-      void* conv2d_37_b =  readTrainedWeights(conv2d_37_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_37_gamma_path =  dir_prefix + std::string("batch_normalization_37_gamma.bin"); 
-      void* batch_normalization_37_gamma =  readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_37_beta_path =  dir_prefix + std::string("batch_normalization_37_beta.bin"); 
-      void* batch_normalization_37_beta =  readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_37_mean_path =  dir_prefix + std::string("batch_normalization_37_mean.bin"); 
-      void* batch_normalization_37_mean =  readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_37_variance_path =  dir_prefix + std::string("batch_normalization_37_variance.bin"); 
-      void* batch_normalization_37_variance =  readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_38_w_path =  dir_prefix + std::string("conv2d_38_w.bin"); 
-      void* conv2d_38_w =  readTrainedWeights(conv2d_38_w_path.c_str(), 0,256,1024,1,1); 
-      std::string conv2d_38_b_path =  dir_prefix + std::string("conv2d_38_b.bin"); 
-      void* conv2d_38_b =  readTrainedWeights(conv2d_38_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_38_gamma_path =  dir_prefix + std::string("batch_normalization_38_gamma.bin"); 
-      void* batch_normalization_38_gamma =  readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_38_beta_path =  dir_prefix + std::string("batch_normalization_38_beta.bin"); 
-      void* batch_normalization_38_beta =  readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_38_mean_path =  dir_prefix + std::string("batch_normalization_38_mean.bin"); 
-      void* batch_normalization_38_mean =  readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_38_variance_path =  dir_prefix + std::string("batch_normalization_38_variance.bin"); 
-      void* batch_normalization_38_variance =  readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_39_w_path =  dir_prefix + std::string("conv2d_39_w.bin"); 
-      void* conv2d_39_w =  readTrainedWeights(conv2d_39_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_39_b_path =  dir_prefix + std::string("conv2d_39_b.bin"); 
-      void* conv2d_39_b =  readTrainedWeights(conv2d_39_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_39_gamma_path =  dir_prefix + std::string("batch_normalization_39_gamma.bin"); 
-      void* batch_normalization_39_gamma =  readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_39_beta_path =  dir_prefix + std::string("batch_normalization_39_beta.bin"); 
-      void* batch_normalization_39_beta =  readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_39_mean_path =  dir_prefix + std::string("batch_normalization_39_mean.bin"); 
-      void* batch_normalization_39_mean =  readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_39_variance_path =  dir_prefix + std::string("batch_normalization_39_variance.bin"); 
-      void* batch_normalization_39_variance =  readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_40_w_path =  dir_prefix + std::string("conv2d_40_w.bin"); 
-      void* conv2d_40_w =  readTrainedWeights(conv2d_40_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_40_b_path =  dir_prefix + std::string("conv2d_40_b.bin"); 
-      void* conv2d_40_b =  readTrainedWeights(conv2d_40_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_40_gamma_path =  dir_prefix + std::string("batch_normalization_40_gamma.bin"); 
-      void* batch_normalization_40_gamma =  readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_40_beta_path =  dir_prefix + std::string("batch_normalization_40_beta.bin"); 
-      void* batch_normalization_40_beta =  readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_40_mean_path =  dir_prefix + std::string("batch_normalization_40_mean.bin"); 
-      void* batch_normalization_40_mean =  readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_40_variance_path =  dir_prefix + std::string("batch_normalization_40_variance.bin"); 
-      void* batch_normalization_40_variance =  readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_41_w_path =  dir_prefix + std::string("conv2d_41_w.bin"); 
-      void* conv2d_41_w =  readTrainedWeights(conv2d_41_w_path.c_str(), 0,256,1024,1,1); 
-      std::string conv2d_41_b_path =  dir_prefix + std::string("conv2d_41_b.bin"); 
-      void* conv2d_41_b =  readTrainedWeights(conv2d_41_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_41_gamma_path =  dir_prefix + std::string("batch_normalization_41_gamma.bin"); 
-      void* batch_normalization_41_gamma =  readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_41_beta_path =  dir_prefix + std::string("batch_normalization_41_beta.bin"); 
-      void* batch_normalization_41_beta =  readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_41_mean_path =  dir_prefix + std::string("batch_normalization_41_mean.bin"); 
-      void* batch_normalization_41_mean =  readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_41_variance_path =  dir_prefix + std::string("batch_normalization_41_variance.bin"); 
-      void* batch_normalization_41_variance =  readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_42_w_path =  dir_prefix + std::string("conv2d_42_w.bin"); 
-      void* conv2d_42_w =  readTrainedWeights(conv2d_42_w_path.c_str(), 0,256,256,3,3); 
-      std::string conv2d_42_b_path =  dir_prefix + std::string("conv2d_42_b.bin"); 
-      void* conv2d_42_b =  readTrainedWeights(conv2d_42_b_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_42_gamma_path =  dir_prefix + std::string("batch_normalization_42_gamma.bin"); 
-      void* batch_normalization_42_gamma =  readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_42_beta_path =  dir_prefix + std::string("batch_normalization_42_beta.bin"); 
-      void* batch_normalization_42_beta =  readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_42_mean_path =  dir_prefix + std::string("batch_normalization_42_mean.bin"); 
-      void* batch_normalization_42_mean =  readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,256,1,1); 
-      std::string batch_normalization_42_variance_path =  dir_prefix + std::string("batch_normalization_42_variance.bin"); 
-      void* batch_normalization_42_variance =  readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,256,1,1); 
-      std::string conv2d_43_w_path =  dir_prefix + std::string("conv2d_43_w.bin"); 
-      void* conv2d_43_w =  readTrainedWeights(conv2d_43_w_path.c_str(), 0,1024,256,1,1); 
-      std::string conv2d_43_b_path =  dir_prefix + std::string("conv2d_43_b.bin"); 
-      void* conv2d_43_b =  readTrainedWeights(conv2d_43_b_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_43_gamma_path =  dir_prefix + std::string("batch_normalization_43_gamma.bin"); 
-      void* batch_normalization_43_gamma =  readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_43_beta_path =  dir_prefix + std::string("batch_normalization_43_beta.bin"); 
-      void* batch_normalization_43_beta =  readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_43_mean_path =  dir_prefix + std::string("batch_normalization_43_mean.bin"); 
-      void* batch_normalization_43_mean =  readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,1024,1,1); 
-      std::string batch_normalization_43_variance_path =  dir_prefix + std::string("batch_normalization_43_variance.bin"); 
-      void* batch_normalization_43_variance =  readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,1024,1,1); 
-      std::string conv2d_44_w_path =  dir_prefix + std::string("conv2d_44_w.bin"); 
-      void* conv2d_44_w =  readTrainedWeights(conv2d_44_w_path.c_str(), 0,512,1024,1,1); 
-      std::string conv2d_44_b_path =  dir_prefix + std::string("conv2d_44_b.bin"); 
-      void* conv2d_44_b =  readTrainedWeights(conv2d_44_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_44_gamma_path =  dir_prefix + std::string("batch_normalization_44_gamma.bin"); 
-      void* batch_normalization_44_gamma =  readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_44_beta_path =  dir_prefix + std::string("batch_normalization_44_beta.bin"); 
-      void* batch_normalization_44_beta =  readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_44_mean_path =  dir_prefix + std::string("batch_normalization_44_mean.bin"); 
-      void* batch_normalization_44_mean =  readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_44_variance_path =  dir_prefix + std::string("batch_normalization_44_variance.bin"); 
-      void* batch_normalization_44_variance =  readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_45_w_path =  dir_prefix + std::string("conv2d_45_w.bin"); 
-      void* conv2d_45_w =  readTrainedWeights(conv2d_45_w_path.c_str(), 0,512,512,3,3); 
-      std::string conv2d_45_b_path =  dir_prefix + std::string("conv2d_45_b.bin"); 
-      void* conv2d_45_b =  readTrainedWeights(conv2d_45_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_45_gamma_path =  dir_prefix + std::string("batch_normalization_45_gamma.bin"); 
-      void* batch_normalization_45_gamma =  readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_45_beta_path =  dir_prefix + std::string("batch_normalization_45_beta.bin"); 
-      void* batch_normalization_45_beta =  readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_45_mean_path =  dir_prefix + std::string("batch_normalization_45_mean.bin"); 
-      void* batch_normalization_45_mean =  readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_45_variance_path =  dir_prefix + std::string("batch_normalization_45_variance.bin"); 
-      void* batch_normalization_45_variance =  readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_46_w_path =  dir_prefix + std::string("conv2d_46_w.bin"); 
-      void* conv2d_46_w =  readTrainedWeights(conv2d_46_w_path.c_str(), 0,2048,512,1,1); 
-      std::string conv2d_46_b_path =  dir_prefix + std::string("conv2d_46_b.bin"); 
-      void* conv2d_46_b =  readTrainedWeights(conv2d_46_b_path.c_str(), 0,1,2048,1,1); 
-      std::string conv2d_47_w_path =  dir_prefix + std::string("conv2d_47_w.bin"); 
-      void* conv2d_47_w =  readTrainedWeights(conv2d_47_w_path.c_str(), 0,2048,1024,1,1); 
-      std::string conv2d_47_b_path =  dir_prefix + std::string("conv2d_47_b.bin"); 
-      void* conv2d_47_b =  readTrainedWeights(conv2d_47_b_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_46_gamma_path =  dir_prefix + std::string("batch_normalization_46_gamma.bin"); 
-      void* batch_normalization_46_gamma =  readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_46_beta_path =  dir_prefix + std::string("batch_normalization_46_beta.bin"); 
-      void* batch_normalization_46_beta =  readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_46_mean_path =  dir_prefix + std::string("batch_normalization_46_mean.bin"); 
-      void* batch_normalization_46_mean =  readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_46_variance_path =  dir_prefix + std::string("batch_normalization_46_variance.bin"); 
-      void* batch_normalization_46_variance =  readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_47_gamma_path =  dir_prefix + std::string("batch_normalization_47_gamma.bin"); 
-      void* batch_normalization_47_gamma =  readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_47_beta_path =  dir_prefix + std::string("batch_normalization_47_beta.bin"); 
-      void* batch_normalization_47_beta =  readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_47_mean_path =  dir_prefix + std::string("batch_normalization_47_mean.bin"); 
-      void* batch_normalization_47_mean =  readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_47_variance_path =  dir_prefix + std::string("batch_normalization_47_variance.bin"); 
-      void* batch_normalization_47_variance =  readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,2048,1,1); 
-      std::string conv2d_48_w_path =  dir_prefix + std::string("conv2d_48_w.bin"); 
-      void* conv2d_48_w =  readTrainedWeights(conv2d_48_w_path.c_str(), 0,512,2048,1,1); 
-      std::string conv2d_48_b_path =  dir_prefix + std::string("conv2d_48_b.bin"); 
-      void* conv2d_48_b =  readTrainedWeights(conv2d_48_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_48_gamma_path =  dir_prefix + std::string("batch_normalization_48_gamma.bin"); 
-      void* batch_normalization_48_gamma =  readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_48_beta_path =  dir_prefix + std::string("batch_normalization_48_beta.bin"); 
-      void* batch_normalization_48_beta =  readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_48_mean_path =  dir_prefix + std::string("batch_normalization_48_mean.bin"); 
-      void* batch_normalization_48_mean =  readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_48_variance_path =  dir_prefix + std::string("batch_normalization_48_variance.bin"); 
-      void* batch_normalization_48_variance =  readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_49_w_path =  dir_prefix + std::string("conv2d_49_w.bin"); 
-      void* conv2d_49_w =  readTrainedWeights(conv2d_49_w_path.c_str(), 0,512,512,3,3); 
-      std::string conv2d_49_b_path =  dir_prefix + std::string("conv2d_49_b.bin"); 
-      void* conv2d_49_b =  readTrainedWeights(conv2d_49_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_49_gamma_path =  dir_prefix + std::string("batch_normalization_49_gamma.bin"); 
-      void* batch_normalization_49_gamma =  readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_49_beta_path =  dir_prefix + std::string("batch_normalization_49_beta.bin"); 
-      void* batch_normalization_49_beta =  readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_49_mean_path =  dir_prefix + std::string("batch_normalization_49_mean.bin"); 
-      void* batch_normalization_49_mean =  readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_49_variance_path =  dir_prefix + std::string("batch_normalization_49_variance.bin"); 
-      void* batch_normalization_49_variance =  readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_50_w_path =  dir_prefix + std::string("conv2d_50_w.bin"); 
-      void* conv2d_50_w =  readTrainedWeights(conv2d_50_w_path.c_str(), 0,2048,512,1,1); 
-      std::string conv2d_50_b_path =  dir_prefix + std::string("conv2d_50_b.bin"); 
-      void* conv2d_50_b =  readTrainedWeights(conv2d_50_b_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_50_gamma_path =  dir_prefix + std::string("batch_normalization_50_gamma.bin"); 
-      void* batch_normalization_50_gamma =  readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_50_beta_path =  dir_prefix + std::string("batch_normalization_50_beta.bin"); 
-      void* batch_normalization_50_beta =  readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_50_mean_path =  dir_prefix + std::string("batch_normalization_50_mean.bin"); 
-      void* batch_normalization_50_mean =  readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_50_variance_path =  dir_prefix + std::string("batch_normalization_50_variance.bin"); 
-      void* batch_normalization_50_variance =  readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,2048,1,1); 
-      std::string conv2d_51_w_path =  dir_prefix + std::string("conv2d_51_w.bin"); 
-      void* conv2d_51_w =  readTrainedWeights(conv2d_51_w_path.c_str(), 0,512,2048,1,1); 
-      std::string conv2d_51_b_path =  dir_prefix + std::string("conv2d_51_b.bin"); 
-      void* conv2d_51_b =  readTrainedWeights(conv2d_51_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_51_gamma_path =  dir_prefix + std::string("batch_normalization_51_gamma.bin"); 
-      void* batch_normalization_51_gamma =  readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_51_beta_path =  dir_prefix + std::string("batch_normalization_51_beta.bin"); 
-      void* batch_normalization_51_beta =  readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_51_mean_path =  dir_prefix + std::string("batch_normalization_51_mean.bin"); 
-      void* batch_normalization_51_mean =  readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_51_variance_path =  dir_prefix + std::string("batch_normalization_51_variance.bin"); 
-      void* batch_normalization_51_variance =  readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_52_w_path =  dir_prefix + std::string("conv2d_52_w.bin"); 
-      void* conv2d_52_w =  readTrainedWeights(conv2d_52_w_path.c_str(), 0,512,512,3,3); 
-      std::string conv2d_52_b_path =  dir_prefix + std::string("conv2d_52_b.bin"); 
-      void* conv2d_52_b =  readTrainedWeights(conv2d_52_b_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_52_gamma_path =  dir_prefix + std::string("batch_normalization_52_gamma.bin"); 
-      void* batch_normalization_52_gamma =  readTrainedWeights(batch_normalization_52_gamma_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_52_beta_path =  dir_prefix + std::string("batch_normalization_52_beta.bin"); 
-      void* batch_normalization_52_beta =  readTrainedWeights(batch_normalization_52_beta_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_52_mean_path =  dir_prefix + std::string("batch_normalization_52_mean.bin"); 
-      void* batch_normalization_52_mean =  readTrainedWeights(batch_normalization_52_mean_path.c_str(), 0,1,512,1,1); 
-      std::string batch_normalization_52_variance_path =  dir_prefix + std::string("batch_normalization_52_variance.bin"); 
-      void* batch_normalization_52_variance =  readTrainedWeights(batch_normalization_52_variance_path.c_str(), 0,1,512,1,1); 
-      std::string conv2d_53_w_path =  dir_prefix + std::string("conv2d_53_w.bin"); 
-      void* conv2d_53_w =  readTrainedWeights(conv2d_53_w_path.c_str(), 0,2048,512,1,1); 
-      std::string conv2d_53_b_path =  dir_prefix + std::string("conv2d_53_b.bin"); 
-      void* conv2d_53_b =  readTrainedWeights(conv2d_53_b_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_53_gamma_path =  dir_prefix + std::string("batch_normalization_53_gamma.bin"); 
-      void* batch_normalization_53_gamma =  readTrainedWeights(batch_normalization_53_gamma_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_53_beta_path =  dir_prefix + std::string("batch_normalization_53_beta.bin"); 
-      void* batch_normalization_53_beta =  readTrainedWeights(batch_normalization_53_beta_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_53_mean_path =  dir_prefix + std::string("batch_normalization_53_mean.bin"); 
-      void* batch_normalization_53_mean =  readTrainedWeights(batch_normalization_53_mean_path.c_str(), 0,1,2048,1,1); 
-      std::string batch_normalization_53_variance_path =  dir_prefix + std::string("batch_normalization_53_variance.bin"); 
-      void* batch_normalization_53_variance =  readTrainedWeights(batch_normalization_53_variance_path.c_str(), 0,1,2048,1,1); 
-      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(), 0,1,1,2048,1000); 
-      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(), 0,1,1000,1,1); 
-
-
-      int start = i * batch_size; 
-      int end = (i + 1) * batch_size; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,224,224); 
-
-      // NOTE: The pooling stride is 3*3 while it should be 2*2 -- interface itself needs fixing -- fix this manually in this case
-      void* var_0 = ConvLayer_PROMISE2(input, -123.68, 151.061, conv2d_1_w, -0.574422012090683, 0.5646807488203113, conv2d_1_b, -0.004829655, 0.014784645, 3, 3, 2, 2, 0, 3, 2, 1, 0.0, 689.7822875976562, 9); 
-      void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-      void* var_2 = ConvLayer_PROMISE(var_1, -4.952117443084717, 12.02118032741582, conv2d_2_w, -0.5448235973715783, 0.2447893574833928, conv2d_2_b, -0.0001412337, 0.00017318528, 0, 0, 1, 1, -1, 0, -1, -9.212617980003357, 8.107657526016425, 9); 
-      void* var_3 = tensorBatchNorm(var_2, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-      void* var_4 = tensorRelu(var_3); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 5.801381123542811, conv2d_3_w, -0.18028786177933215, 0.21247629988193606, conv2d_3_b, -7.8663266e-05, 0.00018541634, 1, 1, 1, 1, -1, 0, -1, -6.834556140899658, 8.541351353645396, 9); 
-      void* var_6 = tensorBatchNorm(var_5, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-      void* var_7 = tensorRelu(var_6); 
-      void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 9.866454153060971, conv2d_4_w, -0.2255178820490837, 0.2254851074665791, conv2d_4_b, -0.00017080337, 0.00021038808, 0, 0, 1, 1, -1, 0, -1, -3.595476400852203, 3.637018930196785, 9); 
-      void* var_9 = tensorBatchNorm(var_8, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-      void* var_10 = ConvLayer_PROMISE(var_1, -4.952117443084717, 12.02118032741582, conv2d_5_w, -0.43272915667295453, 0.29589187785983095, conv2d_5_b, -0.000107640364, 0.00013177324, 0, 0, 1, 1, -1, 0, -1, -7.581318395137787, 7.8835730876923265, 9); 
-      void* var_11 = tensorBatchNorm(var_10, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-      void* var_12 = tensorAdd(var_9, var_11); 
-      void* var_13 = tensorRelu(var_12); 
-      void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 5.885549548625953, conv2d_6_w, -0.17062100511789324, 0.1432653286457067, conv2d_6_b, -7.950033e-05, 0.000104833845, 0, 0, 1, 1, -1, 0, -1, -5.310503073692322, 3.8418860490322224, 9); 
-      void* var_15 = tensorBatchNorm(var_14, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-      void* var_16 = tensorRelu(var_15); 
-      void* var_17 = ConvLayer_PROMISE(var_16, 0.0, 4.006655237674757, conv2d_7_w, -0.15594010630249977, 0.15720265829563249, conv2d_7_b, -6.419372e-05, 6.503685e-05, 1, 1, 1, 1, -1, 0, -1, -3.4114532544612883, 3.075598966121696, 9); 
-      void* var_18 = tensorBatchNorm(var_17, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-      void* var_19 = tensorRelu(var_18); 
-      void* var_20 = ConvLayer_PROMISE(var_19, 0.0, 4.186545849800112, conv2d_8_w, -0.1599232355505228, 0.17352246379853484, conv2d_8_b, -8.235522e-05, 0.000105946136, 0, 0, 1, 1, -1, 0, -1, -1.5299443051815034, 1.425760628223422, 9); 
-      void* var_21 = tensorBatchNorm(var_20, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-      void* var_22 = tensorAdd(var_21, var_13); 
-      void* var_23 = tensorRelu(var_22); 
-      void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 6.36634494018557, conv2d_9_w, -0.14470596650242806, 0.14421831880510708, conv2d_9_b, -3.4270335e-05, 4.177745e-05, 0, 0, 1, 1, -1, 0, -1, -4.584994326114654, 3.8648653411866007, 9); 
-      void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-      void* var_26 = tensorRelu(var_25); 
-      void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 3.3001420612335437, conv2d_10_w, -0.12276832074671984, 0.12627632835507407, conv2d_10_b, -5.8183014e-05, 3.3546e-05, 1, 1, 1, 1, -1, 0, -1, -2.828902014493942, 3.0918669717311893, 9); 
-      void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-      void* var_29 = tensorRelu(var_28); 
-      void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 5.313344509124818, conv2d_11_w, -0.1685639199912548, 0.16309838759899448, conv2d_11_b, -5.3248757e-05, 5.70645e-05, 0, 0, 1, 1, -1, 0, -1, -1.838510752558708, 1.3678752244711045, 9); 
-      void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-      void* var_32 = tensorAdd(var_31, var_23); 
-      void* var_33 = tensorRelu(var_32); 
-      void* var_34 = ConvLayer_PROMISE(var_33, 0.0, 6.605899341106429, conv2d_12_w, -0.149728477448225, 0.13948052291572155, conv2d_12_b, -2.5221272e-05, 3.551765e-05, 0, 0, 2, 2, -1, 0, -1, -5.011460402488709, 3.915426737308551, 9); 
-      void* var_35 = tensorBatchNorm(var_34, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-      void* var_36 = tensorRelu(var_35); 
-      void* var_37 = ConvLayer_PROMISE(var_36, 0.0, 3.794741600990312, conv2d_13_w, -0.09761696971952916, 0.11394361693412249, conv2d_13_b, -3.715329e-05, 2.9298411e-05, 1, 1, 1, 1, -1, 0, -1, -5.206686987876893, 4.520638871669791, 9); 
-      void* var_38 = tensorBatchNorm(var_37, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-      void* var_39 = tensorRelu(var_38); 
-      void* var_40 = ConvLayer_PROMISE(var_39, 0.0, 3.7149479997158603, conv2d_14_w, -0.14844063371419908, 0.14925702929496953, conv2d_14_b, -6.0864673e-05, 5.4444306e-05, 0, 0, 1, 1, -1, 0, -1, -1.5011818276643754, 1.40834725618366, 9); 
-      void* var_41 = tensorBatchNorm(var_40, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-      void* var_42 = ConvLayer_PROMISE(var_33, 0.0, 6.605899341106429, conv2d_15_w, -0.1642171936035156, 0.16866817833483497, conv2d_15_b, -2.4068044e-05, 2.5504653e-05, 0, 0, 2, 2, -1, 0, -1, -4.410076716423035, 4.014970501422923, 9); 
-      void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-      void* var_44 = tensorAdd(var_41, var_43); 
-      void* var_45 = tensorRelu(var_44); 
-      void* var_46 = ConvLayer_PROMISE(var_45, 0.0, 6.518892978191488, conv2d_16_w, -0.09702376063913107, 0.1054209597408773, conv2d_16_b, -1.47610735e-05, 1.7075112e-05, 0, 0, 1, 1, -1, 0, -1, -4.87446900844574, 3.7661991298198862, 9); 
-      void* var_47 = tensorBatchNorm(var_46, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-      void* var_48 = tensorRelu(var_47); 
-      void* var_49 = ConvLayer_PROMISE(var_48, 0.0, 3.259194364786183, conv2d_17_w, -0.08665236312896013, 0.0898308474570517, conv2d_17_b, -3.9163042e-05, 4.2771928e-05, 1, 1, 1, 1, -1, 0, -1, -2.673636848211288, 2.3574042041302774, 9); 
-      void* var_50 = tensorBatchNorm(var_49, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-      void* var_51 = tensorRelu(var_50); 
-      void* var_52 = ConvLayer_PROMISE(var_51, 0.0, 3.641261647939746, conv2d_18_w, -0.12198246002197266, 0.1347003544867095, conv2d_18_b, -5.3173797e-05, 4.8076203e-05, 0, 0, 1, 1, -1, 0, -1, -1.0623184064626694, 0.916913630664359, 9); 
-      void* var_53 = tensorBatchNorm(var_52, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-      void* var_54 = tensorAdd(var_53, var_45); 
-      void* var_55 = tensorRelu(var_54); 
-      void* var_56 = ConvLayer_PROMISE(var_55, 0.0, 6.852215012073557, conv2d_19_w, -0.1122598509863019, 0.1435348897427337, conv2d_19_b, -1.20778e-05, 2.599136e-05, 0, 0, 1, 1, -1, 0, -1, -6.0281127138137816, 6.227049376964593, 9); 
-      void* var_57 = tensorBatchNorm(var_56, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-      void* var_58 = tensorRelu(var_57); 
-      void* var_59 = ConvLayer_PROMISE(var_58, 0.0, 3.397107238292711, conv2d_20_w, -0.1049889962002635, 0.1349111200869117, conv2d_20_b, -2.7412994e-05, 3.9722e-05, 1, 1, 1, 1, -1, 0, -1, -4.057081372261047, 4.329259678363884, 9); 
-      void* var_60 = tensorBatchNorm(var_59, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-      void* var_61 = tensorRelu(var_60); 
-      void* var_62 = ConvLayer_PROMISE(var_61, 0.0, 3.6484641625881262, conv2d_21_w, -0.1401274445652962, 0.12122062336653527, conv2d_21_b, -5.5854776e-05, 7.8164114e-05, 0, 0, 1, 1, -1, 0, -1, -1.626526164531708, 0.8401960272193048, 9); 
-      void* var_63 = tensorBatchNorm(var_62, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-      void* var_64 = tensorAdd(var_63, var_55); 
-      void* var_65 = tensorRelu(var_64); 
-      void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 6.820035747528095, conv2d_22_w, -0.16039140529930593, 0.18889211259782335, conv2d_22_b, -4.6078047e-05, 3.3613425e-05, 0, 0, 1, 1, -1, 0, -1, -4.6271090393066405, 4.527790556430912, 9); 
-      void* var_67 = tensorBatchNorm(var_66, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-      void* var_68 = tensorRelu(var_67); 
-      void* var_69 = ConvLayer_PROMISE(var_68, 0.0, 4.432856665611537, conv2d_23_w, -0.11397356178611517, 0.10787127982825667, conv2d_23_b, -3.6726604e-05, 2.4220695e-05, 1, 1, 1, 1, -1, 0, -1, -3.697339488506317, 3.1427979104519426, 9); 
-      void* var_70 = tensorBatchNorm(var_69, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-      void* var_71 = tensorRelu(var_70); 
-      void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 4.711423307418915, conv2d_24_w, -0.11341997660696507, 0.1437816035747536, conv2d_24_b, -2.7102393e-05, 3.091236e-05, 0, 0, 1, 1, -1, 0, -1, -1.4133628906011582, 1.2987316379547167, 9); 
-      void* var_73 = tensorBatchNorm(var_72, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-      void* var_74 = tensorAdd(var_73, var_65); 
-      void* var_75 = tensorRelu(var_74); 
-      void* var_76 = ConvLayer_PROMISE(var_75, 0.0, 7.624651549339404, conv2d_25_w, -0.10495923960208893, 0.12068889104576047, conv2d_25_b, -1.0208429e-05, 1.1486276e-05, 0, 0, 2, 2, -1, 0, -1, -3.87531214427948, 3.676609352588745, 9); 
-      void* var_77 = tensorBatchNorm(var_76, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-      void* var_78 = tensorRelu(var_77); 
-      void* var_79 = ConvLayer_PROMISE(var_78, 0.0, 4.044620439529737, conv2d_26_w, -0.07615160812437534, 0.07977425544709099, conv2d_26_b, -2.4272886e-05, 1.6434806e-05, 1, 1, 1, 1, -1, 0, -1, -6.102653044223786, 4.761939919948585, 9); 
-      void* var_80 = tensorBatchNorm(var_79, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-      void* var_81 = tensorRelu(var_80); 
-      void* var_82 = ConvLayer_PROMISE(var_81, 0.0, 3.4468260111809705, conv2d_27_w, -0.11533496034890414, 0.10714908299595141, conv2d_27_b, -3.225456e-05, 4.8422902e-05, 0, 0, 1, 1, -1, 0, -1, -1.319659793496132, 1.0189965035915467, 9); 
-      void* var_83 = tensorBatchNorm(var_82, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-      void* var_84 = ConvLayer_PROMISE(var_75, 0.0, 7.624651549339404, conv2d_28_w, -0.0966497472524643, 0.10240990699082783, conv2d_28_b, -1.4815519e-05, 1.554276e-05, 0, 0, 2, 2, -1, 0, -1, -3.9412443549633025, 3.863056869030064, 9); 
-      void* var_85 = tensorBatchNorm(var_84, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, 0.001); 
-      void* var_86 = tensorAdd(var_83, var_85); 
-      void* var_87 = tensorRelu(var_86); 
-      void* var_88 = ConvLayer_PROMISE(var_87, 0.0, 6.879177100658442, conv2d_29_w, -0.06468586190789938, 0.08113565444201333, conv2d_29_b, -7.4607115e-06, 6.926009e-06, 0, 0, 1, 1, -1, 0, -1, -7.112777866363525, 4.633408185959027, 9); 
-      void* var_89 = tensorBatchNorm(var_88, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, 0.001); 
-      void* var_90 = tensorRelu(var_89); 
-      void* var_91 = ConvLayer_PROMISE(var_90, 0.0, 3.2354076790810105, conv2d_30_w, -0.06493933162838221, 0.07104272978752861, conv2d_30_b, -1.9349398e-05, 2.0178473e-05, 1, 1, 1, 1, -1, 0, -1, -3.226332322359085, 2.5138739056587447, 9); 
-      void* var_92 = tensorBatchNorm(var_91, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, 0.001); 
-      void* var_93 = tensorRelu(var_92); 
-      void* var_94 = ConvLayer_PROMISE(var_93, 0.0, 3.003848925829006, conv2d_31_w, -0.0918996930718422, 0.08853508594632167, conv2d_31_b, -4.2279236e-05, 5.5378885e-05, 0, 0, 1, 1, -1, 0, -1, -0.9247466986179351, 0.572747143149404, 9); 
-      void* var_95 = tensorBatchNorm(var_94, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, 0.001); 
-      void* var_96 = tensorAdd(var_95, var_87); 
-      void* var_97 = tensorRelu(var_96); 
-      void* var_98 = ConvLayer_PROMISE(var_97, 0.0, 6.566591289043519, conv2d_32_w, -0.07145480328053236, 0.09098157961666606, conv2d_32_b, -1.0478255e-05, 1.4408147e-05, 0, 0, 1, 1, -1, 0, -1, -4.183038790225982, 3.5941159300804166, 9); 
-      void* var_99 = tensorBatchNorm(var_98, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, 0.001); 
-      void* var_100 = tensorRelu(var_99); 
-      void* var_101 = ConvLayer_PROMISE(var_100, 0.0, 3.0348211803436556, conv2d_33_w, -0.056237234909087414, 0.06478620118647821, conv2d_33_b, -2.2639133e-05, 2.6081116e-05, 1, 1, 1, 1, -1, 0, -1, -2.098393235206604, 1.706788736581844, 9); 
-      void* var_102 = tensorBatchNorm(var_101, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, 0.001); 
-      void* var_103 = tensorRelu(var_102); 
-      void* var_104 = ConvLayer_PROMISE(var_103, 0.0, 3.248518852949145, conv2d_34_w, -0.07141499005258084, 0.08281665176153225, conv2d_34_b, -3.221229e-05, 4.569047e-05, 0, 0, 1, 1, -1, 0, -1, -0.8273181943893433, 0.7378616912961369, 9); 
-      void* var_105 = tensorBatchNorm(var_104, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, 0.001); 
-      void* var_106 = tensorAdd(var_105, var_97); 
-      void* var_107 = tensorRelu(var_106); 
-      void* var_108 = ConvLayer_PROMISE(var_107, 0.0, 6.7038991017341765, conv2d_35_w, -0.06838216692209244, 0.09303134681284767, conv2d_35_b, -1.047402e-05, 1.0168567e-05, 0, 0, 1, 1, -1, 0, -1, -4.168091129779816, 3.5077465448380494, 9); 
-      void* var_109 = tensorBatchNorm(var_108, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, 0.001); 
-      void* var_110 = tensorRelu(var_109); 
-      void* var_111 = ConvLayer_PROMISE(var_110, 0.0, 2.8976624414922814, conv2d_36_w, -0.05521866928786039, 0.06331418491154919, conv2d_36_b, -3.86494e-05, 2.5999781e-05, 1, 1, 1, 1, -1, 0, -1, -2.182177306175232, 2.0366714165211324, 9); 
-      void* var_112 = tensorBatchNorm(var_111, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, 0.001); 
-      void* var_113 = tensorRelu(var_112); 
-      void* var_114 = ConvLayer_PROMISE(var_113, 0.0, 3.1310220296382933, conv2d_37_w, -0.07256266868114472, 0.08391195811331292, conv2d_37_b, -4.8211587e-05, 4.7546604e-05, 0, 0, 1, 1, -1, 0, -1, -1.1372777166366577, 0.5528145518899268, 9); 
-      void* var_115 = tensorBatchNorm(var_114, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, 0.001); 
-      void* var_116 = tensorAdd(var_115, var_107); 
-      void* var_117 = tensorRelu(var_116); 
-      void* var_118 = ConvLayer_PROMISE(var_117, 0.0, 6.625923678875129, conv2d_38_w, -0.06549047549813986, 0.10113389839232205, conv2d_38_b, -1.2351429e-05, 9.263066e-06, 0, 0, 1, 1, -1, 0, -1, -3.846879935503006, 3.639795066118241, 9); 
-      void* var_119 = tensorBatchNorm(var_118, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, 0.001); 
-      void* var_120 = tensorRelu(var_119); 
-      void* var_121 = ConvLayer_PROMISE(var_120, 0.0, 3.200671393632918, conv2d_39_w, -0.05184716333821415, 0.06296417640149599, conv2d_39_b, -2.4313656e-05, 3.812053e-05, 1, 1, 1, 1, -1, 0, -1, -1.9442583957910538, 1.5269825316667864, 9); 
-      void* var_122 = tensorBatchNorm(var_121, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, 0.001); 
-      void* var_123 = tensorRelu(var_122); 
-      void* var_124 = ConvLayer_PROMISE(var_123, 0.0, 4.040827783107826, conv2d_40_w, -0.0670140995979309, 0.0777734544128187, conv2d_40_b, -3.378767e-05, 2.5727571e-05, 0, 0, 1, 1, -1, 0, -1, -1.3243955926895141, 0.9261298480034093, 9); 
-      void* var_125 = tensorBatchNorm(var_124, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, 0.001); 
-      void* var_126 = tensorAdd(var_125, var_117); 
-      void* var_127 = tensorRelu(var_126); 
-      void* var_128 = ConvLayer_PROMISE(var_127, 0.0, 6.8198375024796505, conv2d_41_w, -0.0710306192561984, 0.10828035335987954, conv2d_41_b, -1.3110192e-05, 1.5449377e-05, 0, 0, 1, 1, -1, 0, -1, -3.2434056091308596, 5.530628140926378, 9); 
-      void* var_129 = tensorBatchNorm(var_128, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, 0.001); 
-      void* var_130 = tensorRelu(var_129); 
-      void* var_131 = ConvLayer_PROMISE(var_130, 0.0, 4.811174154282, conv2d_42_w, -0.056100725468248125, 0.06774817473441476, conv2d_42_b, -2.7899796e-05, 3.0695155e-05, 1, 1, 1, 1, -1, 0, -1, -3.553957043647766, 3.0058912243844595, 9); 
-      void* var_132 = tensorBatchNorm(var_131, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, 0.001); 
-      void* var_133 = tensorRelu(var_132); 
-      void* var_134 = ConvLayer_PROMISE(var_133, 0.0, 6.503577950477883, conv2d_43_w, -0.06820484285801648, 0.0836490480080298, conv2d_43_b, -2.2592936e-05, 2.3876093e-05, 0, 0, 1, 1, -1, 0, -1, -2.760284422159195, 1.1501846584081763, 9); 
-      void* var_135 = tensorBatchNorm(var_134, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, 0.001); 
-      void* var_136 = tensorAdd(var_135, var_127); 
-      void* var_137 = tensorRelu(var_136); 
-      void* var_138 = ConvLayer_PROMISE(var_137, 0.0, 7.423539982796591, conv2d_44_w, -0.06768814034759998, 0.07900290366262253, conv2d_44_b, -1.0954906e-05, 1.2313803e-05, 0, 0, 2, 2, -1, 0, -1, -3.8250768241882325, 3.133637444972998, 9); 
-      void* var_139 = tensorBatchNorm(var_138, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, 0.001); 
-      void* var_140 = tensorRelu(var_139); 
-      void* var_141 = ConvLayer_PROMISE(var_140, 0.0, 3.234270730257073, conv2d_45_w, -0.04219715926796198, 0.04603923132643117, conv2d_45_b, -1.9525614e-05, 2.6300824e-05, 1, 1, 1, 1, -1, 0, -1, -3.2753402066230777, 1.8960905054807824, 9); 
-      void* var_142 = tensorBatchNorm(var_141, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, 0.001); 
-      void* var_143 = tensorRelu(var_142); 
-      void* var_144 = ConvLayer_PROMISE(var_143, 0.0, 2.675833512783051, conv2d_46_w, -0.051137199997901915, 0.07428906522691328, conv2d_46_b, -2.6416203e-05, 3.079251e-05, 0, 0, 1, 1, -1, 0, -1, -0.6374539139270782, 0.6678488029241574, 9); 
-      void* var_145 = tensorBatchNorm(var_144, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, 0.001); 
-      void* var_146 = ConvLayer_PROMISE(var_137, 0.0, 7.423539982796591, conv2d_47_w, -0.047168924897909165, 0.06949675244092963, conv2d_47_b, -1.2322937e-05, 2.1868867e-05, 0, 0, 2, 2, -1, 0, -1, -1.8896190267801285, 2.387520755291127, 9); 
-      void* var_147 = tensorBatchNorm(var_146, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, 0.001); 
-      void* var_148 = tensorAdd(var_145, var_147); 
-      void* var_149 = tensorRelu(var_148); 
-      void* var_150 = ConvLayer_PROMISE(var_149, 0.0, 12.392736603737378, conv2d_48_w, -0.04417608780786395, 0.06200448917225007, conv2d_48_b, -6.6323187e-06, 7.1494946e-06, 0, 0, 1, 1, -1, 0, -1, -9.068103209495545, 5.912482521057253, 9); 
-      void* var_151 = tensorBatchNorm(var_150, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, 0.001); 
-      void* var_152 = tensorRelu(var_151); 
-      void* var_153 = ConvLayer_PROMISE(var_152, 0.0, 2.565971518278122, conv2d_49_w, -0.036550714168697596, 0.042889032773673605, conv2d_49_b, -3.1749918e-05, 3.1403273e-05, 1, 1, 1, 1, -1, 0, -1, -2.0715825698375703, 1.4426317431927056, 9); 
-      void* var_154 = tensorBatchNorm(var_153, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, 0.001); 
-      void* var_155 = tensorRelu(var_154); 
-      void* var_156 = ConvLayer_PROMISE(var_155, 0.0, 2.2121606218814973, conv2d_50_w, -0.04563436089083552, 0.07235725801438761, conv2d_50_b, -5.138708e-05, 5.6959605e-05, 0, 0, 1, 1, -1, 0, -1, -0.5048498404622078, 0.4972966857850613, 9); 
-      void* var_157 = tensorBatchNorm(var_156, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, 0.001); 
-      void* var_158 = tensorAdd(var_157, var_149); 
-      void* var_159 = tensorRelu(var_158); 
-      void* var_160 = ConvLayer_PROMISE(var_159, 0.0, 12.996321228027455, conv2d_51_w, -0.051894455961883065, 0.07700131461024579, conv2d_51_b, -8.893526e-06, 7.6235174e-06, 0, 0, 1, 1, -1, 0, -1, -7.534810958862305, 7.1688279371266015, 9); 
-      void* var_161 = tensorBatchNorm(var_160, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, 0.001); 
-      void* var_162 = tensorRelu(var_161); 
-      void* var_163 = ConvLayer_PROMISE(var_162, 0.0, 2.806837086677553, conv2d_52_w, -0.032556386385113004, 0.038920990321785316, conv2d_52_b, -3.1544037e-05, 4.5056524e-05, 1, 1, 1, 1, -1, 0, -1, -1.6795331789255141, 0.9551341712474886, 9); 
-      void* var_164 = tensorBatchNorm(var_163, batch_normalization_52_gamma, batch_normalization_52_beta, batch_normalization_52_mean, batch_normalization_52_variance, 0.001); 
-      void* var_165 = tensorRelu(var_164); 
-      void* var_166 = ConvLayer_PROMISE(var_165, 0.0, 2.7935527668000724, conv2d_53_w, -0.04313115822151303, 0.0774340439587877, conv2d_53_b, -2.8713988e-05, 4.1641888e-05, 0, 0, 1, 1, -1, 0, -1, -0.5173906384706497, 0.5710835611820362, 9); 
-      void* var_167 = tensorBatchNorm(var_166, batch_normalization_53_gamma, batch_normalization_53_beta, batch_normalization_53_mean, batch_normalization_53_variance, 0.001); 
-      void* var_168 = tensorAdd(var_167, var_159); 
-      void* var_169 = tensorRelu(var_168); 
-      void* var_170 = tensorPooling(var_169,1,7,7,0,0,7,7); 
-      void* var_171 = FCLayer_PROMISE(var_170, 0.0, 5.305631495475859, dense_1_w, -0.09220413094758988, 0.24919447432458666, dense_1_b, -0.024729362, 0.028545722, -1, -6.579668023586273, 7.794472872257277, 9); 
-      void* var_172 = tensorSoftmax(var_171); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_172); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-  }
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_piped.cc
deleted file mode 100644
index 1765f133353e127cfff9b6b45ea482a9b6e678b5..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_piped.cc
+++ /dev/null
@@ -1,212 +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.h" 
-#include "utils.h" 
-
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
-  
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-  
-
-  llvm_hpvm_initTensorRt(0); 
-
-  
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar100/"); 
-  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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,512,512); 
-  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(), 0,1,512,1,1); 
-  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(), 0,1,1,512,100); 
-  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(), 0,1,100,1,1); 
-
-    
-  int missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-   // NOTE: Wait on signal from OpenTuner 
-   stallOnOpenTunerSignal();
-    
-   if (missed >= to_skip){
-     break;           
-   }
-
-   startMemTracking(); 
-
-
-   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 + offset; 
-     int end = (i + 1) * batch_size + offset;
-     
-     void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-     void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9); 
-     void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9); 
-     void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9); 
-     void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9); 
-     void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9); 
-     void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9); 
-     void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9); 
-     void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9); 
-     void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9); 
-     void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9); 
-     void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9); 
-     void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9); 
-     void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9); 
-     void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9); 
-     void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9); 
-     void* var_15 = tensorSoftmax(var_14); 
-
-     uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-     float accuracy = computeAccuracy2(labels, batch_size, var_15, 100); 
-     final_accuracy += accuracy;
-
-
-     if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_15, classConfs, predictedLabels, relative_start, relative_end);
-     }
-
-     freeBatchMemory();  
-   }
-
-   final_accuracy = final_accuracy / batch_count; 
-   dumpFinalAccuracy(final_accuracy);
-
-
-   if (final_accuracy < bench_acc)
-     missed += 1;
-
-
-   if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-
-   // NOTE: Signal back to OpenTuner 
-   signalPipeToOpenTuner();
- }
-
- dumpExecutionAccuracies(); 
-
- llvm_hpvm_cleanupTensorRt(); 
-
- return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc
deleted file mode 100644
index 798bc8a1d761a0beca029c6ca1d8f6c543739ab3..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc
+++ /dev/null
@@ -1,207 +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.h" 
-#include "utils.h" 
-
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 1000;
-  int offset = 5000;
-
-  
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-  
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
-  
-  llvm_hpvm_initTensorRt(0); 
-
-  
-  int missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-   if (missed >= to_skip){
-     break;           
-   }
-
-   startMemTracking(); 
-
-
-   int batch_count = test_input_size / batch_size; 
-   float final_accuracy = 0.0; 
-
-   std::string dir_prefix = std::string("../model_params/vgg16_cifar100/"); 
-   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");   
-
-   for(int i = 0; i < batch_count; i++){
-     
-     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(), 0,64,3,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,64,64,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,128,64,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,128,128,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,256,128,3,3); 
-     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(), 0,1,256,1,1); 
-     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(), 0,256,256,3,3); 
-     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(), 0,1,256,1,1); 
-     std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-     void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-     std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-     void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-     std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-     void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-     std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-     void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-     void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-     void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-     void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-     void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-     void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-     void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-     void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-     void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-     void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-     void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-     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(), 0,1,1,512,512); 
-     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(), 0,1,512,1,1); 
-     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(), 0,1,1,512,100); 
-     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(), 0,1,100,1,1); 
-
-
-     int start = i * batch_size + offset; 
-     int end = (i + 1) * batch_size + offset;
-     
-
-     void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-     void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9); 
-     void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9); 
-     void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9); 
-     void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9); 
-     void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9); 
-     void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9); 
-     void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9); 
-     void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9); 
-     void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9); 
-     void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9); 
-     void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9); 
-     void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9); 
-     void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9); 
-     void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9); 
-     void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9); 
-     void* var_15 = tensorSoftmax(var_14); 
-
-     uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-     float accuracy = computeAccuracy2(labels, batch_size, var_15, 100); 
-     final_accuracy += accuracy;
-
-
-     if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_15, classConfs, predictedLabels, relative_start, relative_end);
-     }
-
-     freeBatchMemory();  
-   }
-
-   final_accuracy = final_accuracy / batch_count; 
-   dumpFinalAccuracy(final_accuracy);
-
-
-   if (final_accuracy < bench_acc)
-     missed += 1;
-
-
-   if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
- }
-
- dumpExecutionAccuracies(); 
-
- llvm_hpvm_cleanupTensorRt(); 
-
- return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc
deleted file mode 100644
index 7911c645679f31171e1c1f87facc1c1f82640adc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc
+++ /dev/null
@@ -1,137 +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(3); 
-
- int total_runs = 1; 
- for (int i = 0 ; i < total_runs; i++){ 
-
-   startMemTracking(); 
-
-   int test_input_size = 4000; 
-   //int batch_size = 2500;
-   int batch_size = 4000;
-   int offset = 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("../model_params/vgg16_cifar100_front/"); 
-     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(), 0,64,3,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,64,64,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,128,64,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,128,128,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,256,128,3,3); 
-     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(), 0,1,256,1,1); 
-     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(), 0,256,256,3,3); 
-     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(), 0,1,256,1,1); 
-     std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-     void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-     std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-     void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-     std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-     void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-     std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-     void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-     void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-     void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-     void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-     void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-     void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-     void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-     void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-     void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-     void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-     void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-     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(), 0,1,1,512,512); 
-     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(), 0,1,512,1,1); 
-     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(), 0,1,1,512,100); 
-     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(), 0,1,100,1,1); 
-
-
-     int start = i * batch_size + offset; 
-     int end = (i + 1) * batch_size + offset; 
-
-     void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-     void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9); 
-     void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9); 
-     void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9); 
-     void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9); 
-     void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9); 
-     void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9); 
-     void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9); 
-     void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9); 
-     void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9); 
-     void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9); 
-     void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9); 
-     void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9); 
-     void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9); 
-     void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9); 
-     void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9); 
-     void* var_15 = tensorSoftmax(var_14); 
-
-     uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-     // float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
-     float accuracy = computeTop5Accuracy(labels, batch_size, var_15, 100);
-
-     final_accuracy += accuracy; 
-     freeBatchMemory(); 
- 
-   }
-
-   final_accuracy = final_accuracy / batch_count; 
-   dumpFinalAccuracy(final_accuracy); 
- }
-
- dumpExecutionAccuracies(); 
-
- llvm_hpvm_cleanupTensorRt(); 
-
- return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_piped.cc
deleted file mode 100644
index 19c802dc88bb9a140bf5022ee07ab55f408ac53f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_piped.cc
+++ /dev/null
@@ -1,214 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
- 
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
- 
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
- 
-
-  llvm_hpvm_initTensorRt(0); 
-
-
-  std::string dir_prefix = std::string("../model_params/vgg16_cifar10/");    
-  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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,512,512); 
-  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(), 0,1,512,1,1); 
-  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(), 0,1,1,512,10); 
-  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); 
-
-    
-  int missed = 0; 
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-
-    if (missed >= to_skip){
-      break;           
-    }
-   
-    startMemTracking(); 
-
-   
-    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 + offset; 
-      int end = (i + 1) * batch_size + offset; 
-
-      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.53275156, 0.49437004, conv2d_1_b, -0.6403629, 0.2490165, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3590874671936035, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.3590874671936035, conv2d_2_w, -0.2688396, 0.20639156, conv2d_2_b, -0.7745511, 0.82006615, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.521231179237361, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 2.521231179237361, conv2d_3_w, -0.16776876, 0.14878987, conv2d_3_b, -0.35283303, 0.5154362, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2011985784769053, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1.2011985784769053, conv2d_4_w, -0.088948585, 0.114222586, conv2d_4_b, -0.30250227, 0.36856708, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.0359880930185312, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 1.0359880930185312, conv2d_5_w, -0.07739562, 0.10973293, conv2d_5_b, -0.15568458, 0.17634983, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.3004955950379369, 9); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 0.3004955950379369, conv2d_6_w, -0.051649556, 0.05435231, conv2d_6_b, -0.07395447, 0.07996062, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.11490475405007583, 9); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.11490475405007583, conv2d_7_w, -0.043513633, 0.07577866, conv2d_7_b, -0.06921874, 0.02660573, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.16232508487999475, 9); 
-      void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 0.16232508487999475, conv2d_8_w, -0.033842053, 0.045218028, conv2d_8_b, -0.022827804, 0.023845317, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.12424996573477909, 9); 
-      void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.12424996573477909, conv2d_9_w, -0.02211613, 0.032084666, conv2d_9_b, -0.02699063, 0.03773564, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.1746344865113496, 9); 
-      void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.1746344865113496, conv2d_10_w, -0.01979376, 0.034854397, conv2d_10_b, -0.036107242, 0.07056531, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.5751757621765137, 9); 
-      void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.5751757621765137, conv2d_11_w, -0.03452098, 0.046055835, conv2d_11_b, -0.051925894, 0.07039055, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7718751144409115, 9); 
-      void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.7718751144409115, conv2d_12_w, -0.025946895, 0.040090334, conv2d_12_b, -0.06049362, 0.12658806, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1728516906499844, 9); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1728516906499844, conv2d_13_w, -0.021766115, 0.03315237, conv2d_13_b, -0.20705001, 0.117947325, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.0015769386291495, 9); 
-      void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.0015769386291495, dense_1_w, -0.042597745, 0.046707444, dense_1_b, -0.21937433, 0.2545502, 1, 0.0, 2.002361118793486, 9); 
-      void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.002361118793486, dense_2_w, -0.32550547, 0.30829763, dense_2_b, -1.1787822, 1.2378151, -1, -18.251470546722413, 24.17363445281988, 9); 
-      void* var_15 = tensorSoftmax(var_14); 
-
-      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy2(labels, batch_size, var_15); 
-      final_accuracy += accuracy;
-
-
-      if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_15, classConfs, predictedLabels, relative_start, relative_end);
-      }
-
-     
-      freeBatchMemory(); 
- 
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy); 
-
-
-    if (final_accuracy < bench_acc)
-      missed += 1;
-
-
-    if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-    }
-
-    // NOTE: Signal back to OpenTuner 
-    signalPipeToOpenTuner();
-  }
-
-
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc
deleted file mode 100644
index 754429a3d5328ca011ffbca75cb5aa47273f3d69..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc
+++ /dev/null
@@ -1,208 +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.h" 
-#include "utils.h" 
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){ 
-
-  int test_input_size = 5000; 
-  int batch_size = 500;
-  int offset = 5000;
-
- 
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
- 
-  bool shouldDumpClassConf = false;
-  float* classConfs;
-  int* predictedLabels;
-  if(argc > 7){
-    shouldDumpClassConf = true;
-    classConfs = (float*) malloc(sizeof(float) * test_input_size);
-    predictedLabels = (int*) malloc(sizeof(int) * test_input_size);
-  }
-
- 
-
- llvm_hpvm_initTensorRt(0); 
-
- int missed = 0; 
- for (int i = 0 ; i < total_runs; i++){ 
-
-   if (missed >= to_skip){
-     break;           
-   }
-   
-   startMemTracking(); 
-
-   
-   int batch_count = test_input_size / batch_size; 
-   float final_accuracy = 0.0; 
-   
-   std::string dir_prefix = std::string("../model_params/vgg16_cifar10/");       
-   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"); 
-   
-   for(int i = 0; i < batch_count; i++){ 
-
-     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(), 0,64,3,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,64,64,3,3); 
-     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(), 0,1,64,1,1); 
-     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(), 0,128,64,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,128,128,3,3); 
-     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(), 0,1,128,1,1); 
-     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(), 0,256,128,3,3); 
-     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(), 0,1,256,1,1); 
-     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(), 0,256,256,3,3); 
-     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(), 0,1,256,1,1); 
-     std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-     void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-     std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-     void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-     std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-     void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-     std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-     void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-     void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-     void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-     void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-     void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-     void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-     void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-     void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-     void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-     std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-     void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-     std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-     void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-     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(), 0,1,1,512,512); 
-     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(), 0,1,512,1,1); 
-     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(), 0,1,1,512,10); 
-     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); 
-
-
-     int start = i * batch_size + offset; 
-     int end = (i + 1) * batch_size + offset; 
-
-     void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-     void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.53275156, 0.49437004, conv2d_1_b, -0.6403629, 0.2490165, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3590874671936035, 9); 
-     void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.3590874671936035, conv2d_2_w, -0.2688396, 0.20639156, conv2d_2_b, -0.7745511, 0.82006615, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.521231179237361, 9); 
-     void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 2.521231179237361, conv2d_3_w, -0.16776876, 0.14878987, conv2d_3_b, -0.35283303, 0.5154362, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2011985784769053, 9); 
-     void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1.2011985784769053, conv2d_4_w, -0.088948585, 0.114222586, conv2d_4_b, -0.30250227, 0.36856708, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.0359880930185312, 9); 
-     void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 1.0359880930185312, conv2d_5_w, -0.07739562, 0.10973293, conv2d_5_b, -0.15568458, 0.17634983, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.3004955950379369, 9); 
-     void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 0.3004955950379369, conv2d_6_w, -0.051649556, 0.05435231, conv2d_6_b, -0.07395447, 0.07996062, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.11490475405007583, 9); 
-     void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.11490475405007583, conv2d_7_w, -0.043513633, 0.07577866, conv2d_7_b, -0.06921874, 0.02660573, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.16232508487999475, 9); 
-     void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 0.16232508487999475, conv2d_8_w, -0.033842053, 0.045218028, conv2d_8_b, -0.022827804, 0.023845317, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.12424996573477909, 9); 
-     void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.12424996573477909, conv2d_9_w, -0.02211613, 0.032084666, conv2d_9_b, -0.02699063, 0.03773564, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.1746344865113496, 9); 
-     void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.1746344865113496, conv2d_10_w, -0.01979376, 0.034854397, conv2d_10_b, -0.036107242, 0.07056531, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.5751757621765137, 9); 
-     void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.5751757621765137, conv2d_11_w, -0.03452098, 0.046055835, conv2d_11_b, -0.051925894, 0.07039055, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7718751144409115, 9); 
-     void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.7718751144409115, conv2d_12_w, -0.025946895, 0.040090334, conv2d_12_b, -0.06049362, 0.12658806, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1728516906499844, 9); 
-     void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1728516906499844, conv2d_13_w, -0.021766115, 0.03315237, conv2d_13_b, -0.20705001, 0.117947325, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.0015769386291495, 9); 
-     void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.0015769386291495, dense_1_w, -0.042597745, 0.046707444, dense_1_b, -0.21937433, 0.2545502, 1, 0.0, 2.002361118793486, 9); 
-     void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.002361118793486, dense_2_w, -0.32550547, 0.30829763, dense_2_b, -1.1787822, 1.2378151, -1, -18.251470546722413, 24.17363445281988, 9); 
-     void* var_15 = tensorSoftmax(var_14); 
-
-     uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-     float accuracy = computeAccuracy2(labels, batch_size, var_15); 
-     final_accuracy += accuracy;
-
-
-     if(shouldDumpClassConf){
-	int relative_start = start - offset;
-	int relative_end = end - offset;
-        copyClassConfsAndLabels(var_15, classConfs, predictedLabels, relative_start, relative_end);
-     }
-
-     
-     freeBatchMemory(); 
- 
-   }
-
-   final_accuracy = final_accuracy / batch_count; 
-   dumpFinalAccuracy(final_accuracy); 
-
-
-   if (final_accuracy < bench_acc)
-     missed += 1;
-
-
-   if(shouldDumpClassConf){
-      int labels_start = offset;
-      int labels_end = offset + test_input_size;
-      uint32_t* goldLabels = readLabelsBatch3(labels32_path.c_str(), labels_start, labels_end);
-      dumpClassConfsAndLabels(classConfs, predictedLabels, goldLabels, test_input_size);
-   }
-
- }
-
-
- dumpExecutionAccuracies(); 
-
- llvm_hpvm_cleanupTensorRt(); 
-
- return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_piped.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_piped.cc
deleted file mode 100644
index 99ee36b6eb811a29935071adc08cddcaeb457736..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_piped.cc
+++ /dev/null
@@ -1,186 +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.h" 
-#include "utils.h" 
-
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-
-int main(int argc, char* argv[]){ 
-
-  llvm_hpvm_initTensorRt(0); 
-
-  int total_runs = 1;
-  int offset = 0;
- 
-  int test_input_size = 2000; 
-  int batch_size = 20; 
-  int batch_count = test_input_size / batch_size; 
-  float final_accuracy = 0.0; 
-
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-
-  
-
-  std::string dir_prefix = std::string("/shared/hsharif3/vgg16_imagenet_1/"); 
-  std::string input_path =  dir_prefix + std::string("test_input_combined.bin"); 
-  std::string labels_path =  dir_prefix + std::string("test_labels_combined.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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,25088,4096); 
-  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(), 0,1,4096,1,1); 
-  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(), 0,1,1,4096,4096); 
-  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(), 0,1,4096,1,1); 
-  std::string dense_3_w_path =  dir_prefix + std::string("dense_3_w.bin"); 
-  void* dense_3_w =  readTrainedWeights(dense_3_w_path.c_str(), 0,1,1,4096,1000); 
-  std::string dense_3_b_path =  dir_prefix + std::string("dense_3_b.bin"); 
-  void* dense_3_b =  readTrainedWeights(dense_3_b_path.c_str(), 0,1,1000,1,1); 
-
-
-  for (int i = 0 ; i < total_runs; i++){ 
-
-    // NOTE: Wait on signal from OpenTuner 
-    stallOnOpenTunerSignal();
-   
-    startMemTracking(); 
-
-   
-    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,3,224,224); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -123.68, 151.061, conv2d_1_w, -0.5682651399970055, 0.5677501424551024, conv2d_1_b, -0.015828926, 2.064037, 1, 1, 1, 1, -1, 0, 1, 0.0, 407.96143194580145, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 407.96143194580145, conv2d_2_w, -0.13156980648636818, 0.2164201746285022, conv2d_2_b, -1.0271513, 0.9052184, 1, 1, 1, 1, 0, 2, 1, 0.0, 1973.2054975586288, 9); 
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1973.2054975586288, conv2d_3_w, -0.18644111251831055, 0.202149114727974, conv2d_3_b, -0.17922063, 0.36547425, 1, 1, 1, 1, -1, 0, 1, 0.0, 2386.9648486329534, 9); 
-      void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 2386.9648486329534, conv2d_4_w, -0.10804861642420292, 0.12427636455744764, conv2d_4_b, -0.59533477, 0.63375777, 1, 1, 1, 1, 0, 2, 1, 0.0, 4998.494643554761, 9); 
-      void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 4998.494643554761, conv2d_5_w, -0.08040237371623515, 0.09835810117424044, conv2d_5_b, -0.20097896, 0.34949613, 1, 1, 1, 1, -1, 0, 1, 0.0, 4637.92161425807, 9); 
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 4637.92161425807, conv2d_6_w, -0.05306418750435114, 0.06628044287860436, conv2d_6_b, -0.18124875, 0.274845, 1, 1, 1, 1, -1, 0, 1, 0.0, 4365.822572754019, 9); 
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 4365.822572754019, conv2d_7_w, -0.05084674355760217, 0.07320860563218634, conv2d_7_b, -0.14288792, 0.59477174, 1, 1, 1, 1, 0, 2, 1, 0.0, 5600.749117676456, 9); 
-      void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 5600.749117676456, conv2d_8_w, -0.04523278899490833, 0.053042236261070186, conv2d_8_b, -0.14548235, 0.3148451, 1, 1, 1, 1, -1, 0, 1, 0.0, 3240.830364746551, 9); 
-      void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 3240.830364746551, conv2d_9_w, -0.02917514201253653, 0.03586270406842279, conv2d_9_b, -0.08428453, 0.18237582, 1, 1, 1, 1, -1, 0, 1, 0.0, 1895.9044943847766, 9); 
-      void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 1895.9044943847766, conv2d_10_w, -0.029496615380048753, 0.04047201693058028, conv2d_10_b, -0.19835947, 0.33766547, 1, 1, 1, 1, 0, 2, 1, 0.0, 1273.674801757832, 9); 
-      void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1273.674801757832, conv2d_11_w, -0.031951379626989365, 0.04218719156458998, conv2d_11_b, -0.3508028, 0.6397485, 1, 1, 1, 1, -1, 0, 1, 0.0, 652.76720800782, 9); 
-      void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 652.76720800782, conv2d_12_w, -0.028522676015272738, 0.03794213477522136, conv2d_12_b, -0.9171057, 0.7597668, 1, 1, 1, 1, -1, 0, 1, 0.0, 316.98977236938646, 9); 
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 316.98977236938646, conv2d_13_w, -0.02821479567326605, 0.03854479804635069, conv2d_13_b, -0.50036746, 9.431553, 1, 1, 1, 1, 0, 2, 1, 0.0, 148.72470889282292, 9); 
-      void* var_13 = FCLayer_PROMISE(var_12, 0.0, 148.72470889282292, dense_1_w, -0.007091613108757884, 0.008147951829247227, dense_1_b, -0.78005254, 0.8555075, 1, 0.0, 40.64329356002882, 9); 
-      void* var_14 = FCLayer_PROMISE(var_13, 0.0, 40.64329356002882, dense_2_w, -0.012781758182682096, 0.01437051862943929, dense_2_b, -0.012339931, 1.2154555, 1, 0.0, 11.167800696373025, 9); 
-      void* var_15 = FCLayer_PROMISE(var_14, 0.0, 11.167800696373025, dense_3_w, -0.02119149128906429, 0.02715564412623694, dense_3_b, -0.773357, 0.6615543, -1, -7.4482048592567445, 17.882177452087543, 9); 
-      void* var_16 = tensorSoftmax(var_15); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_16); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
-
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-
-    // NOTE: Signal back to OpenTuner 
-
-    signalPipeToOpenTuner();
-   
-  }
-
- 
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_promise.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_promise.cc
deleted file mode 100644
index 69d47078f30e62c4dc2d225dd1e1a2acd4da0c6a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_imagenet_promise.cc
+++ /dev/null
@@ -1,179 +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.h" 
-#include "utils.h" 
-
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-
-int main(int argc, char* argv[]){ 
-
-  llvm_hpvm_initTensorRt(1); 
-
-  int total_runs = 1;
-  int offset = 0;
- 
-  int test_input_size = 2000; 
-  int batch_size = 20; 
-  int batch_count = test_input_size / batch_size; 
-  float final_accuracy = 0.0; 
-
-
-  if (argc > 1){
-    total_runs = atoi(argv[1]);
-  }
-
-  if (argc > 2){
-    bench_acc = atof(argv[2]);
-  }
-
-  if(argc > 3){
-    to_skip = atoi(argv[3]);   
-  }
-
-  if(argc > 4){
-    test_input_size = atoi(argv[4]);   
-  }
-
-  if(argc > 5){
-    offset = atoi(argv[5]);   
-  }
-
-  if(argc > 6){
-    batch_size = atoi(argv[6]);   
-  }
-  
-
-  
-
-  std::string dir_prefix = std::string("/shared/hsharif3/vgg16_imagenet_1/"); 
-  std::string input_path =  dir_prefix + std::string("test_input_combined.bin"); 
-  std::string labels_path =  dir_prefix + std::string("test_labels_combined.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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,25088,4096); 
-  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(), 0,1,4096,1,1); 
-  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(), 0,1,1,4096,4096); 
-  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(), 0,1,4096,1,1); 
-  std::string dense_3_w_path =  dir_prefix + std::string("dense_3_w.bin"); 
-  void* dense_3_w =  readTrainedWeights(dense_3_w_path.c_str(), 0,1,1,4096,1000); 
-  std::string dense_3_b_path =  dir_prefix + std::string("dense_3_b.bin"); 
-  void* dense_3_b =  readTrainedWeights(dense_3_b_path.c_str(), 0,1,1000,1,1); 
-
-
-  for (int i = 0 ; i < total_runs; i++){ 
-   
-    startMemTracking(); 
-   
-    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,3,224,224); 
-
-      void* var_0 = ConvLayer_PROMISE(input, -123.68, 151.061, conv2d_1_w, -0.5682651399970055, 0.5677501424551024, conv2d_1_b, -0.015828926, 2.064037, 1, 1, 1, 1, -1, 0, 1, 0.0, 407.96143194580145, 9); 
-      void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 407.96143194580145, conv2d_2_w, -0.13156980648636818, 0.2164201746285022, conv2d_2_b, -1.0271513, 0.9052184, 1, 1, 1, 1, 0, 2, 1, 0.0, 1973.2054975586288, 9);
-      void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1973.2054975586288, conv2d_3_w, -0.18644111251831055, 0.202149114727974, conv2d_3_b, -0.17922063, 0.36547425, 1, 1, 1, 1, -1, 0, 1, 0.0, 2386.9648486329534, 9);
-      void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 2386.9648486329534, conv2d_4_w, -0.10804861642420292, 0.12427636455744764, conv2d_4_b, -0.59533477, 0.63375777, 1, 1, 1, 1, 0, 2, 1, 0.0, 4998.494643554761, 9);
-      void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 4998.494643554761, conv2d_5_w, -0.08040237371623515, 0.09835810117424044, conv2d_5_b, -0.20097896, 0.34949613, 1, 1, 1, 1, -1, 0, 1, 0.0, 4637.92161425807, 9);      
-      void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 4637.92161425807, conv2d_6_w, -0.05306418750435114, 0.06628044287860436, conv2d_6_b, -0.18124875, 0.274845, 1, 1, 1, 1, -1, 0, 1, 0.0, 4365.822572754019, 9);      
-      void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 4365.822572754019, conv2d_7_w, -0.05084674355760217, 0.07320860563218634, conv2d_7_b, -0.14288792, 0.59477174, 1, 1, 1, 1, 0, 2, 1, 0.0, 5600.749117676456, 9);      
-      void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 5600.749117676456, conv2d_8_w, -0.04523278899490833, 0.053042236261070186, conv2d_8_b, -0.14548235, 0.3148451, 1, 1, 1, 1, -1, 0, 1, 0.0, 3240.830364746551, 9);	         
-      void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 3240.830364746551, conv2d_9_w, -0.02917514201253653, 0.03586270406842279, conv2d_9_b, -0.08428453, 0.18237582, 1, 1, 1, 1, -1, 0, 1, 0.0, 1895.9044943847766, 9);
-      void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 1895.9044943847766, conv2d_10_w, -0.029496615380048753, 0.04047201693058028, conv2d_10_b, -0.19835947, 0.33766547, 1, 1, 1, 1, 0, 2, 1, 0.0, 1273.674801757832, 9);     
-      void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1273.674801757832, conv2d_11_w, -0.031951379626989365, 0.04218719156458998, conv2d_11_b, -0.3508028, 0.6397485, 1, 1, 1, 1, -1, 0, 1, 0.0, 652.76720800782, 9);
-      void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 652.76720800782, conv2d_12_w, -0.028522676015272738, 0.03794213477522136, conv2d_12_b, -0.9171057, 0.7597668, 1, 1, 1, 1, -1, 0, 1, 0.0, 316.98977236938646, 9);	    
-      void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 316.98977236938646, conv2d_13_w, -0.02821479567326605, 0.03854479804635069, conv2d_13_b, -0.50036746, 9.431553, 1, 1, 1, 1, 0, 2, 1, 0.0, 148.72470889282292, 9);      
-      void* var_13 = FCLayer_PROMISE(var_12, 0.0, 148.72470889282292, dense_1_w, -0.007091613108757884, 0.008147951829247227, dense_1_b, -0.78005254, 0.8555075, 1, 0.0, 40.64329356002882, 9);
-
-      void* var_14 = FCLayer_PROMISE(var_13, 0.0, 40.64329356002882, dense_2_w, -0.012781758182682096, 0.01437051862943929, dense_2_b, -0.012339931, 1.2154555, 1, 0.0, 11.167800696373025, 9); 
-      void* var_15 = FCLayer_PROMISE(var_14, 0.0, 11.167800696373025, dense_3_w, -0.02119149128906429, 0.02715564412623694, dense_3_b, -0.773357, 0.6615543, -1, -7.4482048592567445, 17.882177452087543, 9); 
-      void* var_16 = tensorSoftmax(var_15); 
-
-      uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); 
-
-      float accuracy = computeAccuracy3(labels, var_16); 
-      final_accuracy += accuracy; 
-      freeBatchMemory(); 
-
-    }
-
-    final_accuracy = final_accuracy / batch_count; 
-    dumpFinalAccuracy(final_accuracy);
-   
-  }
-
- 
-  dumpExecutionAccuracies(); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc
deleted file mode 100644
index 3ee273d70aea6d74cfa55f250e999b05506f9b21..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc
+++ /dev/null
@@ -1,167 +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("../model_params/vgg16_cifar100_front/"); 
-  //std::string input_path =  dir_prefix + std::string("vgg16_cifar100_calib.bin"); 
-  //std::string labels_path =  dir_prefix + std::string("vgg16_cifar100_train_labels.bin");
-
-  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(), 0,64,3,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,64,64,3,3); 
-  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(), 0,1,64,1,1); 
-  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(), 0,128,64,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,128,128,3,3); 
-  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(), 0,1,128,1,1); 
-  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(), 0,256,128,3,3); 
-  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(), 0,1,256,1,1); 
-  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(), 0,256,256,3,3); 
-  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(), 0,1,256,1,1); 
-  std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-  void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-  std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-  void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-  std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-  void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-  std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-  void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-  void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-  void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-  void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-  void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-  void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-  void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-  void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-  void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-  std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-  void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-  std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-  void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-  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(), 0,1,1,512,512); 
-  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(), 0,1,512,1,1); 
-  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(), 0,1,1,512,100); 
-  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(), 0,1,100,1,1); 
-
-
-  startMemTracking(); 
-
-  int test_input_size = 5000; 
-  int batch_size = 2500;
-  int offset = 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 + offset; 
-    int end = (i + 1) * batch_size + offset; 
-
-    void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
-
-    void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0); 
-    void* var_1 = tensorAdd(var_0, conv2d_1_b); 
-    void* var_2 = tensorRelu(var_1); 
-    void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-    void* var_5 = tensorAdd(var_4, conv2d_2_b); 
-    void* var_6 = tensorRelu(var_5); 
-    void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2); 
-    void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-    void* var_9 = tensorAdd(var_8, conv2d_3_b); 
-    void* var_10 = tensorRelu(var_9); 
-    void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-    void* var_13 = tensorAdd(var_12, conv2d_4_b); 
-    void* var_14 = tensorRelu(var_13); 
-    void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2); 
-    void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-    void* var_17 = tensorAdd(var_16, conv2d_5_b); 
-    void* var_18 = tensorRelu(var_17); 
-    void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-    void* var_21 = tensorAdd(var_20, conv2d_6_b); 
-    void* var_22 = tensorRelu(var_21); 
-    void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-    void* var_25 = tensorAdd(var_24, conv2d_7_b); 
-    void* var_26 = tensorRelu(var_25); 
-    void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2); 
-    void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0); 
-    void* var_29 = tensorAdd(var_28, conv2d_8_b); 
-    void* var_30 = tensorRelu(var_29); 
-    void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-    void* var_33 = tensorAdd(var_32, conv2d_9_b); 
-    void* var_34 = tensorRelu(var_33); 
-    void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0); 
-    void* var_37 = tensorAdd(var_36, conv2d_10_b); 
-    void* var_38 = tensorRelu(var_37); 
-    void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2); 
-    void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-    void* var_41 = tensorAdd(var_40, conv2d_11_b); 
-    void* var_42 = tensorRelu(var_41); 
-    void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-    void* var_45 = tensorAdd(var_44, conv2d_12_b); 
-    void* var_46 = tensorRelu(var_45); 
-    void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-    void* var_49 = tensorAdd(var_48, conv2d_13_b); 
-    void* var_50 = tensorRelu(var_49); 
-    void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2); 
-    void* var_54 = tensorGemmGPU(var_51, dense_1_w); 
-    void* var_55 = tensorAdd(var_54, dense_1_b); 
-    void* var_56 = tensorRelu(var_55); 
-    void* var_58 = tensorGemmGPU(var_56, dense_2_w); 
-    void* var_59 = tensorAdd(var_58, dense_2_b); 
-    void* var_60 = tensorSoftmax(var_59); 
-
-    uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-    //float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
-    float accuracy = computeTop5Accuracy(labels, batch_size, var_60, 100);
-    final_accuracy += accuracy; 
-    freeBatchMemory(); 
- 
-  }
-
-  final_accuracy = final_accuracy / batch_count; 
-  dumpFinalAccuracy(final_accuracy); 
-
-  llvm_hpvm_cleanupTensorRt(); 
-
-  return 0; 
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/global_knobs.txt b/hpvm/projects/hpvm-tensor-rt/global_knobs.txt
deleted file mode 120000
index 3c40f2450d933e4e5680f61542004d3ccfc06778..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/global_knobs.txt
+++ /dev/null
@@ -1 +0,0 @@
-autotuner/data/global_knobs.txt
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/global_knobs.txt b/hpvm/projects/hpvm-tensor-rt/global_knobs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee2cd80cb6e33da5e97ffe2e842644d7a705cdff
--- /dev/null
+++ b/hpvm/projects/hpvm-tensor-rt/global_knobs.txt
@@ -0,0 +1,69 @@
+fp32,11	-1	1.0	tensorConvolution	tensorConvApprox	dev	conv_fc_red
+fp16,12	-1	1.5	tensorConvolution	tensorConvApproxHalf2	install	conv_fc_red
+perf,121	1,2,0	2.0	tensorConvolution	tensorConvApprox	dev	conv
+perf,122	1,2,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
+perf,123	2,1,0	2.0	tensorConvolution	tensorConvApprox	dev	conv
+perf,124	2,1,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
+perf,125	1,3,0	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,126	1,3,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,127	1,3,2	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,128	3,1,0	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,129	3,1,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,130	3,1,2	1.5	tensorConvolution	tensorConvApprox	dev	conv
+perf,131	1,4,0	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,132	1,4,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,133	1,4,2	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,134	1,4,3	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,135	4,1,0	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,136	4,1,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,137	4,1,2	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf,138	4,1,3	1.33	tensorConvolution	tensorConvApprox	dev	conv
+perf_fp16,151	1,2,0	3.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,152	1,2,1	3.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,153	2,1,0	3.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,154	2,1,1	3.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,155	1,3,0	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,156	1,3,1	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,157	1,3,2	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,158	3,1,0	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,159	3,1,1	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,160	3,1,2	2.25	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,161	1,4,0	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,162	1,4,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,163	1,4,2	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,164	1,4,3	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,165	4,1,0	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,166	4,1,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,167	4,1,2	2.0	tensorConvolution	tensorConvApprox	install	conv
+perf_fp16,168	4,1,3	2.0	tensorConvolution	tensorConvApprox	install	conv
+samp,231	2,0,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
+samp,232	2,1,1	2.0	tensorConvolution	tensorConvApprox	dev	conv
+samp,233	3,0,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
+samp,234	3,1,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
+samp,235	3,2,1	1.5	tensorConvolution	tensorConvApprox	dev	conv
+samp,236	4,0,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+samp,237	4,1,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+samp,238	4,2,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+samp,239	4,3,1	1.33	tensorConvolution	tensorConvApprox	dev	conv
+samp_fp16,261	2,0,1	3.0	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,262	2,1,1	3.0	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,263	3,0,1	2.25	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,264	3,1,1	2.25	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,265	3,2,1	2.25	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,266	4,0,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,267	4,1,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,268	4,2,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+samp_fp16,269	4,3,1	2.0	tensorConvolution	tensorConvApprox	install	conv
+red_samp,41	1	1.5	tensorReduction		tensorReduction		dev	red
+red_samp,42	1	2.25	tensorReduction		tensorReduction		dev	red
+red_samp,43	1	1.4	tensorReduction		tensorReduction		dev	red
+red_samp,44	1	2	tensorReduction		tensorReduction		dev	red
+red_samp,45	1	1.25	tensorReduction		tensorReduction		dev	red
+red_samp,46	1	1.8	tensorReduction		tensorReduction		dev	red
+swing_level,1	1	12	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,2	1	10	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,3	1	9	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,4	1	8	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,5	1	6	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,6	1	5	tensorConvolution	tensorConvApprox	install	conv_fc
+swing_level,7	1	4	tensorConvolution	tensorConvApprox	install	conv_fc
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin
deleted file mode 100644
index 89ab6ad37cac94360f7f87c93676f353829f1deb..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin
deleted file mode 100644
index 0a2a381337e13fe52959c838b4a2bedab3c3f8ab..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-h4Q;¤ù;34¼j0_½G½–h;ìz/½ðÇÊ:àk¥¼{l½t+O;u¼8™¨¼d»”½®¼}8›<íO’¼äÕ¿»¤#½„ö¼”u<¼¿l…¼f¢;Ð4½ŠO ½>Øž¼7K¼0
4½ÎG:à'½ÔOF½M=;
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin
deleted file mode 100644
index 6cd00b88c5be6e212f2d3a37c8ea2a8edb1ceca7..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin
deleted file mode 100644
index c0adf3e885ce855a0cc9d1b4b12f73665187159e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/conv2_bias.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin
deleted file mode 100644
index 152c5bb0baae480f6b8d317889fc68f8d77247b6..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin
deleted file mode 100644
index 58221f45cdc56049b2edc29c244ea9d797a87fb5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc1_bias.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin
deleted file mode 100644
index 97d78a9610b15be285661c1d762026c9fa4100cb..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin
deleted file mode 100644
index cbda59beef150dfbca756621286f042ec8e247bf..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-Ê%”½ùb½Ó„g½W•­½$VĽéum½'Ƶ½J§’½·¾¶½›­¢½
\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin
deleted file mode 100644
index 4d2423f74188cfe0364185ccb66837785ccf4c4e..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/input.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin
deleted file mode 100644
index 5e1f3881897f4729d6d90ff208a08ccdabb8fe7c..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin
deleted file mode 100644
index 6f1d7576cd18621a2cf646d0dd835846623589e5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/labels32.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt
deleted file mode 100644
index af4d13d6f8e6b5902ff743b07ef6875d644df91a..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/lenet_mnist/quant_ranges.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-0 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
--1 1 -1 1 -1 1 -1 1
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt# b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
deleted file mode 100644
index 10692997a90e4490a91ad3d0e6e04285754144fd..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-
-
-activation  
-conv  
-
-activation  
-pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt# b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
deleted file mode 100644
index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
deleted file mode 100644
index 5089eb912bcb5335c96c04f6d98f5d17ab761c72..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
+++ /dev/null
@@ -1,2400 +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, 1, 1, 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, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __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, 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_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_8_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_9_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_11_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_12_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_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_14_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_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_17_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_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_convolution(t1, t2, 0, 0, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_20_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_21_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_23_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_24_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_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_26_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_27_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_29_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_30_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_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_32_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_33_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_35_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_36_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_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_38_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_39_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_41_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_42_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_43_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_44_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_45_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_46_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_47_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_48_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_49_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_50_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_51_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_52_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_53_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_54_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_55_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_56_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_57_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_58_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_59_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_60_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_61_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_62_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_63_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_64_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_65_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_66_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_67_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_68_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_69_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 512); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_70_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_71_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_72_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_73_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_74_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_75_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 1024); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_76_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_77_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_78_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_79_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_80_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_81_node(void* t1, size_t bytes_t1) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(1, t1, 0); 
-
-  void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_82_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_83_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_84_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* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes, 
-	  void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes, 
-	  void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes, 
-	  void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes, 
-	  void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes, 
-	  void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes, 
-	  void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes, 
-	  void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes, 
-	  void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes, 
-	  void* conv2d_2_w, size_t conv2d_2_w_bytes, 
-	  void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes, 
-	  void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes, 
-	  void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes, 
-	  void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes, 
-	  void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes, 
-	  void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes, 
-	  void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes, 
-	  void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes, 
-	  void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes, 
-	  void* conv2d_3_w, size_t conv2d_3_w_bytes, 
-	  void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes, 
-	  void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes, 
-	  void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes, 
-	  void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes, 
-	  void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes, 
-	  void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes, 
-	  void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes, 
-	  void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes, 
-	  void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes, 
-	  void* conv2d_4_w, size_t conv2d_4_w_bytes, 
-	  void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes, 
-	  void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes, 
-	  void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes, 
-	  void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes, 
-	  void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes, 
-	  void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes, 
-	  void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes, 
-	  void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes, 
-	  void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes, 
-	  void* conv2d_5_w, size_t conv2d_5_w_bytes, 
-	  void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes, 
-	  void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes, 
-	  void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes, 
-	  void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes, 
-	  void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes, 
-	  void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes, 
-	  void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes, 
-	  void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes, 
-	  void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes, 
-	  void* conv2d_6_w, size_t conv2d_6_w_bytes, 
-	  void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes, 
-	  void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes, 
-	  void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes, 
-	  void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes, 
-	  void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes, 
-	  void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes, 
-	  void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes, 
-	  void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes, 
-	  void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes, 
-	  void* conv2d_7_w, size_t conv2d_7_w_bytes, 
-	  void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes, 
-	  void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes, 
-	  void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes, 
-	  void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes, 
-	  void* depthwise_conv2d_7_w, size_t depthwise_conv2d_7_w_bytes, 
-	  void* batch_normalization_14_gamma, size_t batch_normalization_14_gamma_bytes, 
-	  void* batch_normalization_14_beta, size_t batch_normalization_14_beta_bytes, 
-	  void* batch_normalization_14_mean, size_t batch_normalization_14_mean_bytes, 
-	  void* batch_normalization_14_variance, size_t batch_normalization_14_variance_bytes, 
-	  void* conv2d_8_w, size_t conv2d_8_w_bytes, 
-	  void* batch_normalization_15_gamma, size_t batch_normalization_15_gamma_bytes, 
-	  void* batch_normalization_15_beta, size_t batch_normalization_15_beta_bytes, 
-	  void* batch_normalization_15_mean, size_t batch_normalization_15_mean_bytes, 
-	  void* batch_normalization_15_variance, size_t batch_normalization_15_variance_bytes, 
-	  void* depthwise_conv2d_8_w, size_t depthwise_conv2d_8_w_bytes, 
-	  void* batch_normalization_16_gamma, size_t batch_normalization_16_gamma_bytes, 
-	  void* batch_normalization_16_beta, size_t batch_normalization_16_beta_bytes, 
-	  void* batch_normalization_16_mean, size_t batch_normalization_16_mean_bytes, 
-	  void* batch_normalization_16_variance, size_t batch_normalization_16_variance_bytes, 
-	  void* conv2d_9_w, size_t conv2d_9_w_bytes, 
-	  void* batch_normalization_17_gamma, size_t batch_normalization_17_gamma_bytes, 
-	  void* batch_normalization_17_beta, size_t batch_normalization_17_beta_bytes, 
-	  void* batch_normalization_17_mean, size_t batch_normalization_17_mean_bytes, 
-	  void* batch_normalization_17_variance, size_t batch_normalization_17_variance_bytes, 
-	  void* depthwise_conv2d_9_w, size_t depthwise_conv2d_9_w_bytes, 
-	  void* batch_normalization_18_gamma, size_t batch_normalization_18_gamma_bytes, 
-	  void* batch_normalization_18_beta, size_t batch_normalization_18_beta_bytes, 
-	  void* batch_normalization_18_mean, size_t batch_normalization_18_mean_bytes, 
-	  void* batch_normalization_18_variance, size_t batch_normalization_18_variance_bytes, 
-	  void* conv2d_10_w, size_t conv2d_10_w_bytes, 
-	  void* batch_normalization_19_gamma, size_t batch_normalization_19_gamma_bytes, 
-	  void* batch_normalization_19_beta, size_t batch_normalization_19_beta_bytes, 
-	  void* batch_normalization_19_mean, size_t batch_normalization_19_mean_bytes, 
-	  void* batch_normalization_19_variance, size_t batch_normalization_19_variance_bytes, 
-	  void* depthwise_conv2d_10_w, size_t depthwise_conv2d_10_w_bytes, 
-	  void* batch_normalization_20_gamma, size_t batch_normalization_20_gamma_bytes, 
-	  void* batch_normalization_20_beta, size_t batch_normalization_20_beta_bytes, 
-	  void* batch_normalization_20_mean, size_t batch_normalization_20_mean_bytes, 
-	  void* batch_normalization_20_variance, size_t batch_normalization_20_variance_bytes, 
-	  void* conv2d_11_w, size_t conv2d_11_w_bytes, 
-	  void* batch_normalization_21_gamma, size_t batch_normalization_21_gamma_bytes, 
-	  void* batch_normalization_21_beta, size_t batch_normalization_21_beta_bytes, 
-	  void* batch_normalization_21_mean, size_t batch_normalization_21_mean_bytes, 
-	  void* batch_normalization_21_variance, size_t batch_normalization_21_variance_bytes, 
-	  void* depthwise_conv2d_11_w, size_t depthwise_conv2d_11_w_bytes, 
-	  void* batch_normalization_22_gamma, size_t batch_normalization_22_gamma_bytes, 
-	  void* batch_normalization_22_beta, size_t batch_normalization_22_beta_bytes, 
-	  void* batch_normalization_22_mean, size_t batch_normalization_22_mean_bytes, 
-	  void* batch_normalization_22_variance, size_t batch_normalization_22_variance_bytes, 
-	  void* conv2d_12_w, size_t conv2d_12_w_bytes, 
-	  void* batch_normalization_23_gamma, size_t batch_normalization_23_gamma_bytes, 
-	  void* batch_normalization_23_beta, size_t batch_normalization_23_beta_bytes, 
-	  void* batch_normalization_23_mean, size_t batch_normalization_23_mean_bytes, 
-	  void* batch_normalization_23_variance, size_t batch_normalization_23_variance_bytes, 
-	  void* depthwise_conv2d_12_w, size_t depthwise_conv2d_12_w_bytes, 
-	  void* batch_normalization_24_gamma, size_t batch_normalization_24_gamma_bytes, 
-	  void* batch_normalization_24_beta, size_t batch_normalization_24_beta_bytes, 
-	  void* batch_normalization_24_mean, size_t batch_normalization_24_mean_bytes, 
-	  void* batch_normalization_24_variance, size_t batch_normalization_24_variance_bytes, 
-	  void* conv2d_13_w, size_t conv2d_13_w_bytes, 
-	  void* batch_normalization_25_gamma, size_t batch_normalization_25_gamma_bytes, 
-	  void* batch_normalization_25_beta, size_t batch_normalization_25_beta_bytes, 
-	  void* batch_normalization_25_mean, size_t batch_normalization_25_mean_bytes, 
-	  void* batch_normalization_25_variance, size_t batch_normalization_25_variance_bytes, 
-	  void* depthwise_conv2d_13_w, size_t depthwise_conv2d_13_w_bytes, 
-	  void* batch_normalization_26_gamma, size_t batch_normalization_26_gamma_bytes, 
-	  void* batch_normalization_26_beta, size_t batch_normalization_26_beta_bytes, 
-	  void* batch_normalization_26_mean, size_t batch_normalization_26_mean_bytes, 
-	  void* batch_normalization_26_variance, size_t batch_normalization_26_variance_bytes, 
-	  void* conv2d_14_w, size_t conv2d_14_w_bytes, 
-	  void* batch_normalization_27_gamma, size_t batch_normalization_27_gamma_bytes, 
-	  void* batch_normalization_27_beta, size_t batch_normalization_27_beta_bytes, 
-	  void* batch_normalization_27_mean, size_t batch_normalization_27_mean_bytes, 
-	  void* batch_normalization_27_variance, size_t batch_normalization_27_variance_bytes, 
-	  void* dense_1_w, size_t dense_1_w_bytes, 
-	  void* dense_1_b, size_t dense_1_b_bytes){ 
-
-
-  __visc__hint(visc::CPU_TARGET); 
-  __visc__attributes(138, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, depthwise_conv2d_7_w, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, conv2d_8_w, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, depthwise_conv2d_8_w, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, conv2d_9_w, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, depthwise_conv2d_9_w, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, conv2d_10_w, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, depthwise_conv2d_10_w, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, conv2d_11_w, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, depthwise_conv2d_11_w, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, conv2d_12_w, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, depthwise_conv2d_12_w, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, conv2d_13_w, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, depthwise_conv2d_13_w, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, conv2d_14_w, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, dense_1_w, dense_1_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); 
-  __visc__bindIn(var_1, 6, 4, 0); 
-  __visc__bindIn(var_1, 7, 5, 0); 
-  __visc__bindIn(var_1, 8, 6, 0); 
-  __visc__bindIn(var_1, 9, 7, 0); 
-  __visc__bindIn(var_1, 10, 8, 0); 
-  __visc__bindIn(var_1, 11, 9, 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, 12, 2, 0); 
-  __visc__bindIn(var_3, 13, 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, 14, 2, 0); 
-  __visc__bindIn(var_4, 15, 3, 0); 
-  __visc__bindIn(var_4, 16, 4, 0); 
-  __visc__bindIn(var_4, 17, 5, 0); 
-  __visc__bindIn(var_4, 18, 6, 0); 
-  __visc__bindIn(var_4, 19, 7, 0); 
-  __visc__bindIn(var_4, 20, 8, 0); 
-  __visc__bindIn(var_4, 21, 9, 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); 
-  __visc__bindIn(var_6, 22, 2, 0); 
-  __visc__bindIn(var_6, 23, 3, 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, 24, 2, 0); 
-  __visc__bindIn(var_7, 25, 3, 0); 
-  __visc__bindIn(var_7, 26, 4, 0); 
-  __visc__bindIn(var_7, 27, 5, 0); 
-  __visc__bindIn(var_7, 28, 6, 0); 
-  __visc__bindIn(var_7, 29, 7, 0); 
-  __visc__bindIn(var_7, 30, 8, 0); 
-  __visc__bindIn(var_7, 31, 9, 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); 
-
-  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); 
-  __visc__bindIn(var_9, 32, 2, 0); 
-  __visc__bindIn(var_9, 33, 3, 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, 34, 2, 0); 
-  __visc__bindIn(var_10, 35, 3, 0); 
-  __visc__bindIn(var_10, 36, 4, 0); 
-  __visc__bindIn(var_10, 37, 5, 0); 
-  __visc__bindIn(var_10, 38, 6, 0); 
-  __visc__bindIn(var_10, 39, 7, 0); 
-  __visc__bindIn(var_10, 40, 8, 0); 
-  __visc__bindIn(var_10, 41, 9, 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); 
-
-  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); 
-  __visc__bindIn(var_12, 42, 2, 0); 
-  __visc__bindIn(var_12, 43, 3, 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, 44, 2, 0); 
-  __visc__bindIn(var_13, 45, 3, 0); 
-  __visc__bindIn(var_13, 46, 4, 0); 
-  __visc__bindIn(var_13, 47, 5, 0); 
-  __visc__bindIn(var_13, 48, 6, 0); 
-  __visc__bindIn(var_13, 49, 7, 0); 
-  __visc__bindIn(var_13, 50, 8, 0); 
-  __visc__bindIn(var_13, 51, 9, 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); 
-
-  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, 52, 2, 0); 
-  __visc__bindIn(var_15, 53, 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); 
-  __visc__bindIn(var_16, 54, 2, 0); 
-  __visc__bindIn(var_16, 55, 3, 0); 
-  __visc__bindIn(var_16, 56, 4, 0); 
-  __visc__bindIn(var_16, 57, 5, 0); 
-  __visc__bindIn(var_16, 58, 6, 0); 
-  __visc__bindIn(var_16, 59, 7, 0); 
-  __visc__bindIn(var_16, 60, 8, 0); 
-  __visc__bindIn(var_16, 61, 9, 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); 
-
-  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, 62, 2, 0); 
-  __visc__bindIn(var_18, 63, 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__bindIn(var_19, 64, 2, 0); 
-  __visc__bindIn(var_19, 65, 3, 0); 
-  __visc__bindIn(var_19, 66, 4, 0); 
-  __visc__bindIn(var_19, 67, 5, 0); 
-  __visc__bindIn(var_19, 68, 6, 0); 
-  __visc__bindIn(var_19, 69, 7, 0); 
-  __visc__bindIn(var_19, 70, 8, 0); 
-  __visc__bindIn(var_19, 71, 9, 0); 
-
-  void* var_20 = __visc__createNodeND(0, var_20_node); 
-
-  __visc__edge(var_19, var_20, 1, 0, 0, 0); 
-  __visc__edge(var_19, var_20, 1, 1, 1, 0); 
-
-  void* var_21 = __visc__createNodeND(0, var_21_node); 
-
-  __visc__edge(var_20, var_21, 1, 0, 0, 0); 
-  __visc__edge(var_20, var_21, 1, 1, 1, 0); 
-  __visc__bindIn(var_21, 72, 2, 0); 
-  __visc__bindIn(var_21, 73, 3, 0); 
-
-  void* var_22 = __visc__createNodeND(0, var_22_node); 
-
-  __visc__edge(var_21, var_22, 1, 0, 0, 0); 
-  __visc__edge(var_21, var_22, 1, 1, 1, 0); 
-  __visc__bindIn(var_22, 74, 2, 0); 
-  __visc__bindIn(var_22, 75, 3, 0); 
-  __visc__bindIn(var_22, 76, 4, 0); 
-  __visc__bindIn(var_22, 77, 5, 0); 
-  __visc__bindIn(var_22, 78, 6, 0); 
-  __visc__bindIn(var_22, 79, 7, 0); 
-  __visc__bindIn(var_22, 80, 8, 0); 
-  __visc__bindIn(var_22, 81, 9, 0); 
-
-  void* var_23 = __visc__createNodeND(0, var_23_node); 
-
-  __visc__edge(var_22, var_23, 1, 0, 0, 0); 
-  __visc__edge(var_22, var_23, 1, 1, 1, 0); 
-
-  void* var_24 = __visc__createNodeND(0, var_24_node); 
-
-  __visc__edge(var_23, var_24, 1, 0, 0, 0); 
-  __visc__edge(var_23, var_24, 1, 1, 1, 0); 
-  __visc__bindIn(var_24, 82, 2, 0); 
-  __visc__bindIn(var_24, 83, 3, 0); 
-
-  void* var_25 = __visc__createNodeND(0, var_25_node); 
-
-  __visc__edge(var_24, var_25, 1, 0, 0, 0); 
-  __visc__edge(var_24, var_25, 1, 1, 1, 0); 
-  __visc__bindIn(var_25, 84, 2, 0); 
-  __visc__bindIn(var_25, 85, 3, 0); 
-  __visc__bindIn(var_25, 86, 4, 0); 
-  __visc__bindIn(var_25, 87, 5, 0); 
-  __visc__bindIn(var_25, 88, 6, 0); 
-  __visc__bindIn(var_25, 89, 7, 0); 
-  __visc__bindIn(var_25, 90, 8, 0); 
-  __visc__bindIn(var_25, 91, 9, 0); 
-
-  void* var_26 = __visc__createNodeND(0, var_26_node); 
-
-  __visc__edge(var_25, var_26, 1, 0, 0, 0); 
-  __visc__edge(var_25, var_26, 1, 1, 1, 0); 
-
-  void* var_27 = __visc__createNodeND(0, var_27_node); 
-
-  __visc__edge(var_26, var_27, 1, 0, 0, 0); 
-  __visc__edge(var_26, var_27, 1, 1, 1, 0); 
-  __visc__bindIn(var_27, 92, 2, 0); 
-  __visc__bindIn(var_27, 93, 3, 0); 
-
-  void* var_28 = __visc__createNodeND(0, var_28_node); 
-
-  __visc__edge(var_27, var_28, 1, 0, 0, 0); 
-  __visc__edge(var_27, var_28, 1, 1, 1, 0); 
-  __visc__bindIn(var_28, 94, 2, 0); 
-  __visc__bindIn(var_28, 95, 3, 0); 
-  __visc__bindIn(var_28, 96, 4, 0); 
-  __visc__bindIn(var_28, 97, 5, 0); 
-  __visc__bindIn(var_28, 98, 6, 0); 
-  __visc__bindIn(var_28, 99, 7, 0); 
-  __visc__bindIn(var_28, 100, 8, 0); 
-  __visc__bindIn(var_28, 101, 9, 0); 
-
-  void* var_29 = __visc__createNodeND(0, var_29_node); 
-
-  __visc__edge(var_28, var_29, 1, 0, 0, 0); 
-  __visc__edge(var_28, var_29, 1, 1, 1, 0); 
-
-  void* var_30 = __visc__createNodeND(0, var_30_node); 
-
-  __visc__edge(var_29, var_30, 1, 0, 0, 0); 
-  __visc__edge(var_29, var_30, 1, 1, 1, 0); 
-  __visc__bindIn(var_30, 102, 2, 0); 
-  __visc__bindIn(var_30, 103, 3, 0); 
-
-  void* var_31 = __visc__createNodeND(0, var_31_node); 
-
-  __visc__edge(var_30, var_31, 1, 0, 0, 0); 
-  __visc__edge(var_30, var_31, 1, 1, 1, 0); 
-  __visc__bindIn(var_31, 104, 2, 0); 
-  __visc__bindIn(var_31, 105, 3, 0); 
-  __visc__bindIn(var_31, 106, 4, 0); 
-  __visc__bindIn(var_31, 107, 5, 0); 
-  __visc__bindIn(var_31, 108, 6, 0); 
-  __visc__bindIn(var_31, 109, 7, 0); 
-  __visc__bindIn(var_31, 110, 8, 0); 
-  __visc__bindIn(var_31, 111, 9, 0); 
-
-  void* var_32 = __visc__createNodeND(0, var_32_node); 
-
-  __visc__edge(var_31, var_32, 1, 0, 0, 0); 
-  __visc__edge(var_31, var_32, 1, 1, 1, 0); 
-
-  void* var_33 = __visc__createNodeND(0, var_33_node); 
-
-  __visc__edge(var_32, var_33, 1, 0, 0, 0); 
-  __visc__edge(var_32, var_33, 1, 1, 1, 0); 
-  __visc__bindIn(var_33, 112, 2, 0); 
-  __visc__bindIn(var_33, 113, 3, 0); 
-
-  void* var_34 = __visc__createNodeND(0, var_34_node); 
-
-  __visc__edge(var_33, var_34, 1, 0, 0, 0); 
-  __visc__edge(var_33, var_34, 1, 1, 1, 0); 
-  __visc__bindIn(var_34, 114, 2, 0); 
-  __visc__bindIn(var_34, 115, 3, 0); 
-  __visc__bindIn(var_34, 116, 4, 0); 
-  __visc__bindIn(var_34, 117, 5, 0); 
-  __visc__bindIn(var_34, 118, 6, 0); 
-  __visc__bindIn(var_34, 119, 7, 0); 
-  __visc__bindIn(var_34, 120, 8, 0); 
-  __visc__bindIn(var_34, 121, 9, 0); 
-
-  void* var_35 = __visc__createNodeND(0, var_35_node); 
-
-  __visc__edge(var_34, var_35, 1, 0, 0, 0); 
-  __visc__edge(var_34, var_35, 1, 1, 1, 0); 
-
-  void* var_36 = __visc__createNodeND(0, var_36_node); 
-
-  __visc__edge(var_35, var_36, 1, 0, 0, 0); 
-  __visc__edge(var_35, var_36, 1, 1, 1, 0); 
-  __visc__bindIn(var_36, 122, 2, 0); 
-  __visc__bindIn(var_36, 123, 3, 0); 
-
-  void* var_37 = __visc__createNodeND(0, var_37_node); 
-
-  __visc__edge(var_36, var_37, 1, 0, 0, 0); 
-  __visc__edge(var_36, var_37, 1, 1, 1, 0); 
-  __visc__bindIn(var_37, 124, 2, 0); 
-  __visc__bindIn(var_37, 125, 3, 0); 
-  __visc__bindIn(var_37, 126, 4, 0); 
-  __visc__bindIn(var_37, 127, 5, 0); 
-  __visc__bindIn(var_37, 128, 6, 0); 
-  __visc__bindIn(var_37, 129, 7, 0); 
-  __visc__bindIn(var_37, 130, 8, 0); 
-  __visc__bindIn(var_37, 131, 9, 0); 
-
-  void* var_38 = __visc__createNodeND(0, var_38_node); 
-
-  __visc__edge(var_37, var_38, 1, 0, 0, 0); 
-  __visc__edge(var_37, var_38, 1, 1, 1, 0); 
-
-  void* var_39 = __visc__createNodeND(0, var_39_node); 
-
-  __visc__edge(var_38, var_39, 1, 0, 0, 0); 
-  __visc__edge(var_38, var_39, 1, 1, 1, 0); 
-  __visc__bindIn(var_39, 132, 2, 0); 
-  __visc__bindIn(var_39, 133, 3, 0); 
-
-  void* var_40 = __visc__createNodeND(0, var_40_node); 
-
-  __visc__edge(var_39, var_40, 1, 0, 0, 0); 
-  __visc__edge(var_39, var_40, 1, 1, 1, 0); 
-  __visc__bindIn(var_40, 134, 2, 0); 
-  __visc__bindIn(var_40, 135, 3, 0); 
-  __visc__bindIn(var_40, 136, 4, 0); 
-  __visc__bindIn(var_40, 137, 5, 0); 
-  __visc__bindIn(var_40, 138, 6, 0); 
-  __visc__bindIn(var_40, 139, 7, 0); 
-  __visc__bindIn(var_40, 140, 8, 0); 
-  __visc__bindIn(var_40, 141, 9, 0); 
-
-  void* var_41 = __visc__createNodeND(0, var_41_node); 
-
-  __visc__edge(var_40, var_41, 1, 0, 0, 0); 
-  __visc__edge(var_40, var_41, 1, 1, 1, 0); 
-
-  void* var_42 = __visc__createNodeND(0, var_42_node); 
-
-  __visc__edge(var_41, var_42, 1, 0, 0, 0); 
-  __visc__edge(var_41, var_42, 1, 1, 1, 0); 
-  __visc__bindIn(var_42, 142, 2, 0); 
-  __visc__bindIn(var_42, 143, 3, 0); 
-
-  void* var_43 = __visc__createNodeND(0, var_43_node); 
-
-  __visc__edge(var_42, var_43, 1, 0, 0, 0); 
-  __visc__edge(var_42, var_43, 1, 1, 1, 0); 
-  __visc__bindIn(var_43, 144, 2, 0); 
-  __visc__bindIn(var_43, 145, 3, 0); 
-  __visc__bindIn(var_43, 146, 4, 0); 
-  __visc__bindIn(var_43, 147, 5, 0); 
-  __visc__bindIn(var_43, 148, 6, 0); 
-  __visc__bindIn(var_43, 149, 7, 0); 
-  __visc__bindIn(var_43, 150, 8, 0); 
-  __visc__bindIn(var_43, 151, 9, 0); 
-
-  void* var_44 = __visc__createNodeND(0, var_44_node); 
-
-  __visc__edge(var_43, var_44, 1, 0, 0, 0); 
-  __visc__edge(var_43, var_44, 1, 1, 1, 0); 
-
-  void* var_45 = __visc__createNodeND(0, var_45_node); 
-
-  __visc__edge(var_44, var_45, 1, 0, 0, 0); 
-  __visc__edge(var_44, var_45, 1, 1, 1, 0); 
-  __visc__bindIn(var_45, 152, 2, 0); 
-  __visc__bindIn(var_45, 153, 3, 0); 
-
-  void* var_46 = __visc__createNodeND(0, var_46_node); 
-
-  __visc__edge(var_45, var_46, 1, 0, 0, 0); 
-  __visc__edge(var_45, var_46, 1, 1, 1, 0); 
-  __visc__bindIn(var_46, 154, 2, 0); 
-  __visc__bindIn(var_46, 155, 3, 0); 
-  __visc__bindIn(var_46, 156, 4, 0); 
-  __visc__bindIn(var_46, 157, 5, 0); 
-  __visc__bindIn(var_46, 158, 6, 0); 
-  __visc__bindIn(var_46, 159, 7, 0); 
-  __visc__bindIn(var_46, 160, 8, 0); 
-  __visc__bindIn(var_46, 161, 9, 0); 
-
-  void* var_47 = __visc__createNodeND(0, var_47_node); 
-
-  __visc__edge(var_46, var_47, 1, 0, 0, 0); 
-  __visc__edge(var_46, var_47, 1, 1, 1, 0); 
-
-  void* var_48 = __visc__createNodeND(0, var_48_node); 
-
-  __visc__edge(var_47, var_48, 1, 0, 0, 0); 
-  __visc__edge(var_47, var_48, 1, 1, 1, 0); 
-  __visc__bindIn(var_48, 162, 2, 0); 
-  __visc__bindIn(var_48, 163, 3, 0); 
-
-  void* var_49 = __visc__createNodeND(0, var_49_node); 
-
-  __visc__edge(var_48, var_49, 1, 0, 0, 0); 
-  __visc__edge(var_48, var_49, 1, 1, 1, 0); 
-  __visc__bindIn(var_49, 164, 2, 0); 
-  __visc__bindIn(var_49, 165, 3, 0); 
-  __visc__bindIn(var_49, 166, 4, 0); 
-  __visc__bindIn(var_49, 167, 5, 0); 
-  __visc__bindIn(var_49, 168, 6, 0); 
-  __visc__bindIn(var_49, 169, 7, 0); 
-  __visc__bindIn(var_49, 170, 8, 0); 
-  __visc__bindIn(var_49, 171, 9, 0); 
-
-  void* var_50 = __visc__createNodeND(0, var_50_node); 
-
-  __visc__edge(var_49, var_50, 1, 0, 0, 0); 
-  __visc__edge(var_49, var_50, 1, 1, 1, 0); 
-
-  void* var_51 = __visc__createNodeND(0, var_51_node); 
-
-  __visc__edge(var_50, var_51, 1, 0, 0, 0); 
-  __visc__edge(var_50, var_51, 1, 1, 1, 0); 
-  __visc__bindIn(var_51, 172, 2, 0); 
-  __visc__bindIn(var_51, 173, 3, 0); 
-
-  void* var_52 = __visc__createNodeND(0, var_52_node); 
-
-  __visc__edge(var_51, var_52, 1, 0, 0, 0); 
-  __visc__edge(var_51, var_52, 1, 1, 1, 0); 
-  __visc__bindIn(var_52, 174, 2, 0); 
-  __visc__bindIn(var_52, 175, 3, 0); 
-  __visc__bindIn(var_52, 176, 4, 0); 
-  __visc__bindIn(var_52, 177, 5, 0); 
-  __visc__bindIn(var_52, 178, 6, 0); 
-  __visc__bindIn(var_52, 179, 7, 0); 
-  __visc__bindIn(var_52, 180, 8, 0); 
-  __visc__bindIn(var_52, 181, 9, 0); 
-
-  void* var_53 = __visc__createNodeND(0, var_53_node); 
-
-  __visc__edge(var_52, var_53, 1, 0, 0, 0); 
-  __visc__edge(var_52, var_53, 1, 1, 1, 0); 
-
-  void* var_54 = __visc__createNodeND(0, var_54_node); 
-
-  __visc__edge(var_53, var_54, 1, 0, 0, 0); 
-  __visc__edge(var_53, var_54, 1, 1, 1, 0); 
-  __visc__bindIn(var_54, 182, 2, 0); 
-  __visc__bindIn(var_54, 183, 3, 0); 
-
-  void* var_55 = __visc__createNodeND(0, var_55_node); 
-
-  __visc__edge(var_54, var_55, 1, 0, 0, 0); 
-  __visc__edge(var_54, var_55, 1, 1, 1, 0); 
-  __visc__bindIn(var_55, 184, 2, 0); 
-  __visc__bindIn(var_55, 185, 3, 0); 
-  __visc__bindIn(var_55, 186, 4, 0); 
-  __visc__bindIn(var_55, 187, 5, 0); 
-  __visc__bindIn(var_55, 188, 6, 0); 
-  __visc__bindIn(var_55, 189, 7, 0); 
-  __visc__bindIn(var_55, 190, 8, 0); 
-  __visc__bindIn(var_55, 191, 9, 0); 
-
-  void* var_56 = __visc__createNodeND(0, var_56_node); 
-
-  __visc__edge(var_55, var_56, 1, 0, 0, 0); 
-  __visc__edge(var_55, var_56, 1, 1, 1, 0); 
-
-  void* var_57 = __visc__createNodeND(0, var_57_node); 
-
-  __visc__edge(var_56, var_57, 1, 0, 0, 0); 
-  __visc__edge(var_56, var_57, 1, 1, 1, 0); 
-  __visc__bindIn(var_57, 192, 2, 0); 
-  __visc__bindIn(var_57, 193, 3, 0); 
-
-  void* var_58 = __visc__createNodeND(0, var_58_node); 
-
-  __visc__edge(var_57, var_58, 1, 0, 0, 0); 
-  __visc__edge(var_57, var_58, 1, 1, 1, 0); 
-  __visc__bindIn(var_58, 194, 2, 0); 
-  __visc__bindIn(var_58, 195, 3, 0); 
-  __visc__bindIn(var_58, 196, 4, 0); 
-  __visc__bindIn(var_58, 197, 5, 0); 
-  __visc__bindIn(var_58, 198, 6, 0); 
-  __visc__bindIn(var_58, 199, 7, 0); 
-  __visc__bindIn(var_58, 200, 8, 0); 
-  __visc__bindIn(var_58, 201, 9, 0); 
-
-  void* var_59 = __visc__createNodeND(0, var_59_node); 
-
-  __visc__edge(var_58, var_59, 1, 0, 0, 0); 
-  __visc__edge(var_58, var_59, 1, 1, 1, 0); 
-
-  void* var_60 = __visc__createNodeND(0, var_60_node); 
-
-  __visc__edge(var_59, var_60, 1, 0, 0, 0); 
-  __visc__edge(var_59, var_60, 1, 1, 1, 0); 
-  __visc__bindIn(var_60, 202, 2, 0); 
-  __visc__bindIn(var_60, 203, 3, 0); 
-
-  void* var_61 = __visc__createNodeND(0, var_61_node); 
-
-  __visc__edge(var_60, var_61, 1, 0, 0, 0); 
-  __visc__edge(var_60, var_61, 1, 1, 1, 0); 
-  __visc__bindIn(var_61, 204, 2, 0); 
-  __visc__bindIn(var_61, 205, 3, 0); 
-  __visc__bindIn(var_61, 206, 4, 0); 
-  __visc__bindIn(var_61, 207, 5, 0); 
-  __visc__bindIn(var_61, 208, 6, 0); 
-  __visc__bindIn(var_61, 209, 7, 0); 
-  __visc__bindIn(var_61, 210, 8, 0); 
-  __visc__bindIn(var_61, 211, 9, 0); 
-
-  void* var_62 = __visc__createNodeND(0, var_62_node); 
-
-  __visc__edge(var_61, var_62, 1, 0, 0, 0); 
-  __visc__edge(var_61, var_62, 1, 1, 1, 0); 
-
-  void* var_63 = __visc__createNodeND(0, var_63_node); 
-
-  __visc__edge(var_62, var_63, 1, 0, 0, 0); 
-  __visc__edge(var_62, var_63, 1, 1, 1, 0); 
-  __visc__bindIn(var_63, 212, 2, 0); 
-  __visc__bindIn(var_63, 213, 3, 0); 
-
-  void* var_64 = __visc__createNodeND(0, var_64_node); 
-
-  __visc__edge(var_63, var_64, 1, 0, 0, 0); 
-  __visc__edge(var_63, var_64, 1, 1, 1, 0); 
-  __visc__bindIn(var_64, 214, 2, 0); 
-  __visc__bindIn(var_64, 215, 3, 0); 
-  __visc__bindIn(var_64, 216, 4, 0); 
-  __visc__bindIn(var_64, 217, 5, 0); 
-  __visc__bindIn(var_64, 218, 6, 0); 
-  __visc__bindIn(var_64, 219, 7, 0); 
-  __visc__bindIn(var_64, 220, 8, 0); 
-  __visc__bindIn(var_64, 221, 9, 0); 
-
-  void* var_65 = __visc__createNodeND(0, var_65_node); 
-
-  __visc__edge(var_64, var_65, 1, 0, 0, 0); 
-  __visc__edge(var_64, var_65, 1, 1, 1, 0); 
-
-  void* var_66 = __visc__createNodeND(0, var_66_node); 
-
-  __visc__edge(var_65, var_66, 1, 0, 0, 0); 
-  __visc__edge(var_65, var_66, 1, 1, 1, 0); 
-  __visc__bindIn(var_66, 222, 2, 0); 
-  __visc__bindIn(var_66, 223, 3, 0); 
-
-  void* var_67 = __visc__createNodeND(0, var_67_node); 
-
-  __visc__edge(var_66, var_67, 1, 0, 0, 0); 
-  __visc__edge(var_66, var_67, 1, 1, 1, 0); 
-  __visc__bindIn(var_67, 224, 2, 0); 
-  __visc__bindIn(var_67, 225, 3, 0); 
-  __visc__bindIn(var_67, 226, 4, 0); 
-  __visc__bindIn(var_67, 227, 5, 0); 
-  __visc__bindIn(var_67, 228, 6, 0); 
-  __visc__bindIn(var_67, 229, 7, 0); 
-  __visc__bindIn(var_67, 230, 8, 0); 
-  __visc__bindIn(var_67, 231, 9, 0); 
-
-  void* var_68 = __visc__createNodeND(0, var_68_node); 
-
-  __visc__edge(var_67, var_68, 1, 0, 0, 0); 
-  __visc__edge(var_67, var_68, 1, 1, 1, 0); 
-
-  void* var_69 = __visc__createNodeND(0, var_69_node); 
-
-  __visc__edge(var_68, var_69, 1, 0, 0, 0); 
-  __visc__edge(var_68, var_69, 1, 1, 1, 0); 
-  __visc__bindIn(var_69, 232, 2, 0); 
-  __visc__bindIn(var_69, 233, 3, 0); 
-
-  void* var_70 = __visc__createNodeND(0, var_70_node); 
-
-  __visc__edge(var_69, var_70, 1, 0, 0, 0); 
-  __visc__edge(var_69, var_70, 1, 1, 1, 0); 
-  __visc__bindIn(var_70, 234, 2, 0); 
-  __visc__bindIn(var_70, 235, 3, 0); 
-  __visc__bindIn(var_70, 236, 4, 0); 
-  __visc__bindIn(var_70, 237, 5, 0); 
-  __visc__bindIn(var_70, 238, 6, 0); 
-  __visc__bindIn(var_70, 239, 7, 0); 
-  __visc__bindIn(var_70, 240, 8, 0); 
-  __visc__bindIn(var_70, 241, 9, 0); 
-
-  void* var_71 = __visc__createNodeND(0, var_71_node); 
-
-  __visc__edge(var_70, var_71, 1, 0, 0, 0); 
-  __visc__edge(var_70, var_71, 1, 1, 1, 0); 
-
-  void* var_72 = __visc__createNodeND(0, var_72_node); 
-
-  __visc__edge(var_71, var_72, 1, 0, 0, 0); 
-  __visc__edge(var_71, var_72, 1, 1, 1, 0); 
-  __visc__bindIn(var_72, 242, 2, 0); 
-  __visc__bindIn(var_72, 243, 3, 0); 
-
-  void* var_73 = __visc__createNodeND(0, var_73_node); 
-
-  __visc__edge(var_72, var_73, 1, 0, 0, 0); 
-  __visc__edge(var_72, var_73, 1, 1, 1, 0); 
-  __visc__bindIn(var_73, 244, 2, 0); 
-  __visc__bindIn(var_73, 245, 3, 0); 
-  __visc__bindIn(var_73, 246, 4, 0); 
-  __visc__bindIn(var_73, 247, 5, 0); 
-  __visc__bindIn(var_73, 248, 6, 0); 
-  __visc__bindIn(var_73, 249, 7, 0); 
-  __visc__bindIn(var_73, 250, 8, 0); 
-  __visc__bindIn(var_73, 251, 9, 0); 
-
-  void* var_74 = __visc__createNodeND(0, var_74_node); 
-
-  __visc__edge(var_73, var_74, 1, 0, 0, 0); 
-  __visc__edge(var_73, var_74, 1, 1, 1, 0); 
-
-  void* var_75 = __visc__createNodeND(0, var_75_node); 
-
-  __visc__edge(var_74, var_75, 1, 0, 0, 0); 
-  __visc__edge(var_74, var_75, 1, 1, 1, 0); 
-  __visc__bindIn(var_75, 252, 2, 0); 
-  __visc__bindIn(var_75, 253, 3, 0); 
-
-  void* var_76 = __visc__createNodeND(0, var_76_node); 
-
-  __visc__edge(var_75, var_76, 1, 0, 0, 0); 
-  __visc__edge(var_75, var_76, 1, 1, 1, 0); 
-  __visc__bindIn(var_76, 254, 2, 0); 
-  __visc__bindIn(var_76, 255, 3, 0); 
-  __visc__bindIn(var_76, 256, 4, 0); 
-  __visc__bindIn(var_76, 257, 5, 0); 
-  __visc__bindIn(var_76, 258, 6, 0); 
-  __visc__bindIn(var_76, 259, 7, 0); 
-  __visc__bindIn(var_76, 260, 8, 0); 
-  __visc__bindIn(var_76, 261, 9, 0); 
-
-  void* var_77 = __visc__createNodeND(0, var_77_node); 
-
-  __visc__edge(var_76, var_77, 1, 0, 0, 0); 
-  __visc__edge(var_76, var_77, 1, 1, 1, 0); 
-
-  void* var_78 = __visc__createNodeND(0, var_78_node); 
-
-  __visc__edge(var_77, var_78, 1, 0, 0, 0); 
-  __visc__edge(var_77, var_78, 1, 1, 1, 0); 
-  __visc__bindIn(var_78, 262, 2, 0); 
-  __visc__bindIn(var_78, 263, 3, 0); 
-
-  void* var_79 = __visc__createNodeND(0, var_79_node); 
-
-  __visc__edge(var_78, var_79, 1, 0, 0, 0); 
-  __visc__edge(var_78, var_79, 1, 1, 1, 0); 
-  __visc__bindIn(var_79, 264, 2, 0); 
-  __visc__bindIn(var_79, 265, 3, 0); 
-  __visc__bindIn(var_79, 266, 4, 0); 
-  __visc__bindIn(var_79, 267, 5, 0); 
-  __visc__bindIn(var_79, 268, 6, 0); 
-  __visc__bindIn(var_79, 269, 7, 0); 
-  __visc__bindIn(var_79, 270, 8, 0); 
-  __visc__bindIn(var_79, 271, 9, 0); 
-
-  void* var_80 = __visc__createNodeND(0, var_80_node); 
-
-  __visc__edge(var_79, var_80, 1, 0, 0, 0); 
-  __visc__edge(var_79, var_80, 1, 1, 1, 0); 
-
-  void* var_81 = __visc__createNodeND(0, var_81_node); 
-
-  __visc__edge(var_80, var_81, 1, 0, 0, 0); 
-  __visc__edge(var_80, var_81, 1, 1, 1, 0); 
-
-  void* var_82 = __visc__createNodeND(0, var_82_node); 
-
-  __visc__edge(var_81, var_82, 1, 0, 0, 0); 
-  __visc__edge(var_81, var_82, 1, 1, 1, 0); 
-  __visc__bindIn(var_82, 272, 2, 0); 
-  __visc__bindIn(var_82, 273, 3, 0); 
-
-  void* var_83 = __visc__createNodeND(0, var_83_node); 
-
-  __visc__edge(var_82, var_83, 1, 0, 0, 0); 
-  __visc__edge(var_82, var_83, 1, 1, 1, 0); 
-  __visc__bindIn(var_83, 274, 2, 0); 
-  __visc__bindIn(var_83, 275, 3, 0); 
-
-  void* var_84 = __visc__createNodeND(0, var_84_node); 
-
-  __visc__edge(var_83, var_84, 1, 0, 0, 0); 
-  __visc__edge(var_83, var_84, 1, 1, 1, 0); 
-
-  __visc__bindOut(var_84, 0, 0, 0); 
-  __visc__bindOut(var_84, 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* batch_normalization_1_gamma; 
-  size_t batch_normalization_1_gamma_bytes; 
-  void* batch_normalization_1_beta; 
-  size_t batch_normalization_1_beta_bytes; 
-  void* batch_normalization_1_mean; 
-  size_t batch_normalization_1_mean_bytes; 
-  void* batch_normalization_1_variance; 
-  size_t batch_normalization_1_variance_bytes; 
-  void* depthwise_conv2d_1_w; 
-  size_t depthwise_conv2d_1_w_bytes; 
-  void* batch_normalization_2_gamma; 
-  size_t batch_normalization_2_gamma_bytes; 
-  void* batch_normalization_2_beta; 
-  size_t batch_normalization_2_beta_bytes; 
-  void* batch_normalization_2_mean; 
-  size_t batch_normalization_2_mean_bytes; 
-  void* batch_normalization_2_variance; 
-  size_t batch_normalization_2_variance_bytes; 
-  void* conv2d_2_w; 
-  size_t conv2d_2_w_bytes; 
-  void* batch_normalization_3_gamma; 
-  size_t batch_normalization_3_gamma_bytes; 
-  void* batch_normalization_3_beta; 
-  size_t batch_normalization_3_beta_bytes; 
-  void* batch_normalization_3_mean; 
-  size_t batch_normalization_3_mean_bytes; 
-  void* batch_normalization_3_variance; 
-  size_t batch_normalization_3_variance_bytes; 
-  void* depthwise_conv2d_2_w; 
-  size_t depthwise_conv2d_2_w_bytes; 
-  void* batch_normalization_4_gamma; 
-  size_t batch_normalization_4_gamma_bytes; 
-  void* batch_normalization_4_beta; 
-  size_t batch_normalization_4_beta_bytes; 
-  void* batch_normalization_4_mean; 
-  size_t batch_normalization_4_mean_bytes; 
-  void* batch_normalization_4_variance; 
-  size_t batch_normalization_4_variance_bytes; 
-  void* conv2d_3_w; 
-  size_t conv2d_3_w_bytes; 
-  void* batch_normalization_5_gamma; 
-  size_t batch_normalization_5_gamma_bytes; 
-  void* batch_normalization_5_beta; 
-  size_t batch_normalization_5_beta_bytes; 
-  void* batch_normalization_5_mean; 
-  size_t batch_normalization_5_mean_bytes; 
-  void* batch_normalization_5_variance; 
-  size_t batch_normalization_5_variance_bytes; 
-  void* depthwise_conv2d_3_w; 
-  size_t depthwise_conv2d_3_w_bytes; 
-  void* batch_normalization_6_gamma; 
-  size_t batch_normalization_6_gamma_bytes; 
-  void* batch_normalization_6_beta; 
-  size_t batch_normalization_6_beta_bytes; 
-  void* batch_normalization_6_mean; 
-  size_t batch_normalization_6_mean_bytes; 
-  void* batch_normalization_6_variance; 
-  size_t batch_normalization_6_variance_bytes; 
-  void* conv2d_4_w; 
-  size_t conv2d_4_w_bytes; 
-  void* batch_normalization_7_gamma; 
-  size_t batch_normalization_7_gamma_bytes; 
-  void* batch_normalization_7_beta; 
-  size_t batch_normalization_7_beta_bytes; 
-  void* batch_normalization_7_mean; 
-  size_t batch_normalization_7_mean_bytes; 
-  void* batch_normalization_7_variance; 
-  size_t batch_normalization_7_variance_bytes; 
-  void* depthwise_conv2d_4_w; 
-  size_t depthwise_conv2d_4_w_bytes; 
-  void* batch_normalization_8_gamma; 
-  size_t batch_normalization_8_gamma_bytes; 
-  void* batch_normalization_8_beta; 
-  size_t batch_normalization_8_beta_bytes; 
-  void* batch_normalization_8_mean; 
-  size_t batch_normalization_8_mean_bytes; 
-  void* batch_normalization_8_variance; 
-  size_t batch_normalization_8_variance_bytes; 
-  void* conv2d_5_w; 
-  size_t conv2d_5_w_bytes; 
-  void* batch_normalization_9_gamma; 
-  size_t batch_normalization_9_gamma_bytes; 
-  void* batch_normalization_9_beta; 
-  size_t batch_normalization_9_beta_bytes; 
-  void* batch_normalization_9_mean; 
-  size_t batch_normalization_9_mean_bytes; 
-  void* batch_normalization_9_variance; 
-  size_t batch_normalization_9_variance_bytes; 
-  void* depthwise_conv2d_5_w; 
-  size_t depthwise_conv2d_5_w_bytes; 
-  void* batch_normalization_10_gamma; 
-  size_t batch_normalization_10_gamma_bytes; 
-  void* batch_normalization_10_beta; 
-  size_t batch_normalization_10_beta_bytes; 
-  void* batch_normalization_10_mean; 
-  size_t batch_normalization_10_mean_bytes; 
-  void* batch_normalization_10_variance; 
-  size_t batch_normalization_10_variance_bytes; 
-  void* conv2d_6_w; 
-  size_t conv2d_6_w_bytes; 
-  void* batch_normalization_11_gamma; 
-  size_t batch_normalization_11_gamma_bytes; 
-  void* batch_normalization_11_beta; 
-  size_t batch_normalization_11_beta_bytes; 
-  void* batch_normalization_11_mean; 
-  size_t batch_normalization_11_mean_bytes; 
-  void* batch_normalization_11_variance; 
-  size_t batch_normalization_11_variance_bytes; 
-  void* depthwise_conv2d_6_w; 
-  size_t depthwise_conv2d_6_w_bytes; 
-  void* batch_normalization_12_gamma; 
-  size_t batch_normalization_12_gamma_bytes; 
-  void* batch_normalization_12_beta; 
-  size_t batch_normalization_12_beta_bytes; 
-  void* batch_normalization_12_mean; 
-  size_t batch_normalization_12_mean_bytes; 
-  void* batch_normalization_12_variance; 
-  size_t batch_normalization_12_variance_bytes; 
-  void* conv2d_7_w; 
-  size_t conv2d_7_w_bytes; 
-  void* batch_normalization_13_gamma; 
-  size_t batch_normalization_13_gamma_bytes; 
-  void* batch_normalization_13_beta; 
-  size_t batch_normalization_13_beta_bytes; 
-  void* batch_normalization_13_mean; 
-  size_t batch_normalization_13_mean_bytes; 
-  void* batch_normalization_13_variance; 
-  size_t batch_normalization_13_variance_bytes; 
-  void* depthwise_conv2d_7_w; 
-  size_t depthwise_conv2d_7_w_bytes; 
-  void* batch_normalization_14_gamma; 
-  size_t batch_normalization_14_gamma_bytes; 
-  void* batch_normalization_14_beta; 
-  size_t batch_normalization_14_beta_bytes; 
-  void* batch_normalization_14_mean; 
-  size_t batch_normalization_14_mean_bytes; 
-  void* batch_normalization_14_variance; 
-  size_t batch_normalization_14_variance_bytes; 
-  void* conv2d_8_w; 
-  size_t conv2d_8_w_bytes; 
-  void* batch_normalization_15_gamma; 
-  size_t batch_normalization_15_gamma_bytes; 
-  void* batch_normalization_15_beta; 
-  size_t batch_normalization_15_beta_bytes; 
-  void* batch_normalization_15_mean; 
-  size_t batch_normalization_15_mean_bytes; 
-  void* batch_normalization_15_variance; 
-  size_t batch_normalization_15_variance_bytes; 
-  void* depthwise_conv2d_8_w; 
-  size_t depthwise_conv2d_8_w_bytes; 
-  void* batch_normalization_16_gamma; 
-  size_t batch_normalization_16_gamma_bytes; 
-  void* batch_normalization_16_beta; 
-  size_t batch_normalization_16_beta_bytes; 
-  void* batch_normalization_16_mean; 
-  size_t batch_normalization_16_mean_bytes; 
-  void* batch_normalization_16_variance; 
-  size_t batch_normalization_16_variance_bytes; 
-  void* conv2d_9_w; 
-  size_t conv2d_9_w_bytes; 
-  void* batch_normalization_17_gamma; 
-  size_t batch_normalization_17_gamma_bytes; 
-  void* batch_normalization_17_beta; 
-  size_t batch_normalization_17_beta_bytes; 
-  void* batch_normalization_17_mean; 
-  size_t batch_normalization_17_mean_bytes; 
-  void* batch_normalization_17_variance; 
-  size_t batch_normalization_17_variance_bytes; 
-  void* depthwise_conv2d_9_w; 
-  size_t depthwise_conv2d_9_w_bytes; 
-  void* batch_normalization_18_gamma; 
-  size_t batch_normalization_18_gamma_bytes; 
-  void* batch_normalization_18_beta; 
-  size_t batch_normalization_18_beta_bytes; 
-  void* batch_normalization_18_mean; 
-  size_t batch_normalization_18_mean_bytes; 
-  void* batch_normalization_18_variance; 
-  size_t batch_normalization_18_variance_bytes; 
-  void* conv2d_10_w; 
-  size_t conv2d_10_w_bytes; 
-  void* batch_normalization_19_gamma; 
-  size_t batch_normalization_19_gamma_bytes; 
-  void* batch_normalization_19_beta; 
-  size_t batch_normalization_19_beta_bytes; 
-  void* batch_normalization_19_mean; 
-  size_t batch_normalization_19_mean_bytes; 
-  void* batch_normalization_19_variance; 
-  size_t batch_normalization_19_variance_bytes; 
-  void* depthwise_conv2d_10_w; 
-  size_t depthwise_conv2d_10_w_bytes; 
-  void* batch_normalization_20_gamma; 
-  size_t batch_normalization_20_gamma_bytes; 
-  void* batch_normalization_20_beta; 
-  size_t batch_normalization_20_beta_bytes; 
-  void* batch_normalization_20_mean; 
-  size_t batch_normalization_20_mean_bytes; 
-  void* batch_normalization_20_variance; 
-  size_t batch_normalization_20_variance_bytes; 
-  void* conv2d_11_w; 
-  size_t conv2d_11_w_bytes; 
-  void* batch_normalization_21_gamma; 
-  size_t batch_normalization_21_gamma_bytes; 
-  void* batch_normalization_21_beta; 
-  size_t batch_normalization_21_beta_bytes; 
-  void* batch_normalization_21_mean; 
-  size_t batch_normalization_21_mean_bytes; 
-  void* batch_normalization_21_variance; 
-  size_t batch_normalization_21_variance_bytes; 
-  void* depthwise_conv2d_11_w; 
-  size_t depthwise_conv2d_11_w_bytes; 
-  void* batch_normalization_22_gamma; 
-  size_t batch_normalization_22_gamma_bytes; 
-  void* batch_normalization_22_beta; 
-  size_t batch_normalization_22_beta_bytes; 
-  void* batch_normalization_22_mean; 
-  size_t batch_normalization_22_mean_bytes; 
-  void* batch_normalization_22_variance; 
-  size_t batch_normalization_22_variance_bytes; 
-  void* conv2d_12_w; 
-  size_t conv2d_12_w_bytes; 
-  void* batch_normalization_23_gamma; 
-  size_t batch_normalization_23_gamma_bytes; 
-  void* batch_normalization_23_beta; 
-  size_t batch_normalization_23_beta_bytes; 
-  void* batch_normalization_23_mean; 
-  size_t batch_normalization_23_mean_bytes; 
-  void* batch_normalization_23_variance; 
-  size_t batch_normalization_23_variance_bytes; 
-  void* depthwise_conv2d_12_w; 
-  size_t depthwise_conv2d_12_w_bytes; 
-  void* batch_normalization_24_gamma; 
-  size_t batch_normalization_24_gamma_bytes; 
-  void* batch_normalization_24_beta; 
-  size_t batch_normalization_24_beta_bytes; 
-  void* batch_normalization_24_mean; 
-  size_t batch_normalization_24_mean_bytes; 
-  void* batch_normalization_24_variance; 
-  size_t batch_normalization_24_variance_bytes; 
-  void* conv2d_13_w; 
-  size_t conv2d_13_w_bytes; 
-  void* batch_normalization_25_gamma; 
-  size_t batch_normalization_25_gamma_bytes; 
-  void* batch_normalization_25_beta; 
-  size_t batch_normalization_25_beta_bytes; 
-  void* batch_normalization_25_mean; 
-  size_t batch_normalization_25_mean_bytes; 
-  void* batch_normalization_25_variance; 
-  size_t batch_normalization_25_variance_bytes; 
-  void* depthwise_conv2d_13_w; 
-  size_t depthwise_conv2d_13_w_bytes; 
-  void* batch_normalization_26_gamma; 
-  size_t batch_normalization_26_gamma_bytes; 
-  void* batch_normalization_26_beta; 
-  size_t batch_normalization_26_beta_bytes; 
-  void* batch_normalization_26_mean; 
-  size_t batch_normalization_26_mean_bytes; 
-  void* batch_normalization_26_variance; 
-  size_t batch_normalization_26_variance_bytes; 
-  void* conv2d_14_w; 
-  size_t conv2d_14_w_bytes; 
-  void* batch_normalization_27_gamma; 
-  size_t batch_normalization_27_gamma_bytes; 
-  void* batch_normalization_27_beta; 
-  size_t batch_normalization_27_beta_bytes; 
-  void* batch_normalization_27_mean; 
-  size_t batch_normalization_27_mean_bytes; 
-  void* batch_normalization_27_variance; 
-  size_t batch_normalization_27_variance_bytes; 
-  void* dense_1_w; 
-  size_t dense_1_w_bytes; 
-  void* dense_1_b; 
-  size_t dense_1_b_bytes; 
-
-  struct ret_t r; 
-}
-RootIn;
-
-int main(){ 
-
-std::string dir_prefix = std::string("data/mobilenet_quant/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-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(), 0,1,1,1024,10); 
-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); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-uint8_t* labels = readLabels(labels_path.c_str(),10000); 
-
-__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->batch_normalization_1_gamma = batch_normalization_1_gamma; 
-args->batch_normalization_1_gamma_bytes = 0; 
-args->batch_normalization_1_beta = batch_normalization_1_beta; 
-args->batch_normalization_1_beta_bytes = 0; 
-args->batch_normalization_1_mean = batch_normalization_1_mean; 
-args->batch_normalization_1_mean_bytes = 0; 
-args->batch_normalization_1_variance = batch_normalization_1_variance; 
-args->batch_normalization_1_variance_bytes = 0; 
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w; 
-args->depthwise_conv2d_1_w_bytes = 0; 
-args->batch_normalization_2_gamma = batch_normalization_2_gamma; 
-args->batch_normalization_2_gamma_bytes = 0; 
-args->batch_normalization_2_beta = batch_normalization_2_beta; 
-args->batch_normalization_2_beta_bytes = 0; 
-args->batch_normalization_2_mean = batch_normalization_2_mean; 
-args->batch_normalization_2_mean_bytes = 0; 
-args->batch_normalization_2_variance = batch_normalization_2_variance; 
-args->batch_normalization_2_variance_bytes = 0; 
-args->conv2d_2_w = conv2d_2_w; 
-args->conv2d_2_w_bytes = 0; 
-args->batch_normalization_3_gamma = batch_normalization_3_gamma; 
-args->batch_normalization_3_gamma_bytes = 0; 
-args->batch_normalization_3_beta = batch_normalization_3_beta; 
-args->batch_normalization_3_beta_bytes = 0; 
-args->batch_normalization_3_mean = batch_normalization_3_mean; 
-args->batch_normalization_3_mean_bytes = 0; 
-args->batch_normalization_3_variance = batch_normalization_3_variance; 
-args->batch_normalization_3_variance_bytes = 0; 
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w; 
-args->depthwise_conv2d_2_w_bytes = 0; 
-args->batch_normalization_4_gamma = batch_normalization_4_gamma; 
-args->batch_normalization_4_gamma_bytes = 0; 
-args->batch_normalization_4_beta = batch_normalization_4_beta; 
-args->batch_normalization_4_beta_bytes = 0; 
-args->batch_normalization_4_mean = batch_normalization_4_mean; 
-args->batch_normalization_4_mean_bytes = 0; 
-args->batch_normalization_4_variance = batch_normalization_4_variance; 
-args->batch_normalization_4_variance_bytes = 0; 
-args->conv2d_3_w = conv2d_3_w; 
-args->conv2d_3_w_bytes = 0; 
-args->batch_normalization_5_gamma = batch_normalization_5_gamma; 
-args->batch_normalization_5_gamma_bytes = 0; 
-args->batch_normalization_5_beta = batch_normalization_5_beta; 
-args->batch_normalization_5_beta_bytes = 0; 
-args->batch_normalization_5_mean = batch_normalization_5_mean; 
-args->batch_normalization_5_mean_bytes = 0; 
-args->batch_normalization_5_variance = batch_normalization_5_variance; 
-args->batch_normalization_5_variance_bytes = 0; 
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w; 
-args->depthwise_conv2d_3_w_bytes = 0; 
-args->batch_normalization_6_gamma = batch_normalization_6_gamma; 
-args->batch_normalization_6_gamma_bytes = 0; 
-args->batch_normalization_6_beta = batch_normalization_6_beta; 
-args->batch_normalization_6_beta_bytes = 0; 
-args->batch_normalization_6_mean = batch_normalization_6_mean; 
-args->batch_normalization_6_mean_bytes = 0; 
-args->batch_normalization_6_variance = batch_normalization_6_variance; 
-args->batch_normalization_6_variance_bytes = 0; 
-args->conv2d_4_w = conv2d_4_w; 
-args->conv2d_4_w_bytes = 0; 
-args->batch_normalization_7_gamma = batch_normalization_7_gamma; 
-args->batch_normalization_7_gamma_bytes = 0; 
-args->batch_normalization_7_beta = batch_normalization_7_beta; 
-args->batch_normalization_7_beta_bytes = 0; 
-args->batch_normalization_7_mean = batch_normalization_7_mean; 
-args->batch_normalization_7_mean_bytes = 0; 
-args->batch_normalization_7_variance = batch_normalization_7_variance; 
-args->batch_normalization_7_variance_bytes = 0; 
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w; 
-args->depthwise_conv2d_4_w_bytes = 0; 
-args->batch_normalization_8_gamma = batch_normalization_8_gamma; 
-args->batch_normalization_8_gamma_bytes = 0; 
-args->batch_normalization_8_beta = batch_normalization_8_beta; 
-args->batch_normalization_8_beta_bytes = 0; 
-args->batch_normalization_8_mean = batch_normalization_8_mean; 
-args->batch_normalization_8_mean_bytes = 0; 
-args->batch_normalization_8_variance = batch_normalization_8_variance; 
-args->batch_normalization_8_variance_bytes = 0; 
-args->conv2d_5_w = conv2d_5_w; 
-args->conv2d_5_w_bytes = 0; 
-args->batch_normalization_9_gamma = batch_normalization_9_gamma; 
-args->batch_normalization_9_gamma_bytes = 0; 
-args->batch_normalization_9_beta = batch_normalization_9_beta; 
-args->batch_normalization_9_beta_bytes = 0; 
-args->batch_normalization_9_mean = batch_normalization_9_mean; 
-args->batch_normalization_9_mean_bytes = 0; 
-args->batch_normalization_9_variance = batch_normalization_9_variance; 
-args->batch_normalization_9_variance_bytes = 0; 
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w; 
-args->depthwise_conv2d_5_w_bytes = 0; 
-args->batch_normalization_10_gamma = batch_normalization_10_gamma; 
-args->batch_normalization_10_gamma_bytes = 0; 
-args->batch_normalization_10_beta = batch_normalization_10_beta; 
-args->batch_normalization_10_beta_bytes = 0; 
-args->batch_normalization_10_mean = batch_normalization_10_mean; 
-args->batch_normalization_10_mean_bytes = 0; 
-args->batch_normalization_10_variance = batch_normalization_10_variance; 
-args->batch_normalization_10_variance_bytes = 0; 
-args->conv2d_6_w = conv2d_6_w; 
-args->conv2d_6_w_bytes = 0; 
-args->batch_normalization_11_gamma = batch_normalization_11_gamma; 
-args->batch_normalization_11_gamma_bytes = 0; 
-args->batch_normalization_11_beta = batch_normalization_11_beta; 
-args->batch_normalization_11_beta_bytes = 0; 
-args->batch_normalization_11_mean = batch_normalization_11_mean; 
-args->batch_normalization_11_mean_bytes = 0; 
-args->batch_normalization_11_variance = batch_normalization_11_variance; 
-args->batch_normalization_11_variance_bytes = 0; 
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w; 
-args->depthwise_conv2d_6_w_bytes = 0; 
-args->batch_normalization_12_gamma = batch_normalization_12_gamma; 
-args->batch_normalization_12_gamma_bytes = 0; 
-args->batch_normalization_12_beta = batch_normalization_12_beta; 
-args->batch_normalization_12_beta_bytes = 0; 
-args->batch_normalization_12_mean = batch_normalization_12_mean; 
-args->batch_normalization_12_mean_bytes = 0; 
-args->batch_normalization_12_variance = batch_normalization_12_variance; 
-args->batch_normalization_12_variance_bytes = 0; 
-args->conv2d_7_w = conv2d_7_w; 
-args->conv2d_7_w_bytes = 0; 
-args->batch_normalization_13_gamma = batch_normalization_13_gamma; 
-args->batch_normalization_13_gamma_bytes = 0; 
-args->batch_normalization_13_beta = batch_normalization_13_beta; 
-args->batch_normalization_13_beta_bytes = 0; 
-args->batch_normalization_13_mean = batch_normalization_13_mean; 
-args->batch_normalization_13_mean_bytes = 0; 
-args->batch_normalization_13_variance = batch_normalization_13_variance; 
-args->batch_normalization_13_variance_bytes = 0; 
-args->depthwise_conv2d_7_w = depthwise_conv2d_7_w; 
-args->depthwise_conv2d_7_w_bytes = 0; 
-args->batch_normalization_14_gamma = batch_normalization_14_gamma; 
-args->batch_normalization_14_gamma_bytes = 0; 
-args->batch_normalization_14_beta = batch_normalization_14_beta; 
-args->batch_normalization_14_beta_bytes = 0; 
-args->batch_normalization_14_mean = batch_normalization_14_mean; 
-args->batch_normalization_14_mean_bytes = 0; 
-args->batch_normalization_14_variance = batch_normalization_14_variance; 
-args->batch_normalization_14_variance_bytes = 0; 
-args->conv2d_8_w = conv2d_8_w; 
-args->conv2d_8_w_bytes = 0; 
-args->batch_normalization_15_gamma = batch_normalization_15_gamma; 
-args->batch_normalization_15_gamma_bytes = 0; 
-args->batch_normalization_15_beta = batch_normalization_15_beta; 
-args->batch_normalization_15_beta_bytes = 0; 
-args->batch_normalization_15_mean = batch_normalization_15_mean; 
-args->batch_normalization_15_mean_bytes = 0; 
-args->batch_normalization_15_variance = batch_normalization_15_variance; 
-args->batch_normalization_15_variance_bytes = 0; 
-args->depthwise_conv2d_8_w = depthwise_conv2d_8_w; 
-args->depthwise_conv2d_8_w_bytes = 0; 
-args->batch_normalization_16_gamma = batch_normalization_16_gamma; 
-args->batch_normalization_16_gamma_bytes = 0; 
-args->batch_normalization_16_beta = batch_normalization_16_beta; 
-args->batch_normalization_16_beta_bytes = 0; 
-args->batch_normalization_16_mean = batch_normalization_16_mean; 
-args->batch_normalization_16_mean_bytes = 0; 
-args->batch_normalization_16_variance = batch_normalization_16_variance; 
-args->batch_normalization_16_variance_bytes = 0; 
-args->conv2d_9_w = conv2d_9_w; 
-args->conv2d_9_w_bytes = 0; 
-args->batch_normalization_17_gamma = batch_normalization_17_gamma; 
-args->batch_normalization_17_gamma_bytes = 0; 
-args->batch_normalization_17_beta = batch_normalization_17_beta; 
-args->batch_normalization_17_beta_bytes = 0; 
-args->batch_normalization_17_mean = batch_normalization_17_mean; 
-args->batch_normalization_17_mean_bytes = 0; 
-args->batch_normalization_17_variance = batch_normalization_17_variance; 
-args->batch_normalization_17_variance_bytes = 0; 
-args->depthwise_conv2d_9_w = depthwise_conv2d_9_w; 
-args->depthwise_conv2d_9_w_bytes = 0; 
-args->batch_normalization_18_gamma = batch_normalization_18_gamma; 
-args->batch_normalization_18_gamma_bytes = 0; 
-args->batch_normalization_18_beta = batch_normalization_18_beta; 
-args->batch_normalization_18_beta_bytes = 0; 
-args->batch_normalization_18_mean = batch_normalization_18_mean; 
-args->batch_normalization_18_mean_bytes = 0; 
-args->batch_normalization_18_variance = batch_normalization_18_variance; 
-args->batch_normalization_18_variance_bytes = 0; 
-args->conv2d_10_w = conv2d_10_w; 
-args->conv2d_10_w_bytes = 0; 
-args->batch_normalization_19_gamma = batch_normalization_19_gamma; 
-args->batch_normalization_19_gamma_bytes = 0; 
-args->batch_normalization_19_beta = batch_normalization_19_beta; 
-args->batch_normalization_19_beta_bytes = 0; 
-args->batch_normalization_19_mean = batch_normalization_19_mean; 
-args->batch_normalization_19_mean_bytes = 0; 
-args->batch_normalization_19_variance = batch_normalization_19_variance; 
-args->batch_normalization_19_variance_bytes = 0; 
-args->depthwise_conv2d_10_w = depthwise_conv2d_10_w; 
-args->depthwise_conv2d_10_w_bytes = 0; 
-args->batch_normalization_20_gamma = batch_normalization_20_gamma; 
-args->batch_normalization_20_gamma_bytes = 0; 
-args->batch_normalization_20_beta = batch_normalization_20_beta; 
-args->batch_normalization_20_beta_bytes = 0; 
-args->batch_normalization_20_mean = batch_normalization_20_mean; 
-args->batch_normalization_20_mean_bytes = 0; 
-args->batch_normalization_20_variance = batch_normalization_20_variance; 
-args->batch_normalization_20_variance_bytes = 0; 
-args->conv2d_11_w = conv2d_11_w; 
-args->conv2d_11_w_bytes = 0; 
-args->batch_normalization_21_gamma = batch_normalization_21_gamma; 
-args->batch_normalization_21_gamma_bytes = 0; 
-args->batch_normalization_21_beta = batch_normalization_21_beta; 
-args->batch_normalization_21_beta_bytes = 0; 
-args->batch_normalization_21_mean = batch_normalization_21_mean; 
-args->batch_normalization_21_mean_bytes = 0; 
-args->batch_normalization_21_variance = batch_normalization_21_variance; 
-args->batch_normalization_21_variance_bytes = 0; 
-args->depthwise_conv2d_11_w = depthwise_conv2d_11_w; 
-args->depthwise_conv2d_11_w_bytes = 0; 
-args->batch_normalization_22_gamma = batch_normalization_22_gamma; 
-args->batch_normalization_22_gamma_bytes = 0; 
-args->batch_normalization_22_beta = batch_normalization_22_beta; 
-args->batch_normalization_22_beta_bytes = 0; 
-args->batch_normalization_22_mean = batch_normalization_22_mean; 
-args->batch_normalization_22_mean_bytes = 0; 
-args->batch_normalization_22_variance = batch_normalization_22_variance; 
-args->batch_normalization_22_variance_bytes = 0; 
-args->conv2d_12_w = conv2d_12_w; 
-args->conv2d_12_w_bytes = 0; 
-args->batch_normalization_23_gamma = batch_normalization_23_gamma; 
-args->batch_normalization_23_gamma_bytes = 0; 
-args->batch_normalization_23_beta = batch_normalization_23_beta; 
-args->batch_normalization_23_beta_bytes = 0; 
-args->batch_normalization_23_mean = batch_normalization_23_mean; 
-args->batch_normalization_23_mean_bytes = 0; 
-args->batch_normalization_23_variance = batch_normalization_23_variance; 
-args->batch_normalization_23_variance_bytes = 0; 
-args->depthwise_conv2d_12_w = depthwise_conv2d_12_w; 
-args->depthwise_conv2d_12_w_bytes = 0; 
-args->batch_normalization_24_gamma = batch_normalization_24_gamma; 
-args->batch_normalization_24_gamma_bytes = 0; 
-args->batch_normalization_24_beta = batch_normalization_24_beta; 
-args->batch_normalization_24_beta_bytes = 0; 
-args->batch_normalization_24_mean = batch_normalization_24_mean; 
-args->batch_normalization_24_mean_bytes = 0; 
-args->batch_normalization_24_variance = batch_normalization_24_variance; 
-args->batch_normalization_24_variance_bytes = 0; 
-args->conv2d_13_w = conv2d_13_w; 
-args->conv2d_13_w_bytes = 0; 
-args->batch_normalization_25_gamma = batch_normalization_25_gamma; 
-args->batch_normalization_25_gamma_bytes = 0; 
-args->batch_normalization_25_beta = batch_normalization_25_beta; 
-args->batch_normalization_25_beta_bytes = 0; 
-args->batch_normalization_25_mean = batch_normalization_25_mean; 
-args->batch_normalization_25_mean_bytes = 0; 
-args->batch_normalization_25_variance = batch_normalization_25_variance; 
-args->batch_normalization_25_variance_bytes = 0; 
-args->depthwise_conv2d_13_w = depthwise_conv2d_13_w; 
-args->depthwise_conv2d_13_w_bytes = 0; 
-args->batch_normalization_26_gamma = batch_normalization_26_gamma; 
-args->batch_normalization_26_gamma_bytes = 0; 
-args->batch_normalization_26_beta = batch_normalization_26_beta; 
-args->batch_normalization_26_beta_bytes = 0; 
-args->batch_normalization_26_mean = batch_normalization_26_mean; 
-args->batch_normalization_26_mean_bytes = 0; 
-args->batch_normalization_26_variance = batch_normalization_26_variance; 
-args->batch_normalization_26_variance_bytes = 0; 
-args->conv2d_14_w = conv2d_14_w; 
-args->conv2d_14_w_bytes = 0; 
-args->batch_normalization_27_gamma = batch_normalization_27_gamma; 
-args->batch_normalization_27_gamma_bytes = 0; 
-args->batch_normalization_27_beta = batch_normalization_27_beta; 
-args->batch_normalization_27_beta_bytes = 0; 
-args->batch_normalization_27_mean = batch_normalization_27_mean; 
-args->batch_normalization_27_mean_bytes = 0; 
-args->batch_normalization_27_variance = batch_normalization_27_variance; 
-args->batch_normalization_27_variance_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; 
-
-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/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin
deleted file mode 100644
index bb1eb07a8e262d2f4d941578fd4c19d6a90c7562..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin
deleted file mode 100644
index 931c8925b89f363a41d3cf81483bde60abafba61..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin
deleted file mode 100644
index 633bdc9fd4a9ef052ca8b6ab488a156002e3d4b5..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
deleted file mode 100644
index f92c73f59eb5eb35ca94e3ce006e5f3c4f60ecef..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
+++ /dev/null
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin
deleted file mode 100644
index 5918477d3638e851c3fdfc47dc550cea3afa7d50..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin
deleted file mode 100644
index 6b3d705199383135bed811a6fdaa237d754487bd..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_mean.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_mean.bin
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_variance.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_variance.bin
deleted file mode 100644
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_beta.bin
deleted file mode 100644
index 8ade4cf080d7d3228e752d284ed500ba6300d261..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin
deleted file mode 100644
index 6dfb7c3833821b29f9230df806c4abc0c16a7b59..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin
+++ /dev/null
@@ -1,5 +0,0 @@
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m?¥Û@?P›L?ìÕ˜?'î?ò˜Y?Ä1?&±?$L?1¡¬?— G?ÚIw?˜ñ?z4? |K?ñN?,™@?a¦H?	dZ?ÈóY?s´N?Ÿ)ˆ?°yd?³ù£?†\?<èX?ŽåO?¬N?²ÚO?™?4aQ?Xy?
-)Ÿ?›^?7uF?(X?	hš?³?A3u?¸-“?«7P?=×a?œ‹C?ßøˆ?
qq?$ÚP?à߁?Šì¨?ö^?%œp?k
O?”Q?Šd?_G?­ˆ??ïÞ@?½œk?<öV?¬<R?°>?.jO?„Œ?2¬Q?¥ûª?µÊY?ÓÙD?L—f?EU?c²6?O©®?Z(H?‰Š?­KX?p¦T?‚Jm?…;?ÇŸŠ?¶€?ým?­Øp?¨@?~Ó^?;öC?/€[?ÃÑ©?zÅ‹?1éH?ìT>?p b?q9^?|	K?
-ÞS?ÑE?[ô[?;ï’?/0?¬`?°µZ?FuX?o@c?eÑn?ÛvL?
>š?êo”?ïèg?
-·q?2èF?AI™?j™=?7ÖZ?üó_?$ÆH?¹È_?øaW?jªƒ?ÅwT?^Ïm?•P?tZm?ns??°r^?]»U?@bk?†Ž?fŸ?ð'†?ñ´‘?ߤ?#Ñž?V'¢?[hM?fY]?
¤?’?q¼a?Ýþ[?g}¡?IJH?Š3”?ç\F?þ]?ÂS?w2D?—!V?¹IV?èõL?¡Œ_?øâ™?ÖxH?‘ùX?éH?+K8?†áV?ûR?–ß“?¶EI?˜'Z?Ñì^?¶~@?ö:–?ª¼_?
²ž?ÒÐ]?C¼·?!V?ÕK?Âc‘?9i”?XY?$¸D??Å©?V#E?>Ž`?!Z?Éñ˜?c¯U?Öæš?v9?jfX?ÌvŸ?3aV?ÍðR?qt‘?ü
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\ No newline at end of file
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_mean.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_mean.bin
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index 8899c2ad8395a98c752b1777095018cc90ca693b..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_variance.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_variance.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_beta.bin
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index 0f5fe8656435b28ec4b928af599b0a63915a651a..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_gamma.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_gamma.bin
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index c79d7d0b02b65ea9953bfd1fa164773f96e5ade0..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_mean.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_13_mean.bin
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-conv  
-
-activation  
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-
-activation  
-conv  
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-activation  
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-activation  
-conv  
-
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-conv  
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-conv  
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-activation  
-pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt
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index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
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@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
deleted file mode 100644
index c2a4a29509ad89724905c869ff900f8ecaa5bf8c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,10000,32,32,32,64,32,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,10000,64,16,16,128,64,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,10000,128,16,16,128,128,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,10000,128,8,8,256,128,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,10000,256,8,8,256,256,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,10000,256,4,4,512,256,1,1
-NML31
-NML32
-NML33
-NML34
-NML35
-Conv15,10000,512,4,4,512,512,1,1
-NML36
-NML37
-NML38
-NML39
-NML40
-Conv17,10000,512,4,4,512,512,1,1
-NML41
-NML42
-NML43
-NML44
-NML45
-Conv19,10000,512,4,4,512,512,1,1
-NML46
-NML47
-NML48
-NML49
-NML50
-Conv21,10000,512,4,4,512,512,1,1
-NML51
-NML52
-NML53
-NML54
-NML55
-Conv23,10000,512,4,4,512,512,1,1
-NML56
-NML57
-NML58
-NML59
-NML60
-Conv25,10000,512,2,2,1024,512,1,1
-NML61
-NML62
-NML63
-NML64
-NML65
-Conv27,10000,1024,2,2,1024,1024,1,1
-NML66
-NML67
-NML68
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
deleted file mode 100644
index 8e18f2ec58cddb9ab0251229b1e908b23b71d6bc..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
+++ /dev/null
@@ -1,165 +0,0 @@
-#Conv1,1
-Conv1
-#NML1,1
-BatchNorm1
-#NML2,1
-Relu1
-#NML3,1
-Conv2
-#NML4,1
-BatchNorm2
-#NML5,1
-Relu2
-#Conv3,1
-Conv3
-#NML6,1
-BatchNorm3
-#NML7,1
-Relu3
-#NML8,1
-Conv4
-#NML9,1
-BatchNorm4
-#NML10,1
-Relu4
-#Conv5,1
-Conv5
-#NML11,1
-BatchNorm5
-#NML12,1
-Relu5
-#NML13,1
-Conv6
-#NML14,1
-BatchNorm6
-#NML15,1
-Relu6
-#Conv7,1
-Conv7
-#NML16,1
-BatchNorm7
-#NML17,1
-Relu7
-#NML18,1
-Conv8
-#NML19,1
-BatchNorm8
-#NML20,1
-Relu8
-#Conv9,1
-Conv9
-#NML21,1
-BatchNorm9
-#NML22,1
-Relu9
-#NML23,1
-Conv10
-#NML24,1
-BatchNorm10
-#NML25,1
-Relu10
-#Conv11,1
-Conv11
-#NML26,1
-BatchNorm11
-#NML27,1
-Relu11
-#NML28,1
-Conv12
-#NML29,1
-BatchNorm12
-#NML30,1
-Relu12
-#Conv13,1
-Conv13
-#NML31,1
-BatchNorm13
-#NML32,1
-Relu13
-#NML33,1
-Conv14
-#NML34,1
-BatchNorm14
-#NML35,1
-Relu14
-#Conv15,1
-Conv15
-#NML36,1
-BatchNorm15
-#NML37,1
-Relu15
-#NML38,1
-Conv16
-#NML39,1
-BatchNorm16
-#NML40,1
-Relu16
-#Conv17,1
-Conv17
-#NML41,1
-BatchNorm17
-#NML42,1
-Relu17
-#NML43,1
-Conv18
-#NML44,1
-BatchNorm18
-#NML45,1
-Relu18
-#Conv19,1
-Conv19
-#NML46,1
-BatchNorm19
-#NML47,1
-Relu19
-#NML48,1
-Conv20
-#NML49,1
-BatchNorm20
-#NML50,1
-Relu20
-#Conv21,1
-conv21
-#NML51,1
-BatchNorm21
-#NML52,1
-Relu21
-#NML53,1
-Conv22
-#NML54,1
-BatchNorm22
-#NML55,1
-Relu22
-#Conv23,1
-Conv23
-#NML56,1
-BatchNorm23
-#NML57,1
-Relu23
-#NML58,1
-Conv24
-#NML59,1
-BatchNorm24
-#NML60,1
-Relu24
-#Conv25,1
-Conv25
-#NML61,1
-BatchNorm25
-#NML62,1
-Relu25
-#NML63,1
-Conv26
-#NML64,1
-BatchNorm26
-#NML65,1
-Relu26
-#Conv27,1
-Conv27
-#NML66,1
-BatchNorm27
-#NML67,1
-Relu27
-#NML68,1
-Pool1
-FC1,10000,1024,1024,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
deleted file mode 100644
index 146bc640cc4b1e8da65e3e7bb6cb5c7f2a007399..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
+++ /dev/null
@@ -1,420 +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 = 10000; 
-int batch_size = 10000; 
-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("data/mobilenet_quant/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-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(), 0,1,1,1024,10); 
-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 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, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9); 
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-void* var_2 = tensorRelu(var_1); 
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-void* var_5 = tensorRelu(var_4); 
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9); 
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-void* var_8 = tensorRelu(var_7); 
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-void* var_11 = tensorRelu(var_10); 
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9); 
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-void* var_14 = tensorRelu(var_13); 
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-void* var_17 = tensorRelu(var_16); 
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9); 
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-void* var_20 = tensorRelu(var_19); 
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-void* var_23 = tensorRelu(var_22); 
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9); 
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-void* var_26 = tensorRelu(var_25); 
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-void* var_29 = tensorRelu(var_28); 
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9); 
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-void* var_32 = tensorRelu(var_31); 
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-void* var_35 = tensorRelu(var_34); 
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9); 
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-void* var_38 = tensorRelu(var_37); 
-void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-void* var_41 = tensorRelu(var_40); 
-void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9); 
-void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-void* var_44 = tensorRelu(var_43); 
-void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-void* var_47 = tensorRelu(var_46); 
-void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9); 
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-void* var_50 = tensorRelu(var_49); 
-void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-void* var_53 = tensorRelu(var_52); 
-void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9); 
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-void* var_56 = tensorRelu(var_55); 
-void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-void* var_59 = tensorRelu(var_58); 
-void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9); 
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-void* var_62 = tensorRelu(var_61); 
-void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-void* var_65 = tensorRelu(var_64); 
-void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9); 
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-void* var_68 = tensorRelu(var_67); 
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-void* var_71 = tensorRelu(var_70); 
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9); 
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-void* var_74 = tensorRelu(var_73); 
-void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-void* var_77 = tensorRelu(var_76); 
-void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9); 
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-void* var_80 = tensorRelu(var_79); 
-void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2); 
-void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9); 
-void* var_83 = tensorSoftmax(var_82); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_83); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-}
-
-dumpExecutionAccuracies(); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt
deleted file mode 100644
index 9ea66b8485dc19a8f2f9abfc5981e023f22ce521..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.9892114 2.126797 -2.19630692005 1.34758170414  0.0  0.0  -60.892750473 51.9925691605 
-0.0 5.71354155397 -0.931772116065 1.07742589378   0.0  0.0 -6.51858950329 6.81084251881 
-0.0 4.93213940287 -0.531654466152 0.57537904036   0.0  0.0  -4.48263123512 3.96730119753 
-0.0 4.10326339769 -0.362340988219 0.407691390038   0.0  0.0  -4.04261828327 3.8867793293 
-0.0 5.38322130251 -0.313120054901 0.293576799393   0.0  0.0  -5.92146921539 4.33867932415 
-0.0 4.31673815441 -0.232992478013 0.258029025793   0.0  0.0  -4.20778994751 3.93243697071 
-0.0 5.8304081068 -0.202337772191 0.189983081758   0.0  0.0  -6.29828691578 4.84813511753 
-0.0 4.44641780996 -0.174427356511 0.176958308667  0.0  0.0   -4.34791088581 3.61443646955 
-0.0 4.5180956049 -0.145467961878 0.15256431669   0.0  0.0   -3.02877027559 2.94873657799 
-0.0 6.34857563496 -0.130258745223 0.135582433432   0.0  0.0  -4.22931008053 3.53150463724 
-0.0 5.22100311041 -0.119001727596 0.125363747835   0.0  0.0  -4.03820378017 4.00400940704 
-0.0 5.73249834776 -0.108397216856 0.116256686077    0.0  0.0  -3.31110151148 4.46293323326 
-0.0 7.24049821186 -0.0862374496162 0.0885944995135   0.0  0.0  -4.17543139458 6.2043294754 
-0.0 7.81395883465 -0.0681302513927 0.0700202777982    0.0  0.0  -10.9205664234 2.64429125786 
-0.0 2.86920666504 -0.223010196954 0.14426593782 -0.1654396 0.23336112 -12.2459499588 23.8053251343
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
deleted file mode 100644
index 25aec9bde3bc1aac157e2acc368dcddf866e455d..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
+++ /dev/null
@@ -1,413 +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("data/mobilenet_quant/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_7_w_path =  dir_prefix + std::string("depthwise_conv2d_7_w.bin"); 
-void* depthwise_conv2d_7_w =  readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_14_gamma_path =  dir_prefix + std::string("batch_normalization_14_gamma.bin"); 
-void* batch_normalization_14_gamma =  readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_beta_path =  dir_prefix + std::string("batch_normalization_14_beta.bin"); 
-void* batch_normalization_14_beta =  readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_mean_path =  dir_prefix + std::string("batch_normalization_14_mean.bin"); 
-void* batch_normalization_14_mean =  readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_14_variance_path =  dir_prefix + std::string("batch_normalization_14_variance.bin"); 
-void* batch_normalization_14_variance =  readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_15_gamma_path =  dir_prefix + std::string("batch_normalization_15_gamma.bin"); 
-void* batch_normalization_15_gamma =  readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_beta_path =  dir_prefix + std::string("batch_normalization_15_beta.bin"); 
-void* batch_normalization_15_beta =  readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_mean_path =  dir_prefix + std::string("batch_normalization_15_mean.bin"); 
-void* batch_normalization_15_mean =  readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_15_variance_path =  dir_prefix + std::string("batch_normalization_15_variance.bin"); 
-void* batch_normalization_15_variance =  readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_8_w_path =  dir_prefix + std::string("depthwise_conv2d_8_w.bin"); 
-void* depthwise_conv2d_8_w =  readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_16_gamma_path =  dir_prefix + std::string("batch_normalization_16_gamma.bin"); 
-void* batch_normalization_16_gamma =  readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_beta_path =  dir_prefix + std::string("batch_normalization_16_beta.bin"); 
-void* batch_normalization_16_beta =  readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_mean_path =  dir_prefix + std::string("batch_normalization_16_mean.bin"); 
-void* batch_normalization_16_mean =  readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_16_variance_path =  dir_prefix + std::string("batch_normalization_16_variance.bin"); 
-void* batch_normalization_16_variance =  readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_17_gamma_path =  dir_prefix + std::string("batch_normalization_17_gamma.bin"); 
-void* batch_normalization_17_gamma =  readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_beta_path =  dir_prefix + std::string("batch_normalization_17_beta.bin"); 
-void* batch_normalization_17_beta =  readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_mean_path =  dir_prefix + std::string("batch_normalization_17_mean.bin"); 
-void* batch_normalization_17_mean =  readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_17_variance_path =  dir_prefix + std::string("batch_normalization_17_variance.bin"); 
-void* batch_normalization_17_variance =  readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_9_w_path =  dir_prefix + std::string("depthwise_conv2d_9_w.bin"); 
-void* depthwise_conv2d_9_w =  readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_18_gamma_path =  dir_prefix + std::string("batch_normalization_18_gamma.bin"); 
-void* batch_normalization_18_gamma =  readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_beta_path =  dir_prefix + std::string("batch_normalization_18_beta.bin"); 
-void* batch_normalization_18_beta =  readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_mean_path =  dir_prefix + std::string("batch_normalization_18_mean.bin"); 
-void* batch_normalization_18_mean =  readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_18_variance_path =  dir_prefix + std::string("batch_normalization_18_variance.bin"); 
-void* batch_normalization_18_variance =  readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_19_gamma_path =  dir_prefix + std::string("batch_normalization_19_gamma.bin"); 
-void* batch_normalization_19_gamma =  readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_beta_path =  dir_prefix + std::string("batch_normalization_19_beta.bin"); 
-void* batch_normalization_19_beta =  readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_mean_path =  dir_prefix + std::string("batch_normalization_19_mean.bin"); 
-void* batch_normalization_19_mean =  readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_19_variance_path =  dir_prefix + std::string("batch_normalization_19_variance.bin"); 
-void* batch_normalization_19_variance =  readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_10_w_path =  dir_prefix + std::string("depthwise_conv2d_10_w.bin"); 
-void* depthwise_conv2d_10_w =  readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_20_gamma_path =  dir_prefix + std::string("batch_normalization_20_gamma.bin"); 
-void* batch_normalization_20_gamma =  readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_beta_path =  dir_prefix + std::string("batch_normalization_20_beta.bin"); 
-void* batch_normalization_20_beta =  readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_mean_path =  dir_prefix + std::string("batch_normalization_20_mean.bin"); 
-void* batch_normalization_20_mean =  readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_20_variance_path =  dir_prefix + std::string("batch_normalization_20_variance.bin"); 
-void* batch_normalization_20_variance =  readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_21_gamma_path =  dir_prefix + std::string("batch_normalization_21_gamma.bin"); 
-void* batch_normalization_21_gamma =  readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_beta_path =  dir_prefix + std::string("batch_normalization_21_beta.bin"); 
-void* batch_normalization_21_beta =  readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_mean_path =  dir_prefix + std::string("batch_normalization_21_mean.bin"); 
-void* batch_normalization_21_mean =  readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_21_variance_path =  dir_prefix + std::string("batch_normalization_21_variance.bin"); 
-void* batch_normalization_21_variance =  readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_11_w_path =  dir_prefix + std::string("depthwise_conv2d_11_w.bin"); 
-void* depthwise_conv2d_11_w =  readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_22_gamma_path =  dir_prefix + std::string("batch_normalization_22_gamma.bin"); 
-void* batch_normalization_22_gamma =  readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_beta_path =  dir_prefix + std::string("batch_normalization_22_beta.bin"); 
-void* batch_normalization_22_beta =  readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_mean_path =  dir_prefix + std::string("batch_normalization_22_mean.bin"); 
-void* batch_normalization_22_mean =  readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_22_variance_path =  dir_prefix + std::string("batch_normalization_22_variance.bin"); 
-void* batch_normalization_22_variance =  readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1); 
-std::string batch_normalization_23_gamma_path =  dir_prefix + std::string("batch_normalization_23_gamma.bin"); 
-void* batch_normalization_23_gamma =  readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_beta_path =  dir_prefix + std::string("batch_normalization_23_beta.bin"); 
-void* batch_normalization_23_beta =  readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_mean_path =  dir_prefix + std::string("batch_normalization_23_mean.bin"); 
-void* batch_normalization_23_mean =  readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_23_variance_path =  dir_prefix + std::string("batch_normalization_23_variance.bin"); 
-void* batch_normalization_23_variance =  readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1); 
-std::string depthwise_conv2d_12_w_path =  dir_prefix + std::string("depthwise_conv2d_12_w.bin"); 
-void* depthwise_conv2d_12_w =  readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3); 
-std::string batch_normalization_24_gamma_path =  dir_prefix + std::string("batch_normalization_24_gamma.bin"); 
-void* batch_normalization_24_gamma =  readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_beta_path =  dir_prefix + std::string("batch_normalization_24_beta.bin"); 
-void* batch_normalization_24_beta =  readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_mean_path =  dir_prefix + std::string("batch_normalization_24_mean.bin"); 
-void* batch_normalization_24_mean =  readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_24_variance_path =  dir_prefix + std::string("batch_normalization_24_variance.bin"); 
-void* batch_normalization_24_variance =  readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1); 
-std::string batch_normalization_25_gamma_path =  dir_prefix + std::string("batch_normalization_25_gamma.bin"); 
-void* batch_normalization_25_gamma =  readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_beta_path =  dir_prefix + std::string("batch_normalization_25_beta.bin"); 
-void* batch_normalization_25_beta =  readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_mean_path =  dir_prefix + std::string("batch_normalization_25_mean.bin"); 
-void* batch_normalization_25_mean =  readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_25_variance_path =  dir_prefix + std::string("batch_normalization_25_variance.bin"); 
-void* batch_normalization_25_variance =  readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1); 
-std::string depthwise_conv2d_13_w_path =  dir_prefix + std::string("depthwise_conv2d_13_w.bin"); 
-void* depthwise_conv2d_13_w =  readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3); 
-std::string batch_normalization_26_gamma_path =  dir_prefix + std::string("batch_normalization_26_gamma.bin"); 
-void* batch_normalization_26_gamma =  readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_beta_path =  dir_prefix + std::string("batch_normalization_26_beta.bin"); 
-void* batch_normalization_26_beta =  readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_mean_path =  dir_prefix + std::string("batch_normalization_26_mean.bin"); 
-void* batch_normalization_26_mean =  readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_26_variance_path =  dir_prefix + std::string("batch_normalization_26_variance.bin"); 
-void* batch_normalization_26_variance =  readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1); 
-std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1); 
-std::string batch_normalization_27_gamma_path =  dir_prefix + std::string("batch_normalization_27_gamma.bin"); 
-void* batch_normalization_27_gamma =  readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_beta_path =  dir_prefix + std::string("batch_normalization_27_beta.bin"); 
-void* batch_normalization_27_beta =  readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_mean_path =  dir_prefix + std::string("batch_normalization_27_mean.bin"); 
-void* batch_normalization_27_mean =  readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1); 
-std::string batch_normalization_27_variance_path =  dir_prefix + std::string("batch_normalization_27_variance.bin"); 
-void* batch_normalization_27_variance =  readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1); 
-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(), 0,1,1,1024,10); 
-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); 
-
-
-
-startMemTracking(); 
-
-int test_input_size = 10000; 
-int batch_size = 10000; 
-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, 1, 1, 1, 1, 1, 1); 
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-void* var_2 = tensorRelu(var_1); 
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-void* var_6 = tensorRelu(var_5); 
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-void* var_9 = tensorRelu(var_8); 
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-void* var_13 = tensorRelu(var_12); 
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-void* var_16 = tensorRelu(var_15); 
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-void* var_20 = tensorRelu(var_19); 
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-void* var_23 = tensorRelu(var_22); 
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-void* var_28 = tensorRelu(var_27); 
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-void* var_31 = tensorRelu(var_30); 
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-void* var_35 = tensorRelu(var_34); 
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-void* var_38 = tensorRelu(var_37); 
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-void* var_43 = tensorRelu(var_42); 
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-void* var_46 = tensorRelu(var_45); 
-void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512); 
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001); 
-void* var_50 = tensorRelu(var_49); 
-void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1); 
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001); 
-void* var_53 = tensorRelu(var_52); 
-void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512); 
-void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001); 
-void* var_57 = tensorRelu(var_56); 
-void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1); 
-void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001); 
-void* var_60 = tensorRelu(var_59); 
-void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512); 
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001); 
-void* var_65 = tensorRelu(var_64); 
-void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1); 
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001); 
-void* var_68 = tensorRelu(var_67); 
-void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512); 
-void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001); 
-void* var_72 = tensorRelu(var_71); 
-void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1); 
-void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001); 
-void* var_75 = tensorRelu(var_74); 
-void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512); 
-void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001); 
-void* var_79 = tensorRelu(var_78); 
-void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1); 
-void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001); 
-void* var_82 = tensorRelu(var_81); 
-void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512); 
-void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001); 
-void* var_87 = tensorRelu(var_86); 
-void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1); 
-void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001); 
-void* var_90 = tensorRelu(var_89); 
-void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024); 
-void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001); 
-void* var_94 = tensorRelu(var_93); 
-void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1); 
-void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001); 
-void* var_97 = tensorRelu(var_96); 
-void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2); 
-void* var_101 = tensorGemmGPU(var_99, dense_1_w); 
-void* var_102 = tensorAdd(var_101, dense_1_b); 
-void* var_103 = tensorSoftmax(var_102); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_103); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
deleted file mode 100644
index dc0c873c63333299981591cb5654cb38be9d4092..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
+++ /dev/null
@@ -1,1224 +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, 1, 1, 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, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __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, 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_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_8_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_9_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_11_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_12_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_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_14_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_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_17_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_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_convolution(t1, t2, 0, 0, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_20_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_21_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_23_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_24_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_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_26_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_27_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_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_29_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_30_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_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_32_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_33_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_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_35_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_36_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_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(5, t1, t2, t3, t4, t5, 0); 
-
-  void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_38_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_39_node(void* t1, size_t bytes_t1) { 
-  __visc__hint(visc::CUDNN_TARGET); 
-  __visc__attributes(1, t1, 0); 
-
-  void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_40_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_41_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_42_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* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes, 
-	  void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes, 
-	  void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes, 
-	  void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes, 
-	  void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes, 
-	  void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes, 
-	  void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes, 
-	  void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes, 
-	  void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes, 
-	  void* conv2d_2_w, size_t conv2d_2_w_bytes, 
-	  void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes, 
-	  void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes, 
-	  void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes, 
-	  void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes, 
-	  void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes, 
-	  void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes, 
-	  void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes, 
-	  void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes, 
-	  void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes, 
-	  void* conv2d_3_w, size_t conv2d_3_w_bytes, 
-	  void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes, 
-	  void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes, 
-	  void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes, 
-	  void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes, 
-	  void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes, 
-	  void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes, 
-	  void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes, 
-	  void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes, 
-	  void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes, 
-	  void* conv2d_4_w, size_t conv2d_4_w_bytes, 
-	  void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes, 
-	  void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes, 
-	  void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes, 
-	  void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes, 
-	  void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes, 
-	  void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes, 
-	  void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes, 
-	  void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes, 
-	  void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes, 
-	  void* conv2d_5_w, size_t conv2d_5_w_bytes, 
-	  void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes, 
-	  void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes, 
-	  void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes, 
-	  void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes, 
-	  void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes, 
-	  void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes, 
-	  void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes, 
-	  void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes, 
-	  void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes, 
-	  void* conv2d_6_w, size_t conv2d_6_w_bytes, 
-	  void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes, 
-	  void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes, 
-	  void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes, 
-	  void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes, 
-	  void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes, 
-	  void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes, 
-	  void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes, 
-	  void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes, 
-	  void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes, 
-	  void* conv2d_7_w, size_t conv2d_7_w_bytes, 
-	  void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes, 
-	  void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes, 
-	  void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes, 
-	  void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes, 
-	  void* dense_1_w, size_t dense_1_w_bytes, 
-	  void* dense_1_b, size_t dense_1_b_bytes){ 
-
-
-  __visc__hint(visc::CPU_TARGET); 
-  __visc__attributes(68, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, dense_1_w, dense_1_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); 
-  __visc__bindIn(var_1, 6, 4, 0); 
-  __visc__bindIn(var_1, 7, 5, 0); 
-  __visc__bindIn(var_1, 8, 6, 0); 
-  __visc__bindIn(var_1, 9, 7, 0); 
-  __visc__bindIn(var_1, 10, 8, 0); 
-  __visc__bindIn(var_1, 11, 9, 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, 12, 2, 0); 
-  __visc__bindIn(var_3, 13, 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, 14, 2, 0); 
-  __visc__bindIn(var_4, 15, 3, 0); 
-  __visc__bindIn(var_4, 16, 4, 0); 
-  __visc__bindIn(var_4, 17, 5, 0); 
-  __visc__bindIn(var_4, 18, 6, 0); 
-  __visc__bindIn(var_4, 19, 7, 0); 
-  __visc__bindIn(var_4, 20, 8, 0); 
-  __visc__bindIn(var_4, 21, 9, 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); 
-  __visc__bindIn(var_6, 22, 2, 0); 
-  __visc__bindIn(var_6, 23, 3, 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, 24, 2, 0); 
-  __visc__bindIn(var_7, 25, 3, 0); 
-  __visc__bindIn(var_7, 26, 4, 0); 
-  __visc__bindIn(var_7, 27, 5, 0); 
-  __visc__bindIn(var_7, 28, 6, 0); 
-  __visc__bindIn(var_7, 29, 7, 0); 
-  __visc__bindIn(var_7, 30, 8, 0); 
-  __visc__bindIn(var_7, 31, 9, 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); 
-
-  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); 
-  __visc__bindIn(var_9, 32, 2, 0); 
-  __visc__bindIn(var_9, 33, 3, 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, 34, 2, 0); 
-  __visc__bindIn(var_10, 35, 3, 0); 
-  __visc__bindIn(var_10, 36, 4, 0); 
-  __visc__bindIn(var_10, 37, 5, 0); 
-  __visc__bindIn(var_10, 38, 6, 0); 
-  __visc__bindIn(var_10, 39, 7, 0); 
-  __visc__bindIn(var_10, 40, 8, 0); 
-  __visc__bindIn(var_10, 41, 9, 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); 
-
-  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); 
-  __visc__bindIn(var_12, 42, 2, 0); 
-  __visc__bindIn(var_12, 43, 3, 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, 44, 2, 0); 
-  __visc__bindIn(var_13, 45, 3, 0); 
-  __visc__bindIn(var_13, 46, 4, 0); 
-  __visc__bindIn(var_13, 47, 5, 0); 
-  __visc__bindIn(var_13, 48, 6, 0); 
-  __visc__bindIn(var_13, 49, 7, 0); 
-  __visc__bindIn(var_13, 50, 8, 0); 
-  __visc__bindIn(var_13, 51, 9, 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); 
-
-  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, 52, 2, 0); 
-  __visc__bindIn(var_15, 53, 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); 
-  __visc__bindIn(var_16, 54, 2, 0); 
-  __visc__bindIn(var_16, 55, 3, 0); 
-  __visc__bindIn(var_16, 56, 4, 0); 
-  __visc__bindIn(var_16, 57, 5, 0); 
-  __visc__bindIn(var_16, 58, 6, 0); 
-  __visc__bindIn(var_16, 59, 7, 0); 
-  __visc__bindIn(var_16, 60, 8, 0); 
-  __visc__bindIn(var_16, 61, 9, 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); 
-
-  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, 62, 2, 0); 
-  __visc__bindIn(var_18, 63, 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__bindIn(var_19, 64, 2, 0); 
-  __visc__bindIn(var_19, 65, 3, 0); 
-  __visc__bindIn(var_19, 66, 4, 0); 
-  __visc__bindIn(var_19, 67, 5, 0); 
-  __visc__bindIn(var_19, 68, 6, 0); 
-  __visc__bindIn(var_19, 69, 7, 0); 
-  __visc__bindIn(var_19, 70, 8, 0); 
-  __visc__bindIn(var_19, 71, 9, 0); 
-
-  void* var_20 = __visc__createNodeND(0, var_20_node); 
-
-  __visc__edge(var_19, var_20, 1, 0, 0, 0); 
-  __visc__edge(var_19, var_20, 1, 1, 1, 0); 
-
-  void* var_21 = __visc__createNodeND(0, var_21_node); 
-
-  __visc__edge(var_20, var_21, 1, 0, 0, 0); 
-  __visc__edge(var_20, var_21, 1, 1, 1, 0); 
-  __visc__bindIn(var_21, 72, 2, 0); 
-  __visc__bindIn(var_21, 73, 3, 0); 
-
-  void* var_22 = __visc__createNodeND(0, var_22_node); 
-
-  __visc__edge(var_21, var_22, 1, 0, 0, 0); 
-  __visc__edge(var_21, var_22, 1, 1, 1, 0); 
-  __visc__bindIn(var_22, 74, 2, 0); 
-  __visc__bindIn(var_22, 75, 3, 0); 
-  __visc__bindIn(var_22, 76, 4, 0); 
-  __visc__bindIn(var_22, 77, 5, 0); 
-  __visc__bindIn(var_22, 78, 6, 0); 
-  __visc__bindIn(var_22, 79, 7, 0); 
-  __visc__bindIn(var_22, 80, 8, 0); 
-  __visc__bindIn(var_22, 81, 9, 0); 
-
-  void* var_23 = __visc__createNodeND(0, var_23_node); 
-
-  __visc__edge(var_22, var_23, 1, 0, 0, 0); 
-  __visc__edge(var_22, var_23, 1, 1, 1, 0); 
-
-  void* var_24 = __visc__createNodeND(0, var_24_node); 
-
-  __visc__edge(var_23, var_24, 1, 0, 0, 0); 
-  __visc__edge(var_23, var_24, 1, 1, 1, 0); 
-  __visc__bindIn(var_24, 82, 2, 0); 
-  __visc__bindIn(var_24, 83, 3, 0); 
-
-  void* var_25 = __visc__createNodeND(0, var_25_node); 
-
-  __visc__edge(var_24, var_25, 1, 0, 0, 0); 
-  __visc__edge(var_24, var_25, 1, 1, 1, 0); 
-  __visc__bindIn(var_25, 84, 2, 0); 
-  __visc__bindIn(var_25, 85, 3, 0); 
-  __visc__bindIn(var_25, 86, 4, 0); 
-  __visc__bindIn(var_25, 87, 5, 0); 
-  __visc__bindIn(var_25, 88, 6, 0); 
-  __visc__bindIn(var_25, 89, 7, 0); 
-  __visc__bindIn(var_25, 90, 8, 0); 
-  __visc__bindIn(var_25, 91, 9, 0); 
-
-  void* var_26 = __visc__createNodeND(0, var_26_node); 
-
-  __visc__edge(var_25, var_26, 1, 0, 0, 0); 
-  __visc__edge(var_25, var_26, 1, 1, 1, 0); 
-
-  void* var_27 = __visc__createNodeND(0, var_27_node); 
-
-  __visc__edge(var_26, var_27, 1, 0, 0, 0); 
-  __visc__edge(var_26, var_27, 1, 1, 1, 0); 
-  __visc__bindIn(var_27, 92, 2, 0); 
-  __visc__bindIn(var_27, 93, 3, 0); 
-
-  void* var_28 = __visc__createNodeND(0, var_28_node); 
-
-  __visc__edge(var_27, var_28, 1, 0, 0, 0); 
-  __visc__edge(var_27, var_28, 1, 1, 1, 0); 
-  __visc__bindIn(var_28, 94, 2, 0); 
-  __visc__bindIn(var_28, 95, 3, 0); 
-  __visc__bindIn(var_28, 96, 4, 0); 
-  __visc__bindIn(var_28, 97, 5, 0); 
-  __visc__bindIn(var_28, 98, 6, 0); 
-  __visc__bindIn(var_28, 99, 7, 0); 
-  __visc__bindIn(var_28, 100, 8, 0); 
-  __visc__bindIn(var_28, 101, 9, 0); 
-
-  void* var_29 = __visc__createNodeND(0, var_29_node); 
-
-  __visc__edge(var_28, var_29, 1, 0, 0, 0); 
-  __visc__edge(var_28, var_29, 1, 1, 1, 0); 
-
-  void* var_30 = __visc__createNodeND(0, var_30_node); 
-
-  __visc__edge(var_29, var_30, 1, 0, 0, 0); 
-  __visc__edge(var_29, var_30, 1, 1, 1, 0); 
-  __visc__bindIn(var_30, 102, 2, 0); 
-  __visc__bindIn(var_30, 103, 3, 0); 
-
-  void* var_31 = __visc__createNodeND(0, var_31_node); 
-
-  __visc__edge(var_30, var_31, 1, 0, 0, 0); 
-  __visc__edge(var_30, var_31, 1, 1, 1, 0); 
-  __visc__bindIn(var_31, 104, 2, 0); 
-  __visc__bindIn(var_31, 105, 3, 0); 
-  __visc__bindIn(var_31, 106, 4, 0); 
-  __visc__bindIn(var_31, 107, 5, 0); 
-  __visc__bindIn(var_31, 108, 6, 0); 
-  __visc__bindIn(var_31, 109, 7, 0); 
-  __visc__bindIn(var_31, 110, 8, 0); 
-  __visc__bindIn(var_31, 111, 9, 0); 
-
-  void* var_32 = __visc__createNodeND(0, var_32_node); 
-
-  __visc__edge(var_31, var_32, 1, 0, 0, 0); 
-  __visc__edge(var_31, var_32, 1, 1, 1, 0); 
-
-  void* var_33 = __visc__createNodeND(0, var_33_node); 
-
-  __visc__edge(var_32, var_33, 1, 0, 0, 0); 
-  __visc__edge(var_32, var_33, 1, 1, 1, 0); 
-  __visc__bindIn(var_33, 112, 2, 0); 
-  __visc__bindIn(var_33, 113, 3, 0); 
-
-  void* var_34 = __visc__createNodeND(0, var_34_node); 
-
-  __visc__edge(var_33, var_34, 1, 0, 0, 0); 
-  __visc__edge(var_33, var_34, 1, 1, 1, 0); 
-  __visc__bindIn(var_34, 114, 2, 0); 
-  __visc__bindIn(var_34, 115, 3, 0); 
-  __visc__bindIn(var_34, 116, 4, 0); 
-  __visc__bindIn(var_34, 117, 5, 0); 
-  __visc__bindIn(var_34, 118, 6, 0); 
-  __visc__bindIn(var_34, 119, 7, 0); 
-  __visc__bindIn(var_34, 120, 8, 0); 
-  __visc__bindIn(var_34, 121, 9, 0); 
-
-  void* var_35 = __visc__createNodeND(0, var_35_node); 
-
-  __visc__edge(var_34, var_35, 1, 0, 0, 0); 
-  __visc__edge(var_34, var_35, 1, 1, 1, 0); 
-
-  void* var_36 = __visc__createNodeND(0, var_36_node); 
-
-  __visc__edge(var_35, var_36, 1, 0, 0, 0); 
-  __visc__edge(var_35, var_36, 1, 1, 1, 0); 
-  __visc__bindIn(var_36, 122, 2, 0); 
-  __visc__bindIn(var_36, 123, 3, 0); 
-
-  void* var_37 = __visc__createNodeND(0, var_37_node); 
-
-  __visc__edge(var_36, var_37, 1, 0, 0, 0); 
-  __visc__edge(var_36, var_37, 1, 1, 1, 0); 
-  __visc__bindIn(var_37, 124, 2, 0); 
-  __visc__bindIn(var_37, 125, 3, 0); 
-  __visc__bindIn(var_37, 126, 4, 0); 
-  __visc__bindIn(var_37, 127, 5, 0); 
-  __visc__bindIn(var_37, 128, 6, 0); 
-  __visc__bindIn(var_37, 129, 7, 0); 
-  __visc__bindIn(var_37, 130, 8, 0); 
-  __visc__bindIn(var_37, 131, 9, 0); 
-
-  void* var_38 = __visc__createNodeND(0, var_38_node); 
-
-  __visc__edge(var_37, var_38, 1, 0, 0, 0); 
-  __visc__edge(var_37, var_38, 1, 1, 1, 0); 
-
-  void* var_39 = __visc__createNodeND(0, var_39_node); 
-
-  __visc__edge(var_38, var_39, 1, 0, 0, 0); 
-  __visc__edge(var_38, var_39, 1, 1, 1, 0); 
-
-  void* var_40 = __visc__createNodeND(0, var_40_node); 
-
-  __visc__edge(var_39, var_40, 1, 0, 0, 0); 
-  __visc__edge(var_39, var_40, 1, 1, 1, 0); 
-  __visc__bindIn(var_40, 132, 2, 0); 
-  __visc__bindIn(var_40, 133, 3, 0); 
-
-  void* var_41 = __visc__createNodeND(0, var_41_node); 
-
-  __visc__edge(var_40, var_41, 1, 0, 0, 0); 
-  __visc__edge(var_40, var_41, 1, 1, 1, 0); 
-  __visc__bindIn(var_41, 134, 2, 0); 
-  __visc__bindIn(var_41, 135, 3, 0); 
-
-  void* var_42 = __visc__createNodeND(0, var_42_node); 
-
-  __visc__edge(var_41, var_42, 1, 0, 0, 0); 
-  __visc__edge(var_41, var_42, 1, 1, 1, 0); 
-
-  __visc__bindOut(var_42, 0, 0, 0); 
-  __visc__bindOut(var_42, 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* batch_normalization_1_gamma; 
-  size_t batch_normalization_1_gamma_bytes; 
-  void* batch_normalization_1_beta; 
-  size_t batch_normalization_1_beta_bytes; 
-  void* batch_normalization_1_mean; 
-  size_t batch_normalization_1_mean_bytes; 
-  void* batch_normalization_1_variance; 
-  size_t batch_normalization_1_variance_bytes; 
-  void* depthwise_conv2d_1_w; 
-  size_t depthwise_conv2d_1_w_bytes; 
-  void* batch_normalization_2_gamma; 
-  size_t batch_normalization_2_gamma_bytes; 
-  void* batch_normalization_2_beta; 
-  size_t batch_normalization_2_beta_bytes; 
-  void* batch_normalization_2_mean; 
-  size_t batch_normalization_2_mean_bytes; 
-  void* batch_normalization_2_variance; 
-  size_t batch_normalization_2_variance_bytes; 
-  void* conv2d_2_w; 
-  size_t conv2d_2_w_bytes; 
-  void* batch_normalization_3_gamma; 
-  size_t batch_normalization_3_gamma_bytes; 
-  void* batch_normalization_3_beta; 
-  size_t batch_normalization_3_beta_bytes; 
-  void* batch_normalization_3_mean; 
-  size_t batch_normalization_3_mean_bytes; 
-  void* batch_normalization_3_variance; 
-  size_t batch_normalization_3_variance_bytes; 
-  void* depthwise_conv2d_2_w; 
-  size_t depthwise_conv2d_2_w_bytes; 
-  void* batch_normalization_4_gamma; 
-  size_t batch_normalization_4_gamma_bytes; 
-  void* batch_normalization_4_beta; 
-  size_t batch_normalization_4_beta_bytes; 
-  void* batch_normalization_4_mean; 
-  size_t batch_normalization_4_mean_bytes; 
-  void* batch_normalization_4_variance; 
-  size_t batch_normalization_4_variance_bytes; 
-  void* conv2d_3_w; 
-  size_t conv2d_3_w_bytes; 
-  void* batch_normalization_5_gamma; 
-  size_t batch_normalization_5_gamma_bytes; 
-  void* batch_normalization_5_beta; 
-  size_t batch_normalization_5_beta_bytes; 
-  void* batch_normalization_5_mean; 
-  size_t batch_normalization_5_mean_bytes; 
-  void* batch_normalization_5_variance; 
-  size_t batch_normalization_5_variance_bytes; 
-  void* depthwise_conv2d_3_w; 
-  size_t depthwise_conv2d_3_w_bytes; 
-  void* batch_normalization_6_gamma; 
-  size_t batch_normalization_6_gamma_bytes; 
-  void* batch_normalization_6_beta; 
-  size_t batch_normalization_6_beta_bytes; 
-  void* batch_normalization_6_mean; 
-  size_t batch_normalization_6_mean_bytes; 
-  void* batch_normalization_6_variance; 
-  size_t batch_normalization_6_variance_bytes; 
-  void* conv2d_4_w; 
-  size_t conv2d_4_w_bytes; 
-  void* batch_normalization_7_gamma; 
-  size_t batch_normalization_7_gamma_bytes; 
-  void* batch_normalization_7_beta; 
-  size_t batch_normalization_7_beta_bytes; 
-  void* batch_normalization_7_mean; 
-  size_t batch_normalization_7_mean_bytes; 
-  void* batch_normalization_7_variance; 
-  size_t batch_normalization_7_variance_bytes; 
-  void* depthwise_conv2d_4_w; 
-  size_t depthwise_conv2d_4_w_bytes; 
-  void* batch_normalization_8_gamma; 
-  size_t batch_normalization_8_gamma_bytes; 
-  void* batch_normalization_8_beta; 
-  size_t batch_normalization_8_beta_bytes; 
-  void* batch_normalization_8_mean; 
-  size_t batch_normalization_8_mean_bytes; 
-  void* batch_normalization_8_variance; 
-  size_t batch_normalization_8_variance_bytes; 
-  void* conv2d_5_w; 
-  size_t conv2d_5_w_bytes; 
-  void* batch_normalization_9_gamma; 
-  size_t batch_normalization_9_gamma_bytes; 
-  void* batch_normalization_9_beta; 
-  size_t batch_normalization_9_beta_bytes; 
-  void* batch_normalization_9_mean; 
-  size_t batch_normalization_9_mean_bytes; 
-  void* batch_normalization_9_variance; 
-  size_t batch_normalization_9_variance_bytes; 
-  void* depthwise_conv2d_5_w; 
-  size_t depthwise_conv2d_5_w_bytes; 
-  void* batch_normalization_10_gamma; 
-  size_t batch_normalization_10_gamma_bytes; 
-  void* batch_normalization_10_beta; 
-  size_t batch_normalization_10_beta_bytes; 
-  void* batch_normalization_10_mean; 
-  size_t batch_normalization_10_mean_bytes; 
-  void* batch_normalization_10_variance; 
-  size_t batch_normalization_10_variance_bytes; 
-  void* conv2d_6_w; 
-  size_t conv2d_6_w_bytes; 
-  void* batch_normalization_11_gamma; 
-  size_t batch_normalization_11_gamma_bytes; 
-  void* batch_normalization_11_beta; 
-  size_t batch_normalization_11_beta_bytes; 
-  void* batch_normalization_11_mean; 
-  size_t batch_normalization_11_mean_bytes; 
-  void* batch_normalization_11_variance; 
-  size_t batch_normalization_11_variance_bytes; 
-  void* depthwise_conv2d_6_w; 
-  size_t depthwise_conv2d_6_w_bytes; 
-  void* batch_normalization_12_gamma; 
-  size_t batch_normalization_12_gamma_bytes; 
-  void* batch_normalization_12_beta; 
-  size_t batch_normalization_12_beta_bytes; 
-  void* batch_normalization_12_mean; 
-  size_t batch_normalization_12_mean_bytes; 
-  void* batch_normalization_12_variance; 
-  size_t batch_normalization_12_variance_bytes; 
-  void* conv2d_7_w; 
-  size_t conv2d_7_w_bytes; 
-  void* batch_normalization_13_gamma; 
-  size_t batch_normalization_13_gamma_bytes; 
-  void* batch_normalization_13_beta; 
-  size_t batch_normalization_13_beta_bytes; 
-  void* batch_normalization_13_mean; 
-  size_t batch_normalization_13_mean_bytes; 
-  void* batch_normalization_13_variance; 
-  size_t batch_normalization_13_variance_bytes; 
-  void* dense_1_w; 
-  size_t dense_1_w_bytes; 
-  void* dense_1_b; 
-  size_t dense_1_b_bytes; 
-
-  struct ret_t r; 
-}
-RootIn;
-
-int main(){ 
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-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); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-uint8_t* labels = readLabels(labels_path.c_str(),10000); 
-
-__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->batch_normalization_1_gamma = batch_normalization_1_gamma; 
-args->batch_normalization_1_gamma_bytes = 0; 
-args->batch_normalization_1_beta = batch_normalization_1_beta; 
-args->batch_normalization_1_beta_bytes = 0; 
-args->batch_normalization_1_mean = batch_normalization_1_mean; 
-args->batch_normalization_1_mean_bytes = 0; 
-args->batch_normalization_1_variance = batch_normalization_1_variance; 
-args->batch_normalization_1_variance_bytes = 0; 
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w; 
-args->depthwise_conv2d_1_w_bytes = 0; 
-args->batch_normalization_2_gamma = batch_normalization_2_gamma; 
-args->batch_normalization_2_gamma_bytes = 0; 
-args->batch_normalization_2_beta = batch_normalization_2_beta; 
-args->batch_normalization_2_beta_bytes = 0; 
-args->batch_normalization_2_mean = batch_normalization_2_mean; 
-args->batch_normalization_2_mean_bytes = 0; 
-args->batch_normalization_2_variance = batch_normalization_2_variance; 
-args->batch_normalization_2_variance_bytes = 0; 
-args->conv2d_2_w = conv2d_2_w; 
-args->conv2d_2_w_bytes = 0; 
-args->batch_normalization_3_gamma = batch_normalization_3_gamma; 
-args->batch_normalization_3_gamma_bytes = 0; 
-args->batch_normalization_3_beta = batch_normalization_3_beta; 
-args->batch_normalization_3_beta_bytes = 0; 
-args->batch_normalization_3_mean = batch_normalization_3_mean; 
-args->batch_normalization_3_mean_bytes = 0; 
-args->batch_normalization_3_variance = batch_normalization_3_variance; 
-args->batch_normalization_3_variance_bytes = 0; 
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w; 
-args->depthwise_conv2d_2_w_bytes = 0; 
-args->batch_normalization_4_gamma = batch_normalization_4_gamma; 
-args->batch_normalization_4_gamma_bytes = 0; 
-args->batch_normalization_4_beta = batch_normalization_4_beta; 
-args->batch_normalization_4_beta_bytes = 0; 
-args->batch_normalization_4_mean = batch_normalization_4_mean; 
-args->batch_normalization_4_mean_bytes = 0; 
-args->batch_normalization_4_variance = batch_normalization_4_variance; 
-args->batch_normalization_4_variance_bytes = 0; 
-args->conv2d_3_w = conv2d_3_w; 
-args->conv2d_3_w_bytes = 0; 
-args->batch_normalization_5_gamma = batch_normalization_5_gamma; 
-args->batch_normalization_5_gamma_bytes = 0; 
-args->batch_normalization_5_beta = batch_normalization_5_beta; 
-args->batch_normalization_5_beta_bytes = 0; 
-args->batch_normalization_5_mean = batch_normalization_5_mean; 
-args->batch_normalization_5_mean_bytes = 0; 
-args->batch_normalization_5_variance = batch_normalization_5_variance; 
-args->batch_normalization_5_variance_bytes = 0; 
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w; 
-args->depthwise_conv2d_3_w_bytes = 0; 
-args->batch_normalization_6_gamma = batch_normalization_6_gamma; 
-args->batch_normalization_6_gamma_bytes = 0; 
-args->batch_normalization_6_beta = batch_normalization_6_beta; 
-args->batch_normalization_6_beta_bytes = 0; 
-args->batch_normalization_6_mean = batch_normalization_6_mean; 
-args->batch_normalization_6_mean_bytes = 0; 
-args->batch_normalization_6_variance = batch_normalization_6_variance; 
-args->batch_normalization_6_variance_bytes = 0; 
-args->conv2d_4_w = conv2d_4_w; 
-args->conv2d_4_w_bytes = 0; 
-args->batch_normalization_7_gamma = batch_normalization_7_gamma; 
-args->batch_normalization_7_gamma_bytes = 0; 
-args->batch_normalization_7_beta = batch_normalization_7_beta; 
-args->batch_normalization_7_beta_bytes = 0; 
-args->batch_normalization_7_mean = batch_normalization_7_mean; 
-args->batch_normalization_7_mean_bytes = 0; 
-args->batch_normalization_7_variance = batch_normalization_7_variance; 
-args->batch_normalization_7_variance_bytes = 0; 
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w; 
-args->depthwise_conv2d_4_w_bytes = 0; 
-args->batch_normalization_8_gamma = batch_normalization_8_gamma; 
-args->batch_normalization_8_gamma_bytes = 0; 
-args->batch_normalization_8_beta = batch_normalization_8_beta; 
-args->batch_normalization_8_beta_bytes = 0; 
-args->batch_normalization_8_mean = batch_normalization_8_mean; 
-args->batch_normalization_8_mean_bytes = 0; 
-args->batch_normalization_8_variance = batch_normalization_8_variance; 
-args->batch_normalization_8_variance_bytes = 0; 
-args->conv2d_5_w = conv2d_5_w; 
-args->conv2d_5_w_bytes = 0; 
-args->batch_normalization_9_gamma = batch_normalization_9_gamma; 
-args->batch_normalization_9_gamma_bytes = 0; 
-args->batch_normalization_9_beta = batch_normalization_9_beta; 
-args->batch_normalization_9_beta_bytes = 0; 
-args->batch_normalization_9_mean = batch_normalization_9_mean; 
-args->batch_normalization_9_mean_bytes = 0; 
-args->batch_normalization_9_variance = batch_normalization_9_variance; 
-args->batch_normalization_9_variance_bytes = 0; 
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w; 
-args->depthwise_conv2d_5_w_bytes = 0; 
-args->batch_normalization_10_gamma = batch_normalization_10_gamma; 
-args->batch_normalization_10_gamma_bytes = 0; 
-args->batch_normalization_10_beta = batch_normalization_10_beta; 
-args->batch_normalization_10_beta_bytes = 0; 
-args->batch_normalization_10_mean = batch_normalization_10_mean; 
-args->batch_normalization_10_mean_bytes = 0; 
-args->batch_normalization_10_variance = batch_normalization_10_variance; 
-args->batch_normalization_10_variance_bytes = 0; 
-args->conv2d_6_w = conv2d_6_w; 
-args->conv2d_6_w_bytes = 0; 
-args->batch_normalization_11_gamma = batch_normalization_11_gamma; 
-args->batch_normalization_11_gamma_bytes = 0; 
-args->batch_normalization_11_beta = batch_normalization_11_beta; 
-args->batch_normalization_11_beta_bytes = 0; 
-args->batch_normalization_11_mean = batch_normalization_11_mean; 
-args->batch_normalization_11_mean_bytes = 0; 
-args->batch_normalization_11_variance = batch_normalization_11_variance; 
-args->batch_normalization_11_variance_bytes = 0; 
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w; 
-args->depthwise_conv2d_6_w_bytes = 0; 
-args->batch_normalization_12_gamma = batch_normalization_12_gamma; 
-args->batch_normalization_12_gamma_bytes = 0; 
-args->batch_normalization_12_beta = batch_normalization_12_beta; 
-args->batch_normalization_12_beta_bytes = 0; 
-args->batch_normalization_12_mean = batch_normalization_12_mean; 
-args->batch_normalization_12_mean_bytes = 0; 
-args->batch_normalization_12_variance = batch_normalization_12_variance; 
-args->batch_normalization_12_variance_bytes = 0; 
-args->conv2d_7_w = conv2d_7_w; 
-args->conv2d_7_w_bytes = 0; 
-args->batch_normalization_13_gamma = batch_normalization_13_gamma; 
-args->batch_normalization_13_gamma_bytes = 0; 
-args->batch_normalization_13_beta = batch_normalization_13_beta; 
-args->batch_normalization_13_beta_bytes = 0; 
-args->batch_normalization_13_mean = batch_normalization_13_mean; 
-args->batch_normalization_13_mean_bytes = 0; 
-args->batch_normalization_13_variance = batch_normalization_13_variance; 
-args->batch_normalization_13_variance_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; 
-
-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; 
-
-} 
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_beta.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_beta.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
+++ /dev/null
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-êÞ^>ÂX`¾q=Ï·‡>Hp‚>°¾¾B—b>6ÁU¾$ƒt¾½M¾
\ No newline at end of file
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
deleted file mode 100644
index 9b8b3f7e11a428a28fecbde2c204bf39b7e02703..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-depthwise_conv  
-batchnorm  
-activation  
-conv  
-batchnorm  
-activation  
-pool  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
deleted file mode 100644
index a9415755180a7ebdceb89b7e3e6d6cee258b18c4..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorPooling1
-FC1,10000,2048,2048,10
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
deleted file mode 100644
index c5fd3606da51281bc2c583e98f024bd2f54f837b..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
+++ /dev/null
@@ -1,238 +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 = 10000; 
-int batch_size = 10000; 
-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("data/mobilenet_shallow_nathan/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-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 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, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9); 
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-void* var_2 = tensorRelu(var_1); 
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-void* var_5 = tensorRelu(var_4); 
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9); 
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-void* var_8 = tensorRelu(var_7); 
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-void* var_11 = tensorRelu(var_10); 
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9); 
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-void* var_14 = tensorRelu(var_13); 
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-void* var_17 = tensorRelu(var_16); 
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9); 
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-void* var_20 = tensorRelu(var_19); 
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-void* var_23 = tensorRelu(var_22); 
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9); 
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-void* var_26 = tensorRelu(var_25); 
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-void* var_29 = tensorRelu(var_28); 
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9); 
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-void* var_32 = tensorRelu(var_31); 
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-void* var_35 = tensorRelu(var_34); 
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9); 
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-void* var_38 = tensorRelu(var_37); 
-void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2); 
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9); 
-void* var_41 = tensorSoftmax(var_40); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_41); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-}
-
-dumpExecutionAccuracies(); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
deleted file mode 100644
index 6599f7d0ea0be6a76c4154d25b3a7be2c6724115..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
+++ /dev/null
@@ -1,231 +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("data/mobilenet_shallow_nathan/"); 
-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 batch_normalization_1_gamma_path =  dir_prefix + std::string("batch_normalization_1_gamma.bin"); 
-void* batch_normalization_1_gamma =  readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_beta_path =  dir_prefix + std::string("batch_normalization_1_beta.bin"); 
-void* batch_normalization_1_beta =  readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_mean_path =  dir_prefix + std::string("batch_normalization_1_mean.bin"); 
-void* batch_normalization_1_mean =  readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_1_variance_path =  dir_prefix + std::string("batch_normalization_1_variance.bin"); 
-void* batch_normalization_1_variance =  readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1); 
-std::string depthwise_conv2d_1_w_path =  dir_prefix + std::string("depthwise_conv2d_1_w.bin"); 
-void* depthwise_conv2d_1_w =  readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3); 
-std::string batch_normalization_2_gamma_path =  dir_prefix + std::string("batch_normalization_2_gamma.bin"); 
-void* batch_normalization_2_gamma =  readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_beta_path =  dir_prefix + std::string("batch_normalization_2_beta.bin"); 
-void* batch_normalization_2_beta =  readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_mean_path =  dir_prefix + std::string("batch_normalization_2_mean.bin"); 
-void* batch_normalization_2_mean =  readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1); 
-std::string batch_normalization_2_variance_path =  dir_prefix + std::string("batch_normalization_2_variance.bin"); 
-void* batch_normalization_2_variance =  readTrainedWeights(batch_normalization_2_variance_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(), 0,64,32,1,1); 
-std::string batch_normalization_3_gamma_path =  dir_prefix + std::string("batch_normalization_3_gamma.bin"); 
-void* batch_normalization_3_gamma =  readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_beta_path =  dir_prefix + std::string("batch_normalization_3_beta.bin"); 
-void* batch_normalization_3_beta =  readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_mean_path =  dir_prefix + std::string("batch_normalization_3_mean.bin"); 
-void* batch_normalization_3_mean =  readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_3_variance_path =  dir_prefix + std::string("batch_normalization_3_variance.bin"); 
-void* batch_normalization_3_variance =  readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1); 
-std::string depthwise_conv2d_2_w_path =  dir_prefix + std::string("depthwise_conv2d_2_w.bin"); 
-void* depthwise_conv2d_2_w =  readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3); 
-std::string batch_normalization_4_gamma_path =  dir_prefix + std::string("batch_normalization_4_gamma.bin"); 
-void* batch_normalization_4_gamma =  readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_beta_path =  dir_prefix + std::string("batch_normalization_4_beta.bin"); 
-void* batch_normalization_4_beta =  readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_mean_path =  dir_prefix + std::string("batch_normalization_4_mean.bin"); 
-void* batch_normalization_4_mean =  readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1); 
-std::string batch_normalization_4_variance_path =  dir_prefix + std::string("batch_normalization_4_variance.bin"); 
-void* batch_normalization_4_variance =  readTrainedWeights(batch_normalization_4_variance_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(), 0,128,64,1,1); 
-std::string batch_normalization_5_gamma_path =  dir_prefix + std::string("batch_normalization_5_gamma.bin"); 
-void* batch_normalization_5_gamma =  readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_beta_path =  dir_prefix + std::string("batch_normalization_5_beta.bin"); 
-void* batch_normalization_5_beta =  readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_mean_path =  dir_prefix + std::string("batch_normalization_5_mean.bin"); 
-void* batch_normalization_5_mean =  readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_5_variance_path =  dir_prefix + std::string("batch_normalization_5_variance.bin"); 
-void* batch_normalization_5_variance =  readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_3_w_path =  dir_prefix + std::string("depthwise_conv2d_3_w.bin"); 
-void* depthwise_conv2d_3_w =  readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_6_gamma_path =  dir_prefix + std::string("batch_normalization_6_gamma.bin"); 
-void* batch_normalization_6_gamma =  readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_beta_path =  dir_prefix + std::string("batch_normalization_6_beta.bin"); 
-void* batch_normalization_6_beta =  readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_mean_path =  dir_prefix + std::string("batch_normalization_6_mean.bin"); 
-void* batch_normalization_6_mean =  readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_6_variance_path =  dir_prefix + std::string("batch_normalization_6_variance.bin"); 
-void* batch_normalization_6_variance =  readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,128,128,1,1); 
-std::string batch_normalization_7_gamma_path =  dir_prefix + std::string("batch_normalization_7_gamma.bin"); 
-void* batch_normalization_7_gamma =  readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_beta_path =  dir_prefix + std::string("batch_normalization_7_beta.bin"); 
-void* batch_normalization_7_beta =  readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_mean_path =  dir_prefix + std::string("batch_normalization_7_mean.bin"); 
-void* batch_normalization_7_mean =  readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_7_variance_path =  dir_prefix + std::string("batch_normalization_7_variance.bin"); 
-void* batch_normalization_7_variance =  readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1); 
-std::string depthwise_conv2d_4_w_path =  dir_prefix + std::string("depthwise_conv2d_4_w.bin"); 
-void* depthwise_conv2d_4_w =  readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3); 
-std::string batch_normalization_8_gamma_path =  dir_prefix + std::string("batch_normalization_8_gamma.bin"); 
-void* batch_normalization_8_gamma =  readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_beta_path =  dir_prefix + std::string("batch_normalization_8_beta.bin"); 
-void* batch_normalization_8_beta =  readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_mean_path =  dir_prefix + std::string("batch_normalization_8_mean.bin"); 
-void* batch_normalization_8_mean =  readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1); 
-std::string batch_normalization_8_variance_path =  dir_prefix + std::string("batch_normalization_8_variance.bin"); 
-void* batch_normalization_8_variance =  readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1); 
-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(), 0,256,128,1,1); 
-std::string batch_normalization_9_gamma_path =  dir_prefix + std::string("batch_normalization_9_gamma.bin"); 
-void* batch_normalization_9_gamma =  readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_beta_path =  dir_prefix + std::string("batch_normalization_9_beta.bin"); 
-void* batch_normalization_9_beta =  readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_mean_path =  dir_prefix + std::string("batch_normalization_9_mean.bin"); 
-void* batch_normalization_9_mean =  readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_9_variance_path =  dir_prefix + std::string("batch_normalization_9_variance.bin"); 
-void* batch_normalization_9_variance =  readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_5_w_path =  dir_prefix + std::string("depthwise_conv2d_5_w.bin"); 
-void* depthwise_conv2d_5_w =  readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_10_gamma_path =  dir_prefix + std::string("batch_normalization_10_gamma.bin"); 
-void* batch_normalization_10_gamma =  readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_beta_path =  dir_prefix + std::string("batch_normalization_10_beta.bin"); 
-void* batch_normalization_10_beta =  readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_mean_path =  dir_prefix + std::string("batch_normalization_10_mean.bin"); 
-void* batch_normalization_10_mean =  readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_10_variance_path =  dir_prefix + std::string("batch_normalization_10_variance.bin"); 
-void* batch_normalization_10_variance =  readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1); 
-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(), 0,256,256,1,1); 
-std::string batch_normalization_11_gamma_path =  dir_prefix + std::string("batch_normalization_11_gamma.bin"); 
-void* batch_normalization_11_gamma =  readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_beta_path =  dir_prefix + std::string("batch_normalization_11_beta.bin"); 
-void* batch_normalization_11_beta =  readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_mean_path =  dir_prefix + std::string("batch_normalization_11_mean.bin"); 
-void* batch_normalization_11_mean =  readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_11_variance_path =  dir_prefix + std::string("batch_normalization_11_variance.bin"); 
-void* batch_normalization_11_variance =  readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1); 
-std::string depthwise_conv2d_6_w_path =  dir_prefix + std::string("depthwise_conv2d_6_w.bin"); 
-void* depthwise_conv2d_6_w =  readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3); 
-std::string batch_normalization_12_gamma_path =  dir_prefix + std::string("batch_normalization_12_gamma.bin"); 
-void* batch_normalization_12_gamma =  readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_beta_path =  dir_prefix + std::string("batch_normalization_12_beta.bin"); 
-void* batch_normalization_12_beta =  readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_mean_path =  dir_prefix + std::string("batch_normalization_12_mean.bin"); 
-void* batch_normalization_12_mean =  readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1); 
-std::string batch_normalization_12_variance_path =  dir_prefix + std::string("batch_normalization_12_variance.bin"); 
-void* batch_normalization_12_variance =  readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1); 
-std::string batch_normalization_13_gamma_path =  dir_prefix + std::string("batch_normalization_13_gamma.bin"); 
-void* batch_normalization_13_gamma =  readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_beta_path =  dir_prefix + std::string("batch_normalization_13_beta.bin"); 
-void* batch_normalization_13_beta =  readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_mean_path =  dir_prefix + std::string("batch_normalization_13_mean.bin"); 
-void* batch_normalization_13_mean =  readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1); 
-std::string batch_normalization_13_variance_path =  dir_prefix + std::string("batch_normalization_13_variance.bin"); 
-void* batch_normalization_13_variance =  readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1); 
-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); 
-
-
-
-startMemTracking(); 
-
-int test_input_size = 10000; 
-int batch_size = 10000; 
-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, 1, 1, 1, 1, 1, 1); 
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001); 
-void* var_2 = tensorRelu(var_1); 
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32); 
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001); 
-void* var_6 = tensorRelu(var_5); 
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1); 
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001); 
-void* var_9 = tensorRelu(var_8); 
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64); 
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001); 
-void* var_13 = tensorRelu(var_12); 
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1); 
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001); 
-void* var_16 = tensorRelu(var_15); 
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128); 
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001); 
-void* var_20 = tensorRelu(var_19); 
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1); 
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001); 
-void* var_23 = tensorRelu(var_22); 
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128); 
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001); 
-void* var_28 = tensorRelu(var_27); 
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1); 
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001); 
-void* var_31 = tensorRelu(var_30); 
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256); 
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001); 
-void* var_35 = tensorRelu(var_34); 
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1); 
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001); 
-void* var_38 = tensorRelu(var_37); 
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256); 
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001); 
-void* var_43 = tensorRelu(var_42); 
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1); 
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001); 
-void* var_46 = tensorRelu(var_45); 
-void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2); 
-void* var_49 = tensorGemmGPU(var_47, dense_1_w); 
-void* var_50 = tensorAdd(var_49, dense_1_b); 
-void* var_51 = tensorSoftmax(var_50); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_51); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
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-)¯¼YÅG¾^þ™½-‚ä¾R¾Ý¾‰c¾l2&?D¾
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Š¾ïޏ¾WŸ¾ô¾í÷³¾³Õ½
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¦d¾!6Á¾Cb?ûÄ輆¤¼¸Ñ¿j½–B·=œœY½Æ+®>9ø]½Š
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_b.bin
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+++ /dev/null
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-oD??êÙ€?àkü½žHÝ>Ô61¾½<=¡¶(>Ž—¿œˆ>"W«¿¨^B½F[$¿:’ì>¾ýë¼Í
-'¿]3¡>Üx!¿¿N?}Þ=oCÀ”=¾é½*¢Ÿ>í#œ¿Gk?ŠxÞ>YÌ¢¾$l¢¼)Ó¿² ¿6¸q¿ä—¿,à>?ÿѽ£ïξFlü¾%»¾ò—X¿³¶:>IZ
À±@q½bõ‰½‚yŸ¾Ü&,¿÷‰µ¿‹8¿qÞD?*ƒ;¿­û뽚ۗ½‰­>ô'*¿pÑ¡¾—Á>Âý\>*Y5¾åsP½Êè’>E¥¿—1Q¿¢*)¾ç©–¿GSµ¿›JÔ¾
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_21_w.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_b.bin
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+++ /dev/null
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-ç‹=߇˜>Ä@Y>žÁ9>âË»Sé>“4W>Ó€"=.ÊU½l‘¬>¬ÅÕ>!ì=\¿V>µ¤>2ô¼
eŽ¾
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/conv2d_2_w.bin
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ž¾
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>aý>é?}#’>Ñ8Ç>Y«¬<
UÀ>A ¾ªð—¾ìª=FR¸½ãI>D>
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc
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index f6e7e32153a5e89a68798f809cf4166285b408ea..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/promise_src.cc
+++ /dev/null
@@ -1,162 +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("resnet18_cifar10_promise/"); 
-std::string input_path =  dir_prefix + std::string("input.bin"); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-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(), 0,16,3,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3); 
-std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1); 
-std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-void* conv2d_21_b =  readTrainedWeights(conv2d_21_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(), 0,1,1,64,10); 
-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); 
-
-
-void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.9356618, 9); 
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 2.9356618, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.0425158, 9); 
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.0425158, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -10.203314, 9.055045, 9); 
-void* var_3 = tensorAdd(var_0, var_2); 
-void* var_4 = tensorRelu(var_3); 
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 9.734258, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 7.0053105, 9); 
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 7.0053105, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -4.8778534, 6.7311873, 9); 
-void* var_7 = tensorAdd(var_4, var_6); 
-void* var_8 = tensorRelu(var_7); 
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 10.858562, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.843336, 9); 
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 8.843336, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -10.204111, 5.4952374, 9); 
-void* var_11 = tensorAdd(var_8, var_10); 
-void* var_12 = tensorRelu(var_11); 
-void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 19.303282, 9); 
-void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 19.303282, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -24.404322, 24.37361, 9); 
-void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -6.497986, 12.8968935, 9); 
-void* var_16 = tensorAdd(var_15, var_14); 
-void* var_17 = tensorRelu(var_16); 
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 29.462997, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 24.329395, 9); 
-void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 24.329395, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -9.780206, 20.320444, 9); 
-void* var_20 = tensorAdd(var_17, var_19); 
-void* var_21 = tensorRelu(var_20); 
-void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 29.647404, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 25.600573, 9); 
-void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 25.600573, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -10.179335, 27.598986, 9); 
-void* var_24 = tensorAdd(var_21, var_23); 
-void* var_25 = tensorRelu(var_24); 
-void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 48.943645, 9); 
-void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 48.943645, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -75.016396, 112.3873, 9); 
-void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -13.93596, 24.614315, 9); 
-void* var_29 = tensorAdd(var_28, var_27); 
-void* var_30 = tensorRelu(var_29); 
-void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 126.758545, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 130.02768, 9); 
-void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 130.02768, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -97.91927, 130.89008, 9); 
-void* var_33 = tensorAdd(var_30, var_32); 
-void* var_34 = tensorRelu(var_33); 
-void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 220.4094, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 268.19254, 9); 
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 268.19254, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -235.08034, 264.33008, 9); 
-void* var_37 = tensorAdd(var_34, var_36); 
-void* var_38 = tensorRelu(var_37); 
-void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8); 
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 39.821262, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -84.49565, 60.312202, 9); 
-void* var_41 = tensorSoftmax(var_40); 
-
-computeAccuracy2(labels,10000,var_41); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt
deleted file mode 100644
index af0279b1d2980d8c8d71f20f3ef8c3f3da585699..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/quant_ranges.txt
+++ /dev/null
@@ -1,22 +0,0 @@
--0.5500815 0.60786617 -1.0248864 1.2929907 -0.36291853 0.2533059 0.0 0.753551840782 
-0.0 0.753551840782 -0.69884616 0.71849966 -0.2781147 0.45571187 0.0 1.01057458043 
-0.0 1.01057458043 -0.59568167 0.7714691 -0.8602873 0.19743633 -1.84771883726 1.87930787086 
-0.0 2.33981014252 -0.41976976 0.43748936 -0.7021962 0.3033103 0.0 1.04317724705 
-0.0 1.04317724705 -0.46757826 0.4635873 -0.20662616 0.1778044 -0.829483509064 0.786805033684 
-0.0 2.49733686686 -0.64404047 0.45383143 -0.819547 0.38550296 0.0 0.897360802293 
-0.0 0.897360802293 -0.41986948 0.33654243 -0.3563013 0.22371122 -0.957150224447 0.54919362247 
-0.0 2.37362146616 -0.4805263 0.50655717 -0.296758 0.7742441 0.0 3.01592136621 
-0.0 3.01592136621 -0.52083415 0.45517674 -0.20242067 0.8236838 -5.2759475708 5.79733039856 
-0.0 2.37362146616 -0.5338656 1.3395424 -0.20242067 0.8236838 -0.738995380998 2.33600783587 
-0.0 7.07933432579 -0.34429058 0.43629733 -1.0744808 0.056708273 0.0 1.58645607233 
-0.0 1.58645607233 -0.30342352 0.39493486 -0.44630566 0.6492069 -1.49672914267 1.29970229745 
-0.0 7.11914063454 -0.38351893 0.45775774 -1.4733055 -0.014426912 0.0 1.52876508832 
-0.0 1.52876508832 -0.25695276 0.45372736 -0.5259744 0.26591402 -1.59576894164 1.08074297309 
-0.0 6.94405080318 -0.55299705 0.5443531 -0.71790683 1.2730768 0.0 10.3651468277 
-0.0 10.3651468277 -0.4203967 0.48641303 -0.90653443 1.3546854 -22.372925148 17.2033731079 
-0.0 6.94405080318 -0.4365755 0.84913826 -0.90653443 1.3546851 -3.66810325861 4.87814051151 
-0.0 18.8401451111 -0.38657624 0.5228989 -1.2083547 0.76361173 0.0 19.1229192352 
-0.0 19.1229192352 -0.40857902 0.575035 -1.8731614 1.0960501 -31.3229312897 14.8234729958 
-0.0 23.7382488823 -0.33079496 0.5893278 -1.0234511 1.0016295 0.0 19.5892774963 
-0.0 19.5892774963 -0.27897888 0.38280907 -2.2086356 1.0066502 -34.4416886902 20.9890329933 
-0.0 10.8541981602 -1.5092047 1.0279838 -0.49379802 0.61032647 -40.9121678543 25.7082381058
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc
deleted file mode 100644
index e82c54651aaa8b8df2ab34b65490aa79b730c327..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10/src.cc
+++ /dev/null
@@ -1,193 +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("resnet18_cifar10_promise/"); 
-std::string input_path =  dir_prefix + std::string("input.bin"); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-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(), 0,16,3,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-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(), 0,16,16,3,3); 
-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(), 0,1,16,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_14_w_path =  dir_prefix + std::string("conv2d_14_w.bin"); 
-void* conv2d_14_w =  readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3); 
-std::string conv2d_14_b_path =  dir_prefix + std::string("conv2d_14_b.bin"); 
-void* conv2d_14_b =  readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1); 
-std::string conv2d_15_w_path =  dir_prefix + std::string("conv2d_15_w.bin"); 
-void* conv2d_15_w =  readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3); 
-std::string conv2d_15_b_path =  dir_prefix + std::string("conv2d_15_b.bin"); 
-void* conv2d_15_b =  readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_17_w_path =  dir_prefix + std::string("conv2d_17_w.bin"); 
-void* conv2d_17_w =  readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1); 
-std::string conv2d_17_b_path =  dir_prefix + std::string("conv2d_17_b.bin"); 
-void* conv2d_17_b =  readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_16_w_path =  dir_prefix + std::string("conv2d_16_w.bin"); 
-void* conv2d_16_w =  readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_16_b_path =  dir_prefix + std::string("conv2d_16_b.bin"); 
-void* conv2d_16_b =  readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_18_w_path =  dir_prefix + std::string("conv2d_18_w.bin"); 
-void* conv2d_18_w =  readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_18_b_path =  dir_prefix + std::string("conv2d_18_b.bin"); 
-void* conv2d_18_b =  readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_19_w_path =  dir_prefix + std::string("conv2d_19_w.bin"); 
-void* conv2d_19_w =  readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_19_b_path =  dir_prefix + std::string("conv2d_19_b.bin"); 
-void* conv2d_19_b =  readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_20_w_path =  dir_prefix + std::string("conv2d_20_w.bin"); 
-void* conv2d_20_w =  readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_20_b_path =  dir_prefix + std::string("conv2d_20_b.bin"); 
-void* conv2d_20_b =  readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1); 
-std::string conv2d_21_w_path =  dir_prefix + std::string("conv2d_21_w.bin"); 
-void* conv2d_21_w =  readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3); 
-std::string conv2d_21_b_path =  dir_prefix + std::string("conv2d_21_b.bin"); 
-void* conv2d_21_b =  readTrainedWeights(conv2d_21_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(), 0,1,1,64,10); 
-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); 
-
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0); 
-void* var_3 = tensorAdd(var_2, conv2d_1_b); 
-void* var_4 = tensorRelu(var_3); 
-void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-void* var_7 = tensorAdd(var_6, conv2d_2_b); 
-void* var_8 = tensorRelu(var_7); 
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-void* var_11 = tensorAdd(var_10, conv2d_3_b); 
-void* var_12 = tensorAdd(var_4, var_11); 
-void* var_13 = tensorRelu(var_12); 
-void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-void* var_16 = tensorAdd(var_15, conv2d_4_b); 
-void* var_17 = tensorRelu(var_16); 
-void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-void* var_20 = tensorAdd(var_19, conv2d_5_b); 
-void* var_21 = tensorAdd(var_13, var_20); 
-void* var_22 = tensorRelu(var_21); 
-void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-void* var_25 = tensorAdd(var_24, conv2d_6_b); 
-void* var_26 = tensorRelu(var_25); 
-void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-void* var_29 = tensorAdd(var_28, conv2d_7_b); 
-void* var_30 = tensorAdd(var_22, var_29); 
-void* var_31 = tensorRelu(var_30); 
-void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0); 
-void* var_34 = tensorAdd(var_33, conv2d_8_b); 
-void* var_35 = tensorRelu(var_34); 
-void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-void* var_38 = tensorAdd(var_37, conv2d_9_b); 
-void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0); 
-void* var_41 = tensorAdd(var_40, conv2d_10_b); 
-void* var_42 = tensorAdd(var_41, var_38); 
-void* var_43 = tensorRelu(var_42); 
-void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-void* var_46 = tensorAdd(var_45, conv2d_11_b); 
-void* var_47 = tensorRelu(var_46); 
-void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-void* var_50 = tensorAdd(var_49, conv2d_12_b); 
-void* var_51 = tensorAdd(var_43, var_50); 
-void* var_52 = tensorRelu(var_51); 
-void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-void* var_55 = tensorAdd(var_54, conv2d_13_b); 
-void* var_56 = tensorRelu(var_55); 
-void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0); 
-void* var_59 = tensorAdd(var_58, conv2d_14_b); 
-void* var_60 = tensorAdd(var_52, var_59); 
-void* var_61 = tensorRelu(var_60); 
-void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0); 
-void* var_64 = tensorAdd(var_63, conv2d_15_b); 
-void* var_65 = tensorRelu(var_64); 
-void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0); 
-void* var_68 = tensorAdd(var_67, conv2d_16_b); 
-void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0); 
-void* var_71 = tensorAdd(var_70, conv2d_17_b); 
-void* var_72 = tensorAdd(var_71, var_68); 
-void* var_73 = tensorRelu(var_72); 
-void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0); 
-void* var_76 = tensorAdd(var_75, conv2d_18_b); 
-void* var_77 = tensorRelu(var_76); 
-void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0); 
-void* var_80 = tensorAdd(var_79, conv2d_19_b); 
-void* var_81 = tensorAdd(var_73, var_80); 
-void* var_82 = tensorRelu(var_81); 
-void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0); 
-void* var_85 = tensorAdd(var_84, conv2d_20_b); 
-void* var_86 = tensorRelu(var_85); 
-void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0); 
-void* var_89 = tensorAdd(var_88, conv2d_21_b); 
-void* var_90 = tensorAdd(var_82, var_89); 
-void* var_91 = tensorRelu(var_90); 
-void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8); 
-void* var_94 = tensorGemmGPU(var_92, dense_1_w); 
-void* var_95 = tensorAdd(var_94, dense_1_b); 
-void* var_96 = tensorSoftmax(var_95); 
-
-computeAccuracy2(labels,10000,var_96); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
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+++ /dev/null
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-!Ü=!J¹<«¾j>ÓOä=ŸÐî:²_¾Dr=~>Y«=֝>³½iÛ½ñ3½,3¾ŠÛ>ÇÛ=¸¾uŒ>ž\<Z7=Z§¬=''6¾o¨;Ñ»õ¾à¡t¾©¾	òG½àÓ¨¾¥å¼ôÿ]¾,O¨=¶’<»lÏ=¥Ò">¡'Œ½ÃŠ=®¤T¾Z=­=2éÏ=üjë½ûHF¿õ¾“œ¼£}=³¬¾ßä¼Ó°”>ÛïQ?VÍ<; =8ñ‰½‹xr¾¢°}<ÿz>îšG¾{“½ax6¾¶Î‚»$Ôë¾DpÎ=Š|5¾Á2>¤tY>ä©=
\ No newline at end of file
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/dense_2_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/dense_2_b.bin
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/dense_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-
Sî>:Ä}¾õ…>ºpž?ñ‡¿’ß²>Vâ–¿Q0å<qö
>Ò/“<
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt
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index b742502f145c535db5432c0f6a0de27ba3ed3979..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.8816367 2.0934217 -0.53275156 0.49437004 -0.6403629 0.2490165 0.0 1.35908746719 
-0.0 1.35908746719 -0.2688396 0.20639156 -0.7745511 0.82006615 0.0 2.52123117924 
-0.0 2.52123117924 -0.16776876 0.14878987 -0.35283303 0.5154362 0.0 1.20119857848 
-0.0 1.20119857848 -0.088948585 0.114222586 -0.30250227 0.36856708 0.0 1.03598809302 
-0.0 1.03598809302 -0.07739562 0.10973293 -0.15568458 0.17634983 0.0 0.300495595038 
-0.0 0.300495595038 -0.051649556 0.05435231 -0.07395447 0.07996062 0.0 0.11490475405 
-0.0 0.11490475405 -0.043513633 0.07577866 -0.06921874 0.02660573 0.0 0.16232508488 
-0.0 0.16232508488 -0.033842053 0.045218028 -0.022827804 0.023845317 0.0 0.124249965735 
-0.0 0.124249965735 -0.02211613 0.032084666 -0.02699063 0.03773564 0.0 0.174634486511 
-0.0 0.174634486511 -0.01979376 0.034854397 -0.036107242 0.07056531 0.0 0.575175762177 
-0.0 0.575175762177 -0.03452098 0.046055835 -0.051925894 0.07039055 0.0 0.771875114441 
-0.0 0.771875114441 -0.025946895 0.040090334 -0.06049362 0.12658806 0.0 1.17285169065 
-0.0 1.17285169065 -0.021766115 0.03315237 -0.20705001 0.117947325 0.0 2.00157693863 
-0.0 2.00157693863 -0.042597745 0.046707444 -0.21937433 0.2545502 0.0 2.00236111879 
-0.0 2.00236111879 -0.32550547 0.30829763 -1.1787822 1.2378151 -18.2514705467 24.1736344528
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc
deleted file mode 100644
index 44179ee9f39c9547270b45fc84249835350bee5f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/src.cc
+++ /dev/null
@@ -1,141 +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("vgg16_cifar10/"); 
-std::string input_path =  dir_prefix + std::string("input.bin"); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-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(), 0,64,3,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,64,64,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,128,64,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,128,128,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,256,128,3,3); 
-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(), 0,1,256,1,1); 
-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(), 0,256,256,3,3); 
-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(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-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(), 0,1,1,512,512); 
-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(), 0,1,512,1,1); 
-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(), 0,1,1,512,10); 
-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, 1, 1, 1, 1, 1, 0); 
-void* var_1 = tensorAdd(var_0, conv2d_1_b); 
-void* var_2 = tensorRelu(var_1); 
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-void* var_5 = tensorAdd(var_4, conv2d_2_b); 
-void* var_6 = tensorRelu(var_5); 
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2); 
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-void* var_9 = tensorAdd(var_8, conv2d_3_b); 
-void* var_10 = tensorRelu(var_9); 
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-void* var_13 = tensorAdd(var_12, conv2d_4_b); 
-void* var_14 = tensorRelu(var_13); 
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2); 
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-void* var_17 = tensorAdd(var_16, conv2d_5_b); 
-void* var_18 = tensorRelu(var_17); 
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-void* var_21 = tensorAdd(var_20, conv2d_6_b); 
-void* var_22 = tensorRelu(var_21); 
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-void* var_25 = tensorAdd(var_24, conv2d_7_b); 
-void* var_26 = tensorRelu(var_25); 
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2); 
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0); 
-void* var_29 = tensorAdd(var_28, conv2d_8_b); 
-void* var_30 = tensorRelu(var_29); 
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-void* var_33 = tensorAdd(var_32, conv2d_9_b); 
-void* var_34 = tensorRelu(var_33); 
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0); 
-void* var_37 = tensorAdd(var_36, conv2d_10_b); 
-void* var_38 = tensorRelu(var_37); 
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2); 
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-void* var_41 = tensorAdd(var_40, conv2d_11_b); 
-void* var_42 = tensorRelu(var_41); 
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-void* var_45 = tensorAdd(var_44, conv2d_12_b); 
-void* var_46 = tensorRelu(var_45); 
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-void* var_49 = tensorAdd(var_48, conv2d_13_b); 
-void* var_50 = tensorRelu(var_49); 
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2); 
-void* var_54 = tensorGemmGPU(var_51, dense_1_w); 
-void* var_55 = tensorAdd(var_54, dense_1_b); 
-void* var_56 = tensorRelu(var_55); 
-void* var_58 = tensorGemmGPU(var_56, dense_2_w); 
-void* var_59 = tensorAdd(var_58, dense_2_b); 
-void* var_60 = tensorSoftmax(var_59); 
-
-computeAccuracy2(labels,10000,var_60); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin
deleted file mode 100644
index 43bc1e5b985604c5a17fe67d2db4fec82e12042d..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_cifar_calib.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin
deleted file mode 100644
index 9be730fd6f397987a6948a8d9196c7e156675d1b..0000000000000000000000000000000000000000
Binary files a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10/vgg16_train_labels.bin and /dev/null differ
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc
deleted file mode 100644
index 8084e3723a6141ac0e99729b8455bcc529ac7a0f..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/approxhpvm_src.cc
+++ /dev/null
@@ -1,982 +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, 1, 1, 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, 1, 1, 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, 1, 1, 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, 1, 1, 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_convolution(t1, t2, 1, 1, 1, 1); 
-  __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_convolution(t1, t2, 1, 1, 1, 1); 
-  __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_relu(t1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_20_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_21_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_22_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_23_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_24_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_25_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_26_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_27_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_28_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_29_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_30_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_31_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_32_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_33_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_34_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_35_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_36_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_37_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_38_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_39_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_40_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, 1, 1); 
-  __visc__return(2, r, (size_t) 0); 
-}
-
-void var_41_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_42_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_43_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_44_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_45_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_46_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_47_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_48_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_49_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* conv2d_5_w, size_t conv2d_5_w_bytes, 
-	  void* conv2d_5_b, size_t conv2d_5_b_bytes, 
-	  void* conv2d_6_w, size_t conv2d_6_w_bytes, 
-	  void* conv2d_6_b, size_t conv2d_6_b_bytes, 
-	  void* conv2d_7_w, size_t conv2d_7_w_bytes, 
-	  void* conv2d_7_b, size_t conv2d_7_b_bytes, 
-	  void* conv2d_8_w, size_t conv2d_8_w_bytes, 
-	  void* conv2d_8_b, size_t conv2d_8_b_bytes, 
-	  void* conv2d_9_w, size_t conv2d_9_w_bytes, 
-	  void* conv2d_9_b, size_t conv2d_9_b_bytes, 
-	  void* conv2d_10_w, size_t conv2d_10_w_bytes, 
-	  void* conv2d_10_b, size_t conv2d_10_b_bytes, 
-	  void* conv2d_11_w, size_t conv2d_11_w_bytes, 
-	  void* conv2d_11_b, size_t conv2d_11_b_bytes, 
-	  void* conv2d_12_w, size_t conv2d_12_w_bytes, 
-	  void* conv2d_12_b, size_t conv2d_12_b_bytes, 
-	  void* conv2d_13_w, size_t conv2d_13_w_bytes, 
-	  void* conv2d_13_b, size_t conv2d_13_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(31, 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, conv2d_5_w, conv2d_5_b, conv2d_6_w, conv2d_6_b, conv2d_7_w, conv2d_7_b, conv2d_8_w, conv2d_8_b, conv2d_9_w, conv2d_9_b, conv2d_10_w, conv2d_10_b, conv2d_11_w, conv2d_11_b, conv2d_12_w, conv2d_12_b, conv2d_13_w, conv2d_13_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); 
-
-  void* var_20 = __visc__createNodeND(0, var_20_node); 
-
-  __visc__edge(var_19, var_20, 1, 0, 0, 0); 
-  __visc__edge(var_19, var_20, 1, 1, 1, 0); 
-  __visc__bindIn(var_20, 26, 2, 0); 
-  __visc__bindIn(var_20, 27, 3, 0); 
-
-  void* var_21 = __visc__createNodeND(0, var_21_node); 
-
-  __visc__edge(var_20, var_21, 1, 0, 0, 0); 
-  __visc__edge(var_20, var_21, 1, 1, 1, 0); 
-  __visc__bindIn(var_21, 28, 2, 0); 
-  __visc__bindIn(var_21, 29, 3, 0); 
-
-  void* var_22 = __visc__createNodeND(0, var_22_node); 
-
-  __visc__edge(var_21, var_22, 1, 0, 0, 0); 
-  __visc__edge(var_21, var_22, 1, 1, 1, 0); 
-
-  void* var_23 = __visc__createNodeND(0, var_23_node); 
-
-  __visc__edge(var_22, var_23, 1, 0, 0, 0); 
-  __visc__edge(var_22, var_23, 1, 1, 1, 0); 
-
-  void* var_24 = __visc__createNodeND(0, var_24_node); 
-
-  __visc__edge(var_23, var_24, 1, 0, 0, 0); 
-  __visc__edge(var_23, var_24, 1, 1, 1, 0); 
-  __visc__bindIn(var_24, 30, 2, 0); 
-  __visc__bindIn(var_24, 31, 3, 0); 
-
-  void* var_25 = __visc__createNodeND(0, var_25_node); 
-
-  __visc__edge(var_24, var_25, 1, 0, 0, 0); 
-  __visc__edge(var_24, var_25, 1, 1, 1, 0); 
-  __visc__bindIn(var_25, 32, 2, 0); 
-  __visc__bindIn(var_25, 33, 3, 0); 
-
-  void* var_26 = __visc__createNodeND(0, var_26_node); 
-
-  __visc__edge(var_25, var_26, 1, 0, 0, 0); 
-  __visc__edge(var_25, var_26, 1, 1, 1, 0); 
-
-  void* var_27 = __visc__createNodeND(0, var_27_node); 
-
-  __visc__edge(var_26, var_27, 1, 0, 0, 0); 
-  __visc__edge(var_26, var_27, 1, 1, 1, 0); 
-  __visc__bindIn(var_27, 34, 2, 0); 
-  __visc__bindIn(var_27, 35, 3, 0); 
-
-  void* var_28 = __visc__createNodeND(0, var_28_node); 
-
-  __visc__edge(var_27, var_28, 1, 0, 0, 0); 
-  __visc__edge(var_27, var_28, 1, 1, 1, 0); 
-  __visc__bindIn(var_28, 36, 2, 0); 
-  __visc__bindIn(var_28, 37, 3, 0); 
-
-  void* var_29 = __visc__createNodeND(0, var_29_node); 
-
-  __visc__edge(var_28, var_29, 1, 0, 0, 0); 
-  __visc__edge(var_28, var_29, 1, 1, 1, 0); 
-
-  void* var_30 = __visc__createNodeND(0, var_30_node); 
-
-  __visc__edge(var_29, var_30, 1, 0, 0, 0); 
-  __visc__edge(var_29, var_30, 1, 1, 1, 0); 
-  __visc__bindIn(var_30, 38, 2, 0); 
-  __visc__bindIn(var_30, 39, 3, 0); 
-
-  void* var_31 = __visc__createNodeND(0, var_31_node); 
-
-  __visc__edge(var_30, var_31, 1, 0, 0, 0); 
-  __visc__edge(var_30, var_31, 1, 1, 1, 0); 
-  __visc__bindIn(var_31, 40, 2, 0); 
-  __visc__bindIn(var_31, 41, 3, 0); 
-
-  void* var_32 = __visc__createNodeND(0, var_32_node); 
-
-  __visc__edge(var_31, var_32, 1, 0, 0, 0); 
-  __visc__edge(var_31, var_32, 1, 1, 1, 0); 
-
-  void* var_33 = __visc__createNodeND(0, var_33_node); 
-
-  __visc__edge(var_32, var_33, 1, 0, 0, 0); 
-  __visc__edge(var_32, var_33, 1, 1, 1, 0); 
-
-  void* var_34 = __visc__createNodeND(0, var_34_node); 
-
-  __visc__edge(var_33, var_34, 1, 0, 0, 0); 
-  __visc__edge(var_33, var_34, 1, 1, 1, 0); 
-  __visc__bindIn(var_34, 42, 2, 0); 
-  __visc__bindIn(var_34, 43, 3, 0); 
-
-  void* var_35 = __visc__createNodeND(0, var_35_node); 
-
-  __visc__edge(var_34, var_35, 1, 0, 0, 0); 
-  __visc__edge(var_34, var_35, 1, 1, 1, 0); 
-  __visc__bindIn(var_35, 44, 2, 0); 
-  __visc__bindIn(var_35, 45, 3, 0); 
-
-  void* var_36 = __visc__createNodeND(0, var_36_node); 
-
-  __visc__edge(var_35, var_36, 1, 0, 0, 0); 
-  __visc__edge(var_35, var_36, 1, 1, 1, 0); 
-
-  void* var_37 = __visc__createNodeND(0, var_37_node); 
-
-  __visc__edge(var_36, var_37, 1, 0, 0, 0); 
-  __visc__edge(var_36, var_37, 1, 1, 1, 0); 
-  __visc__bindIn(var_37, 46, 2, 0); 
-  __visc__bindIn(var_37, 47, 3, 0); 
-
-  void* var_38 = __visc__createNodeND(0, var_38_node); 
-
-  __visc__edge(var_37, var_38, 1, 0, 0, 0); 
-  __visc__edge(var_37, var_38, 1, 1, 1, 0); 
-  __visc__bindIn(var_38, 48, 2, 0); 
-  __visc__bindIn(var_38, 49, 3, 0); 
-
-  void* var_39 = __visc__createNodeND(0, var_39_node); 
-
-  __visc__edge(var_38, var_39, 1, 0, 0, 0); 
-  __visc__edge(var_38, var_39, 1, 1, 1, 0); 
-
-  void* var_40 = __visc__createNodeND(0, var_40_node); 
-
-  __visc__edge(var_39, var_40, 1, 0, 0, 0); 
-  __visc__edge(var_39, var_40, 1, 1, 1, 0); 
-  __visc__bindIn(var_40, 50, 2, 0); 
-  __visc__bindIn(var_40, 51, 3, 0); 
-
-  void* var_41 = __visc__createNodeND(0, var_41_node); 
-
-  __visc__edge(var_40, var_41, 1, 0, 0, 0); 
-  __visc__edge(var_40, var_41, 1, 1, 1, 0); 
-  __visc__bindIn(var_41, 52, 2, 0); 
-  __visc__bindIn(var_41, 53, 3, 0); 
-
-  void* var_42 = __visc__createNodeND(0, var_42_node); 
-
-  __visc__edge(var_41, var_42, 1, 0, 0, 0); 
-  __visc__edge(var_41, var_42, 1, 1, 1, 0); 
-
-  void* var_43 = __visc__createNodeND(0, var_43_node); 
-
-  __visc__edge(var_42, var_43, 1, 0, 0, 0); 
-  __visc__edge(var_42, var_43, 1, 1, 1, 0); 
-
-  void* var_44 = __visc__createNodeND(0, var_44_node); 
-
-  __visc__edge(var_43, var_44, 1, 0, 0, 0); 
-  __visc__edge(var_43, var_44, 1, 1, 1, 0); 
-  __visc__bindIn(var_44, 54, 2, 0); 
-  __visc__bindIn(var_44, 55, 3, 0); 
-
-  void* var_45 = __visc__createNodeND(0, var_45_node); 
-
-  __visc__edge(var_44, var_45, 1, 0, 0, 0); 
-  __visc__edge(var_44, var_45, 1, 1, 1, 0); 
-  __visc__bindIn(var_45, 56, 2, 0); 
-  __visc__bindIn(var_45, 57, 3, 0); 
-
-  void* var_46 = __visc__createNodeND(0, var_46_node); 
-
-  __visc__edge(var_45, var_46, 1, 0, 0, 0); 
-  __visc__edge(var_45, var_46, 1, 1, 1, 0); 
-
-  void* var_47 = __visc__createNodeND(0, var_47_node); 
-
-  __visc__edge(var_46, var_47, 1, 0, 0, 0); 
-  __visc__edge(var_46, var_47, 1, 1, 1, 0); 
-  __visc__bindIn(var_47, 58, 2, 0); 
-  __visc__bindIn(var_47, 59, 3, 0); 
-
-  void* var_48 = __visc__createNodeND(0, var_48_node); 
-
-  __visc__edge(var_47, var_48, 1, 0, 0, 0); 
-  __visc__edge(var_47, var_48, 1, 1, 1, 0); 
-  __visc__bindIn(var_48, 60, 2, 0); 
-  __visc__bindIn(var_48, 61, 3, 0); 
-
-  void* var_49 = __visc__createNodeND(0, var_49_node); 
-
-  __visc__edge(var_48, var_49, 1, 0, 0, 0); 
-  __visc__edge(var_48, var_49, 1, 1, 1, 0); 
-
-  __visc__bindOut(var_49, 0, 0, 0); 
-  __visc__bindOut(var_49, 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* conv2d_5_w; 
-  size_t conv2d_5_w_bytes; 
-  void* conv2d_5_b; 
-  size_t conv2d_5_b_bytes; 
-  void* conv2d_6_w; 
-  size_t conv2d_6_w_bytes; 
-  void* conv2d_6_b; 
-  size_t conv2d_6_b_bytes; 
-  void* conv2d_7_w; 
-  size_t conv2d_7_w_bytes; 
-  void* conv2d_7_b; 
-  size_t conv2d_7_b_bytes; 
-  void* conv2d_8_w; 
-  size_t conv2d_8_w_bytes; 
-  void* conv2d_8_b; 
-  size_t conv2d_8_b_bytes; 
-  void* conv2d_9_w; 
-  size_t conv2d_9_w_bytes; 
-  void* conv2d_9_b; 
-  size_t conv2d_9_b_bytes; 
-  void* conv2d_10_w; 
-  size_t conv2d_10_w_bytes; 
-  void* conv2d_10_b; 
-  size_t conv2d_10_b_bytes; 
-  void* conv2d_11_w; 
-  size_t conv2d_11_w_bytes; 
-  void* conv2d_11_b; 
-  size_t conv2d_11_b_bytes; 
-  void* conv2d_12_w; 
-  size_t conv2d_12_w_bytes; 
-  void* conv2d_12_b; 
-  size_t conv2d_12_b_bytes; 
-  void* conv2d_13_w; 
-  size_t conv2d_13_w_bytes; 
-  void* conv2d_13_b; 
-  size_t conv2d_13_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("vgg16_cifar100_test/"); 
-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(), 0,64,3,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,64,64,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,128,64,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,128,128,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,256,128,3,3); 
-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(), 0,1,256,1,1); 
-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(), 0,256,256,3,3); 
-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(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-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(), 0,1,1,512,512); 
-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(), 0,1,512,1,1); 
-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(), 0,1,1,512,100); 
-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(), 0,1,100,1,1); 
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32); 
-uint8_t* labels = readLabels(labels_path.c_str(),10000); 
-
-__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->conv2d_5_w = conv2d_5_w; 
-args->conv2d_5_w_bytes = 0; 
-args->conv2d_5_b = conv2d_5_b; 
-args->conv2d_5_b_bytes = 0; 
-args->conv2d_6_w = conv2d_6_w; 
-args->conv2d_6_w_bytes = 0; 
-args->conv2d_6_b = conv2d_6_b; 
-args->conv2d_6_b_bytes = 0; 
-args->conv2d_7_w = conv2d_7_w; 
-args->conv2d_7_w_bytes = 0; 
-args->conv2d_7_b = conv2d_7_b; 
-args->conv2d_7_b_bytes = 0; 
-args->conv2d_8_w = conv2d_8_w; 
-args->conv2d_8_w_bytes = 0; 
-args->conv2d_8_b = conv2d_8_b; 
-args->conv2d_8_b_bytes = 0; 
-args->conv2d_9_w = conv2d_9_w; 
-args->conv2d_9_w_bytes = 0; 
-args->conv2d_9_b = conv2d_9_b; 
-args->conv2d_9_b_bytes = 0; 
-args->conv2d_10_w = conv2d_10_w; 
-args->conv2d_10_w_bytes = 0; 
-args->conv2d_10_b = conv2d_10_b; 
-args->conv2d_10_b_bytes = 0; 
-args->conv2d_11_w = conv2d_11_w; 
-args->conv2d_11_w_bytes = 0; 
-args->conv2d_11_b = conv2d_11_b; 
-args->conv2d_11_b_bytes = 0; 
-args->conv2d_12_w = conv2d_12_w; 
-args->conv2d_12_w_bytes = 0; 
-args->conv2d_12_b = conv2d_12_b; 
-args->conv2d_12_b_bytes = 0; 
-args->conv2d_13_w = conv2d_13_w; 
-args->conv2d_13_w_bytes = 0; 
-args->conv2d_13_b = conv2d_13_b; 
-args->conv2d_13_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; 
-
-} 
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin
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--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_b.bin
+++ /dev/null
@@ -1,2 +0,0 @@
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-k>`M¶¿	?³0T>ò£>u‹>]ˆÁ½®‚¿m‚¿M—a>ù䑽2>ë>¯ñ
¿W[…>²>#z·>Õ®Ú;›J¸=žr=€Ã=
£>
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_w.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_2_w.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_3_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_3_b.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin
deleted file mode 100644
index e2999812df22d44cf3e658d32986e6ccbabc9d4c..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/conv2d_6_b.bin
+++ /dev/null
@@ -1,5 +0,0 @@
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ë¾…~£>Ë…·>Ûgž¾¦_=¼ü<œ‰€>y,¿}«·>š?O=
->.Íô=”í¾
dP¾<>¶¤>?^÷½o)	>Ä#?¹”s½ÎM~>‚?øáy>FÅT¾4E“=†¿§B½I€=Û=?
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’Ï>›s>,|>þ®>ïºS>Æ–>%>“?»Àå>¤¢Á½Û÷Ø>û‚œ;õ)ï=v6̾> >®£”<i°¾¬é>Žà×¼ÜüÏ>0œÌ>˜Lä>ʯ¾–“n>ບ>!û¯>€õ£>G>ÌÍÑ>i1>•â¼§8½ ε>AˆÖ>‰F<A?¯>Ìt>x=wô?ñl	?4®_>t^»>BáÒ>¾fÙ½`:˜½-N@?ñw›¾ð|÷<É'‘>ºJ¾|>⽩ë´>ß0b>áXß½£å'¾fMM>£Ix>¥&Ƚ3úƒ>â'È>­þþ>8åÉ>Y|§>ûT½9º¾
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels.bin
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels32.bin b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/labels32.bin
deleted file mode 100644
index 0e29aa05a086316e36429e20e1a13580e1b0c36a..0000000000000000000000000000000000000000
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diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt
deleted file mode 100644
index 79818d6f010035c6e19f12881749f4d5b3d3c253..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layer_composition.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-conv  add  activation  
-conv  add  activation  
-conv  add  activation  pool  
-dense  add  activation  
-dense  add  
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt
deleted file mode 100644
index 7eaa520e4a2e451d5ccec5c8737dec8be8458369..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Conv1,10000,3,32,32,64,3,3,3
-Conv2,10000,64,32,32,64,64,3,3
-Conv3,10000,64,16,16,128,64,3,3
-Conv4,10000,128,16,16,128,128,3,3
-Conv5,10000,128,8,8,256,128,3,3
-Conv6,10000,256,8,8,256,256,3,3
-Conv7,10000,256,8,8,256,256,3,3
-Conv8,10000,256,4,4,512,256,3,3
-Conv9,10000,512,4,4,512,512,3,3
-Conv10,10000,512,4,4,512,512,3,3
-Conv11,10000,512,2,2,512,512,3,3
-Conv12,10000,512,2,2,512,512,3,3
-Conv13,10000,512,2,2,512,512,3,3
-FC1,10000,512,512,512
-FC2,10000,512,512,100
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc
deleted file mode 100644
index 0f28f2bfd69d9e8c4895e782bd02173eefcd0993..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/promise_src.cc
+++ /dev/null
@@ -1,138 +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 = 10000; 
-int batch_size = 10000; 
-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("vgg16_cifar100_test/"); 
-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(), 0,64,3,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,64,64,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,128,64,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,128,128,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,256,128,3,3); 
-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(), 0,1,256,1,1); 
-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(), 0,256,256,3,3); 
-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(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-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(), 0,1,1,512,512); 
-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(), 0,1,512,1,1); 
-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(), 0,1,1,512,100); 
-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(), 0,1,100,1,1); 
-
-
-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, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9); 
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9); 
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9); 
-void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9); 
-void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9); 
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9); 
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9); 
-void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9); 
-void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9); 
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9); 
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9); 
-void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9); 
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9); 
-void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9); 
-void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9); 
-void* var_15 = tensorSoftmax(var_14); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_15); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-}
-
-dumpExecutionAccuracies(); 
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt
deleted file mode 100644
index 4e614e1664822d2ecf6fa426a7eb2fd7c362a2e7..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/quant_ranges.txt
+++ /dev/null
@@ -1,15 +0,0 @@
--1.7829767 1.9456929 -0.7450515 0.71249133 -1.5885142 0.275554 0.0 8.190712 
-0.0 8.190712 -0.30790088 0.43504623 -1.4242363 1.2602744 0.0 19.023172 
-0.0 19.023172 -0.29189092 0.26958522 -1.0527138 0.9075671 0.0 14.428051 
-0.0 14.428051 -0.15521508 0.1829038 -0.845419 1.9358484 0.0 23.065294 
-0.0 23.065294 -0.13149762 0.14811686 -0.7162557 1.0370971 0.0 15.165984 
-0.0 15.165984 -0.06236292 0.08321518 -0.9067523 0.9922458 0.0 13.664733 
-0.0 13.664733 -0.06471479 0.1024472 -0.15943134 0.7988499 0.0 19.025272 
-0.0 19.025272 -0.06320205 0.08291938 -0.32540628 0.5203079 0.0 6.727217 
-0.0 6.727217 -0.037707984 0.051601283 -0.25622904 0.11251946 0.0 3.2003012 
-0.0 3.2003012 -0.056007143 0.09549151 -0.11591503 0.06267536 0.0 4.321189 
-0.0 4.321189 -0.060094673 0.10868926 -0.105962686 0.09584572 0.0 2.936297 
-0.0 2.936297 -0.034618977 0.05792674 -0.4237576 0.11035452 0.0 4.87262 
-0.0 4.87262 -0.035480656 0.058295887 -0.21477045 0.14263579 0.0 10.32133 
-0.0 10.32133 -0.08929961 0.11301676 -0.20798548 0.47405547 0.0 13.91 
-0.0 13.91 -0.6627122 0.35539475 -1.0631907 0.9830786 -70.45701 87.34367 
diff --git a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc b/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc
deleted file mode 100644
index bb792eaf71e851a5bf9791362aa09991dbc8ef68..0000000000000000000000000000000000000000
--- a/hpvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100/src.cc
+++ /dev/null
@@ -1,164 +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("vgg16_cifar100_test/"); 
-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(), 0,64,3,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,64,64,3,3); 
-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(), 0,1,64,1,1); 
-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(), 0,128,64,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,128,128,3,3); 
-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(), 0,1,128,1,1); 
-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(), 0,256,128,3,3); 
-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(), 0,1,256,1,1); 
-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(), 0,256,256,3,3); 
-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(), 0,1,256,1,1); 
-std::string conv2d_7_w_path =  dir_prefix + std::string("conv2d_7_w.bin"); 
-void* conv2d_7_w =  readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3); 
-std::string conv2d_7_b_path =  dir_prefix + std::string("conv2d_7_b.bin"); 
-void* conv2d_7_b =  readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1); 
-std::string conv2d_8_w_path =  dir_prefix + std::string("conv2d_8_w.bin"); 
-void* conv2d_8_w =  readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3); 
-std::string conv2d_8_b_path =  dir_prefix + std::string("conv2d_8_b.bin"); 
-void* conv2d_8_b =  readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_9_w_path =  dir_prefix + std::string("conv2d_9_w.bin"); 
-void* conv2d_9_w =  readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_9_b_path =  dir_prefix + std::string("conv2d_9_b.bin"); 
-void* conv2d_9_b =  readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_10_w_path =  dir_prefix + std::string("conv2d_10_w.bin"); 
-void* conv2d_10_w =  readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_10_b_path =  dir_prefix + std::string("conv2d_10_b.bin"); 
-void* conv2d_10_b =  readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_11_w_path =  dir_prefix + std::string("conv2d_11_w.bin"); 
-void* conv2d_11_w =  readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_11_b_path =  dir_prefix + std::string("conv2d_11_b.bin"); 
-void* conv2d_11_b =  readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_12_w_path =  dir_prefix + std::string("conv2d_12_w.bin"); 
-void* conv2d_12_w =  readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_12_b_path =  dir_prefix + std::string("conv2d_12_b.bin"); 
-void* conv2d_12_b =  readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1); 
-std::string conv2d_13_w_path =  dir_prefix + std::string("conv2d_13_w.bin"); 
-void* conv2d_13_w =  readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3); 
-std::string conv2d_13_b_path =  dir_prefix + std::string("conv2d_13_b.bin"); 
-void* conv2d_13_b =  readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1); 
-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(), 0,1,1,512,512); 
-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(), 0,1,512,1,1); 
-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(), 0,1,1,512,100); 
-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(), 0,1,100,1,1); 
-
-
-
-startMemTracking(); 
-
-int test_input_size = 10000; 
-int batch_size = 10000; 
-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, 1, 1, 1, 1, 1, 0); 
-void* var_1 = tensorAdd(var_0, conv2d_1_b); 
-void* var_2 = tensorRelu(var_1); 
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0); 
-void* var_5 = tensorAdd(var_4, conv2d_2_b); 
-void* var_6 = tensorRelu(var_5); 
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2); 
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0); 
-void* var_9 = tensorAdd(var_8, conv2d_3_b); 
-void* var_10 = tensorRelu(var_9); 
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0); 
-void* var_13 = tensorAdd(var_12, conv2d_4_b); 
-void* var_14 = tensorRelu(var_13); 
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2); 
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0); 
-void* var_17 = tensorAdd(var_16, conv2d_5_b); 
-void* var_18 = tensorRelu(var_17); 
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0); 
-void* var_21 = tensorAdd(var_20, conv2d_6_b); 
-void* var_22 = tensorRelu(var_21); 
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0); 
-void* var_25 = tensorAdd(var_24, conv2d_7_b); 
-void* var_26 = tensorRelu(var_25); 
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2); 
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0); 
-void* var_29 = tensorAdd(var_28, conv2d_8_b); 
-void* var_30 = tensorRelu(var_29); 
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0); 
-void* var_33 = tensorAdd(var_32, conv2d_9_b); 
-void* var_34 = tensorRelu(var_33); 
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0); 
-void* var_37 = tensorAdd(var_36, conv2d_10_b); 
-void* var_38 = tensorRelu(var_37); 
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2); 
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0); 
-void* var_41 = tensorAdd(var_40, conv2d_11_b); 
-void* var_42 = tensorRelu(var_41); 
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0); 
-void* var_45 = tensorAdd(var_44, conv2d_12_b); 
-void* var_46 = tensorRelu(var_45); 
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0); 
-void* var_49 = tensorAdd(var_48, conv2d_13_b); 
-void* var_50 = tensorRelu(var_49); 
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2); 
-void* var_54 = tensorGemmGPU(var_51, dense_1_w); 
-void* var_55 = tensorAdd(var_54, dense_1_b); 
-void* var_56 = tensorRelu(var_55); 
-void* var_58 = tensorGemmGPU(var_56, dense_2_w); 
-void* var_59 = tensorAdd(var_58, dense_2_b); 
-void* var_60 = tensorSoftmax(var_59); 
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
-
-float accuracy = computeAccuracy2(labels, batch_size, var_60); 
-final_accuracy += accuracy; 
-freeBatchMemory(); 
- 
-}
-
-final_accuracy = final_accuracy / batch_count; 
-dumpFinalAccuracy(final_accuracy); 
-
-
-llvm_hpvm_cleanupTensorRt(); 
-
-return 0; 
-
-}
diff --git a/hpvm/projects/hpvm-tensor-rt/scripts/test_dnns.py b/hpvm/projects/hpvm-tensor-rt/scripts/test_dnns.py
index 8d13a292372d81d491aedf21341c0e51859be723..11c3584a41e272527bc8141d9e9a9ed2d22ab51b 100644
--- a/hpvm/projects/hpvm-tensor-rt/scripts/test_dnns.py
+++ b/hpvm/projects/hpvm-tensor-rt/scripts/test_dnns.py
@@ -28,9 +28,8 @@ def createBaselineConfig(f_path, base_flag, num_layers):
         
 if __name__ == "__main__":
 
-    FP32_binary_paths = ["alexnet_cifar10", "alexnet2_cifar10", "resnet18_cifar10", "vgg16_cifar10", "vgg16_cifar100", "lenet_mnist", "mobilenet", "mobilenet_shallow"]
-    FP16_binary_paths = ["alexnet_half", "alexnet2_half", "resnet18_half", "vgg16_cifar10_half", "vgg16_cifar100_half", "lenet_half", "mobilenet_half", "mobilenet_shallow_half"]
-    PROMISE_binary_paths = ["alexnet_promise", "alexnet2_promise", "resnet18_promise", "vgg16_cifar10_promise", "vgg16_cifar100_promise", "mobilenet_promise", "mobilenet_shallow_promise"]
+    FP32_binary_paths = ["alexnet_cifar10_fp32", "alexnet2_cifar10_fp32", "resnet18_cifar10_fp32", "vgg16_cifar10_fp32", "vgg16_cifar100_fp32", "lenet_mnist_fp32", "mobilenet_cifar10_fp32"]
+    FP16_binary_paths = ["alexnet_cifar10_fp16", "alexnet2_cifar10_fp16", "resnet18_cifar10_fp16", "vgg16_cifar10_fp16", "vgg16_cifar100_fp16", "lenet_mnist_fp16", "mobilenet_cifar10_fp16"]
 
     fp32_results = {}
     for binary_path in FP32_binary_paths:
@@ -46,15 +45,5 @@ if __name__ == "__main__":
         fp16_results[binary_path] = accuracy
 
 
-    createBaselineConfig("promise_flags", 11, 1000)
-    promise_results = {}
-    for binary_path in PROMISE_binary_paths:
-        subprocess.call("./" + binary_path)
-        accuracy = readAccuracy("final_accuracy")
-        promise_results[binary_path] = accuracy
-
-
     printResults(fp32_results)
     printResults(fp16_results)
-    printResults(promise_results)
-
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_knobs_utils.cc b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_knobs_utils.cc
index c5d79020acbaa6d7588577934dc222f679050ecf..b272bbcab45573f03ac17305f86a99e630db2950 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_knobs_utils.cc
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_knobs_utils.cc
@@ -23,16 +23,21 @@ PerfParams::PerfParams(int row1, int col1, int skip_offset1) {
 }
 
 PerfParamSet::PerfParamSet() {
+
   printf("- knobs_file_path = %s \n", GLOBAL_KNOBS_FILE);
   std::ifstream file(GLOBAL_KNOBS_FILE);
 
+  if (!file){
+    ERROR(" Could NOT find global_knobs.txt \n");
+  }
+  
   std::string line;
   std::string partial;
   std::vector<std::string> tokens;
 
   while (std::getline(file, line)) { // Read each line
 
-    // printf ("***** line === %s ", line);
+    //printf ("***** line === %s ", line);
     std::istringstream iss(line);
     std::string token;
     while (std::getline(iss, token, '\t')) { // Read each token in the line
@@ -59,8 +64,8 @@ PerfParamSet::PerfParamSet() {
         std::getline(token_stream, tok, ',');
         int offset = atoi(tok.c_str());
 
-        printf("**** knob = %d, row = %d, col = %d, offset = %d \n\n", knob,
-               row, col, offset);
+        //printf("**** knob = %d, row = %d, col = %d, offset = %d \n\n", knob,
+        //       row, col, offset);
         PerfParams params(row, col, offset);
         perf_knob_map[knob] = params;
       }
@@ -92,9 +97,14 @@ SampParams::SampParams(int skip_rate1, int skip_offset1,
 }
 
 SampParamSet::SampParamSet() {
+
   printf("- knobs_file_path = %s \n", GLOBAL_KNOBS_FILE);
   std::ifstream file(GLOBAL_KNOBS_FILE);
 
+  if (!file){
+    ERROR("Could NOT find global_knobs.txt \n");
+  }
+  
   std::string line;
   std::string partial;
   std::vector<std::string> tokens;
@@ -114,7 +124,7 @@ SampParamSet::SampParamSet() {
         int index2 = token.find(",");
         std::string knob_str = token.substr(index2 + 1);
         int knob = atoi(knob_str.c_str());
-        printf("knob = %d \n", knob);
+        //printf("knob = %d \n", knob);
 
         std::getline(iss, token, '\t');
         std::istringstream token_stream(token);
@@ -130,7 +140,7 @@ SampParamSet::SampParamSet() {
         std::getline(token_stream, tok, ',');
         float interpolation_id = atof(tok.c_str());
 
-        printf("skip_every = %d, offset = %d \n", skip_every, offset);
+        //printf("skip_every = %d, offset = %d \n", skip_every, offset);
         SampParams params(skip_every, offset, interpolation_id);
         samp_knob_map[knob] = params;
       }
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_simulation.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_simulation.cu
index 5051b780894b6e758cf768e663739ea6b92c71e5..9a3c9ca848d443a20f1dcbb98fb3eda52ee15945 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_simulation.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_simulation.cu
@@ -1,9 +1,17 @@
+//===--------------------------- approxs_simulator.cu ---------------------===//
+//
+//===----------------------------------------------------------------------===//
+//   
+//  This file  consists of the simulation of implementation of software 
+// approximations for tensor convolutions. The approximations implemented are 
+// feature sampling and perforation for FP32 and FP16 compute precisions.  
+//
+//===----------------------------------------------------------------------===//
+
 
 #ifndef SIM_HEADER
 #define SIM_HEADER
 
-
-
 #include "tensor_runtime.h"
 #include "tensor_utils.h"
 #include "debug.h"
@@ -29,8 +37,6 @@
 #include <cassert>
 
 
-
-
 //N is new_data's size
 //n, c, h, w are the dimensions of new_data
 __global__
@@ -925,7 +931,7 @@ int getSwing(int swing){
 
 void initializeAutotuner(){
 
-  printf("initializing tuner .... \n");
+  DEBUG("initializing tuner .... \n");
   
   sampParamSet = new SampParamSet;
   perfParamSet = new PerfParamSet;  
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques2.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques.cu
similarity index 90%
rename from hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques2.cu
rename to hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques.cu
index 8f2d840362ee523a458339b848e9080a2822d92f..1b770736bab93dd6a47cb4351dd0ad054e8eb14d 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques2.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/approx_techniques.cu
@@ -1,3 +1,13 @@
+//===--------------------------- approxtechniques.cu ---------------------===//
+//
+//===----------------------------------------------------------------------===//
+//   
+//  This file  consists of the custom implementation of software approximations
+// for tensor convolutions. The approximations implemented are feature sampling
+// and perforation for FP32 and FP16 compute precisions.  
+//
+//===----------------------------------------------------------------------===//
+ 
 
 #include "tensor_utils.h"
 #include "approx_utils.h"
@@ -159,19 +169,13 @@ __global__ void convToGemmHalfInputNewIrregular(__half * const __restrict__ outp
       if(n < N) { //is thread id within bounds?
           for(int i = 0; i < KH; i++) {
               for(int j = 0; j < KW; j++) {
-                  //const int ki = c * KH * KW + i;
-                  //const int kj = c * KH * KW + j;
+
                   const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
                   if((filter_elem_num - skip_offset) % skip_every) {
                     const int condition = (filter_elem_num < skip_offset);
                      const int output_col = condition * filter_elem_num 
                                     + (!condition) * (filter_elem_num - ((filter_elem_num + 1 - skip_offset) / skip_every) 
-                                                         - ((filter_elem_num + 1 - skip_offset) % skip_every > 0));                   
-                  //if(filter_elem_num % skip_every != skip_offset) {
-                  // int output_col = filter_elem_num -
-                    //  (filter_elem_num/skip_every + (filter_elem_num % skip_every > skip_offset));
-                   //if(skip_every == 1)
-                   //    output_col = filter_elem_num;
+                                                         - ((filter_elem_num + 1 - skip_offset) % skip_every > 0));                   		     
                     const int out_index = ((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w;
                     //((output_col*N + n) * H_out + h) * W_out + w;
                     if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
@@ -203,22 +207,16 @@ __global__ void convToGemmHalfInputNewIrregular2(__half * const __restrict__ out
     if(n < N) { //is thread id within bounds?
         for(int i = 0; i < KH; i++) {
             for(int j = 0; j < KW; j++) {
-                //const int ki = c * KH * KW + i;
-                //const int kj = c * KH * KW + j;
-                const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
+
+	        const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
                 if((filter_elem_num - skip_offset) % skip_every) {
                     const int condition = (filter_elem_num < skip_offset);
                     const int output_col = condition * filter_elem_num
                                         + (!condition) * (filter_elem_num - ((filter_elem_num + 1 - skip_offset) / skip_every)
                                         - ((filter_elem_num + 1 - skip_offset) % skip_every > 0));
-                    //if(filter_elem_num % skip_every != skip_offset) {
-                    // int output_col = filter_elem_num -
-                    //  (filter_elem_num/skip_every + (filter_elem_num % skip_every > skip_offset));
-                    //if(skip_every == 1)
-                    //    output_col = filter_elem_num;
+
                     const int out_index = ((output_col * N + n) * H_out + h) * W_out + w;
-                    //((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w;
-                    //((output_col*N + n) * H_out + h) * W_out + w
+                    
                     if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
                         output[out_index] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
                     else
@@ -278,15 +276,13 @@ __global__ void convToGemmPerfRow(float * const __restrict__ output,
     }
     const int inH = h_index * V_stride - V_pad;
     const int inW = w * H_stride - H_pad; //input width index (col number)
-   //#pragma unroll
-    //for (int ki = 0; ki < KH * KW; ki++) {
-      //  int i = ki / KW;
-      //  int j = ki % KW;
+
     for(int i = 0; i < KH; i++) {
         for(int j = 0; j < KW; j++) {
 	const int filter_elem_num = c * KH * KW + i* KW + j; //index of this filter element
-    const int out_index = ((n * C * KH * KW + filter_elem_num) * H_eff + h) * W_out + w;
-    if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
+	const int out_index = ((n * C * KH * KW + filter_elem_num) * H_eff + h) * W_out + w;
+
+	if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
 	  output[out_index] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
 	else
 	  output[out_index] = 0;
@@ -347,11 +343,7 @@ __global__ void convToGemmPerfCol(float * const __restrict__ output,
     }
     const int inW = w_index * H_stride - H_pad; 
     const int inH = h * V_stride - V_pad; //input height index (row number)
-    //#pragma unroll
-    //for (int ki = 0; ki < KH * KW; ki++) {
-      //  int i = ki / KW;
-       // int j = ki % KW;
-    
+
     for(int i = 0; i < KH; i++) {
       for(int j = 0; j < KW; j++) {
 	const int filter_elem_num = c * KH * KW  + i * KW + j; //index of this filter element
@@ -417,11 +409,8 @@ __global__ void convToGemmPerfRowHalf(__half * const __restrict__ output,
     }
     const int inH = h_index * V_stride - V_pad;
     const int inW = w * H_stride - H_pad; //input width index (col number)
-  // #pragma unroll
-    //for (int ki = 0; ki < KH * KW; ki++) {
-     //   int i = ki / KW; 
-     //   int j = ki % KW;
-   
+
+    
    for(int i = 0; i < KH; i++) {
       for(int j = 0; j < KW; j++) {
         const int filter_elem_num = c * KH * KW + i * KW + j; //index of this filter element
@@ -455,38 +444,31 @@ __global__ void convToGemmPerfRowHalf2(__half * const __restrict__ output,
         }                              
         const int inH = h_index * V_stride - V_pad;
         const int inW = w * H_stride - H_pad; //input width index (col number)
-        // #pragma unroll
-        //for (int ki = 0; ki < KH * KW; ki++) {
-            //   int i = ki / KW; 
-            //   int j = ki % KW; 
-            for(int i = 0; i < KH; i++) {
-                for(int j = 0; j < KW; j++) {
-                    const int filter_elem_num = c * KH * KW + i * KW + j; //index of this filter element
-                    const int out_index = ((filter_elem_num * N + n) * H_eff + h) * W_out + w;
-                    //((n * C * KH * KW + filter_elem_num) * H_eff + h) * W_out + w;
-                    if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
-                        output[out_index] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
-                    else
-                        output[out_index] = 0;
-                }
-            }
+
+
+	for(int i = 0; i < KH; i++) {
+	  for(int j = 0; j < KW; j++) {
+	    const int filter_elem_num = c * KH * KW + i * KW + j; //index of this filter element
+	    const int out_index = ((filter_elem_num * N + n) * H_eff + h) * W_out + w;
+
+	    if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
+	      output[out_index] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
+	    else
+	      output[out_index] = 0;
+
+	  }
+	}
+	
     }
 }
 
 __global__ void approxInterpolateRowHalf(int N, int old_h, int j, int c, int h, int w,
                           __half *old_data, __half *new_data, int x, int start) {
 
-    //const int index = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-    //const int n = tx / (c * h * w); //output image number
-    //const int stride = blockDim.x * gridDim.x;
+
     const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
     const int n = tx / (c * h * w); //output image number
     if(n < N) {
-    //for(int i = index; i < N; i += stride){
-        //const int col = ((i % (c * h * w)) % (h * w)) % w;
-        //const int row = ((i % (c * h * w)) % (h * w)) / w;
-        //const int ch = (i % (c * h * w)) / (h * w);
-       // const int n = i / (c * h * w);
 
         const int ch = tx % (c * h * w) / (h * w); //filter number
         const int row = tx % (h * w) / w; //output height index (row number)
@@ -517,17 +499,9 @@ __global__ void approxInterpolateRowHalf(int N, int old_h, int j, int c, int h,
 __global__ void approxInterpolateRowHalf2(int N, int old_h, int b, int c, int h, int w,
                           __half *old_data, __half *new_data, int x, int start) {
     
-    //const int index = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-    //const int n = tx / (c * h * w); //output image numbe
-    //const int stride = blockDim.x * gridDim.x;
     const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
     const int n = tx / (c * h * w); //output image number
     if(n < N) {
-    //for(int i = index; i < N; i += stride){
-        //const int col = ((i % (c * h * w)) % (h * w)) % w;
-        //const int row = ((i % (c * h * w)) % (h * w)) / w;
-        //const int ch = (i % (c * h * w)) / (h * w);
-        //const int n = i / (c * h * w);
         
         const int ch = tx % (c * h * w) / (h * w); //filter number
         const int row = tx % (h * w) / w; //output height index (row number)
@@ -544,13 +518,11 @@ __global__ void approxInterpolateRowHalf2(int N, int old_h, int b, int c, int h,
         } else if((row - start) % x == 0) {
             const int row_index = row - ((row + 1 - start) / x);
             const int output_index = ch * (b * old_h * w) + n * (old_h * w) + row_index * (w) + col;
-            //n * (c * old_h * w) + ch * (old_h * w) + row_index * (w) + col;
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
                     __hdiv(__hadd(old_data[output_index], old_data[output_index - w]), 2);
         } else {
             const int row_index = row - ((row + 1 - start) / x) - ((row + 1 - start) % x > 0);
             const int output_index = ch * (b * old_h * w) + n * (old_h * w) + row_index * (w) + col;
-            //n * (c * old_h * w) + ch * (old_h * w) + row_index * (w) + col;
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] = old_data[output_index];
         }
     }
@@ -577,11 +549,7 @@ __global__ void convToGemmPerfColHalf(__half * const __restrict__ output,
     }
     const int inW = w_index * H_stride - H_pad;
     const int inH = h * V_stride - V_pad; //input height index (row number)
-     //#pragma unroll
-    //  for (int ki = 0; ki < KH * KW; ki++) {               
-      //    int i = ki / KW;
-       //   int j = ki % KW; 
-    
+
     for(int i = 0; i < KH; i++) {
       for(int j = 0; j < KW; j++) {
         const int filter_elem_num = c * KH * KW + i * KW + j; //index of this filter element
@@ -616,10 +584,8 @@ __global__ void convToGemmPerfColHalf2(__half * const __restrict__ output,
           }
           const int inW = w_index * H_stride - H_pad;
           const int inH = h * V_stride - V_pad; //input height index (row number)
-          //#pragma unroll
-          //  for (int ki = 0; ki < KH * KW; ki++) {               
-              //    int i = ki / KW;
-              //   int j = ki % KW; 
+
+
           for(int i = 0; i < KH; i++) {
               for(int j = 0; j < KW; j++) {
                   const int filter_elem_num = c * KH * KW + i * KW + j; //index of this filter elemen
@@ -637,15 +603,6 @@ __global__ void convToGemmPerfColHalf2(__half * const __restrict__ output,
 __global__ void approxInterpolateColHalf(int N, int old_w, int b, int c, int h, int w,
                                                 __half *old_data, __half *new_data, int x, int start) {
 
-    //const int index = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-    //const int stride = blockDim.x * gridDim.x;
-    
-    //for(int i = index; i < N; i += stride){
-      //  const int col = ((i % (c * h * w)) % (h * w)) % w;
-      //  const int row = ((i % (c * h * w)) % (h * w)) / w;
-      //  const int ch = (i % (c * h * w)) / (h * w);
-      //  const int n = i / (c * h * w);
-
     const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
     const int n = tx / (c * h * w); //output image number
     if(n < N) {
@@ -678,14 +635,6 @@ __global__ void approxInterpolateColHalf(int N, int old_w, int b, int c, int h,
 __global__ void approxInterpolateColHalf2(int N, int old_w, int b, int c, int h, int w,
                                                 __half *old_data, __half *new_data, int x, int start) {
     
-    //const int index = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-    //const int stride = blockDim.x * gridDim.x;
-    
-   // for(int i = index; i < N; i += stride){
-       // const int col = ((i % (c * h * w)) % (h * w)) % w;
-       // const int row = ((i % (c * h * w)) % (h * w)) / w;
-       // const int ch = (i % (c * h * w)) / (h * w);
-       // const int n = i / (c * h * w);
     const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
     const int n = tx / (c * h * w); //output image number
     if(n < N) {
@@ -695,25 +644,23 @@ __global__ void approxInterpolateColHalf2(int N, int old_w, int b, int c, int h,
         if(col < start) {
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col]
                         = old_data[ch * (b * h * old_w) + n * (h * old_w) + row * old_w + col];
-                        //n * (c * h * old_w) + ch * (h * old_w) + row * old_w + col];
+   
         } else if(col == w - 1) {
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
                             old_data[ch * (b * h * old_w) + n * (h * old_w) + row * (old_w) + old_w - 1];
-                            //n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + old_w - 1];
+   
         } else if (col == 0) {
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
                         old_data[ch * (b * h * old_w) + n * (h * old_w) + row * (old_w)];
-                        //n * (c * h * old_w) + ch * (h * old_w) + row * (old_w)];
+   
         } else if((col - start) % x == 0) {
             const int col_index = col - ((col + 1 - start) / x);
             const int output_index = ch * (b * h * old_w) + n * (h * old_w) + row * old_w + col_index;
-            //n * (c * h * old_w) + ch * (h * old_w) + row * old_w + col_index;
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
                             __hdiv(__hadd(old_data[output_index], old_data[output_index - 1]), 2);
         } else {
             const int col_index = col - ((col + 1 - start) / x) - ((col + 1 - start) % x > 0);
             const int output_index = ch * (b * h * old_w) + n * (h * old_w) + row * old_w + col_index;
-            //const int output_index = n * (c * h * old_w) + ch * (h * old_w) + row * old_w + col_index;
             new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] = old_data[output_index];
         }
     }
@@ -749,6 +696,7 @@ __global__ void convToGemmFullInputRegular(float * const __restrict__ output,
              in_index = ((fi - offset + 1) * skip_every) / (skip_every - 1)
                         + (((fi - offset + 1) * skip_every) % (skip_every - 1) > 0) + offset - 1;
         }
+	 
         const int i = (in_index % (KW * KH)) / KW;
         const int j = in_index % KW;
         const int out_index = ((n * reduced_filter_elem + fi) * H_out + h) * W_out + w; 
@@ -799,13 +747,15 @@ __global__ void convToGemmFullInputIrregular(float * const __restrict__ output,
             }
         }
     }
+
+    
 }
 
 __global__ void createReducedFiltersFullRegular(float * output,
-					 const float * const __restrict input, const int NF,
-					 const int num_filter_elem, const int reduced_filter_elem, 
-                     const int channels,
-					 const int skip_every, const int skip_offset, const float fac) {
+						const float * const __restrict input, const int NF,
+						const int num_filter_elem, const int reduced_filter_elem, 
+						const int channels,
+						const int skip_every, const int skip_offset, const float fac) {
   
   const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
   const int fIdx = tx / reduced_filter_elem; //filter index
@@ -816,11 +766,13 @@ __global__ void createReducedFiltersFullRegular(float * output,
       int in_index;
       if(offset < channel_offset) {
         in_index = offset;
-     } else {
+      }
+      else {
          in_index = ((offset - channel_offset + 1) * skip_every) / (skip_every - 1)
                   + (((offset - channel_offset + 1) * skip_every) % (skip_every - 1) > 0) + channel_offset -1;
-     }
-    output[fIdx * reduced_filter_elem + offset] = fac * input[num_filter_elem * fIdx + in_index];
+      }
+      
+      output[fIdx * reduced_filter_elem + offset] = fac * input[num_filter_elem * fIdx + in_index];
   }
 }
 
@@ -863,30 +815,23 @@ __global__ void convToGemmHalfInputRegular(__half * const __restrict__ output,
     const int inH = h * V_stride - V_pad; //input height index (row number)
     const int inW = w * H_stride - H_pad; //input width index (col number)
     
-    #pragma unroll
-    //for(int fi = 0; fi < reduced_filter_elem; fi++) {
-         //const int ch = (fi * C) / reduced_filter_elem;
+      #pragma unroll
       for(int ki = 0; ki < reduced_filter_elem / C; ki++) {
-        const int fi = ch * (reduced_filter_elem / C) + ki;
-        const int offset = (skip_offset + ch) % skip_every;
-         //int in_index;
+         const int fi = ch * (reduced_filter_elem / C) + ki;
+         const int offset = (skip_offset + ch) % skip_every;
+   
          const bool condition = (fi < offset);
          const int in_index = condition * fi + (!condition) * (((fi - offset + 1) * skip_every) / (skip_every - 1)
                                                 + (((fi - offset + 1) * skip_every) % (skip_every - 1) > 0) + offset - 1);
-         //if(fi < offset) {
-         //    in_index = fi;
-         //} else {
-         //    in_index = ((fi - offset + 1) * skip_every) / (skip_every - 1) 
-           //             + (((fi - offset + 1) * skip_every) % (skip_every - 1) > 0) + offset - 1;
-       // }
-        const int i = (in_index % (KW * KH)) / KW;
-        const int j = in_index % KW;
-        const int out_index = ((n * reduced_filter_elem + fi) * H_out + h) * W_out + w;
-        if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W) { 
-            output[out_index] = input[((n * C + ch) * H + (inH + i)) * W + (inW + j)];
-        } else {
+  
+         const int i = (in_index % (KW * KH)) / KW;
+         const int j = in_index % KW;
+         const int out_index = ((n * reduced_filter_elem + fi) * H_out + h) * W_out + w;
+         if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W) { 
+             output[out_index] = input[((n * C + ch) * H + (inH + i)) * W + (inW + j)];
+         } else {
             output[out_index] = 0;
-        }
+         }
       }
     }
 }
@@ -912,26 +857,20 @@ __global__ void convToGemmHalfInputRegular2(__half * const __restrict__ output,
           
           #pragma unroll
            for(int ki = 0; ki < reduced_filter_elem / C; ki++) {
-               const int fi = ch * (reduced_filter_elem / C) + ki;
-          //for(int fi = 0; fi < reduced_filter_elem; fi++) {
-           //   const int ch = (fi * C) / reduced_filter_elem;
+
+	      const int fi = ch * (reduced_filter_elem / C) + ki;	          
               const int offset = (skip_offset + ch) % skip_every;
               const int condition = (fi < offset);
-             const int in_index = condition * fi + (! condition) * (((fi - offset + 1) * skip_every) / (skip_every - 1)
+              const int in_index = condition * fi + (! condition) * (((fi - offset + 1) * skip_every) / (skip_every - 1)
                                                           + (((fi - offset + 1) * skip_every) % (skip_every - 1) > 0) + offset - 1);
-             // int in_index;
-              //if(fi < offset) {
-               //   in_index = fi;
-              //} else {
-               //   in_index = ((fi - offset + 1) * skip_every) / (skip_every - 1)
-                 //               + (((fi - offset + 1) * skip_every) % (skip_every - 1) > 0) + offset - 1;
-             // }
+         
               const int i = (in_index % (KW * KH)) / KW;
               const int j = in_index % KW;
               const int out_index = ((fi * N + n) * H_out + h) * W_out + w;
               if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W) {
                   output[out_index] = input[((n * C + ch) * H + (inH + i)) * W + (inW + j)];
-              } else {
+              }
+	      else {
                   output[out_index] = 0;
              }
         }
@@ -961,20 +900,15 @@ __global__ void convToGemmHalfInputIrregular(__half * const __restrict__ output,
             const int condition = (fi < skip_offset);
             const int in_index = condition * fi + (! condition) * (((fi - skip_offset + 1) * skip_every) / (skip_every - 1)
                                              + (((fi - skip_offset + 1) * skip_every) % (skip_every - 1) > 0) + skip_offset - 1);
-            //int in_index;
-            //if(fi < skip_offset) {
-             //   in_index = fi;
-            //} else {        
-              //  in_index = ((fi - skip_offset + 1) * skip_every) / (skip_every - 1)
-              //              + (((fi - skip_offset + 1) * skip_every) % (skip_every - 1) > 0) + skip_offset - 1;
-           // }
-            const int ch = in_index / (KW * KH);
+
+	    const int ch = in_index / (KW * KH);
             const int i = (in_index % (KW * KH)) / KW;
             const int j = in_index % KW; 
             const int out_index = ((n * reduced_filter_elem + fi) * H_out + h) * W_out + w;
             if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W) {
                 output[out_index] = input[((n * C + ch) * H + (inH + i)) * W + (inW + j)];
-            } else {
+            }
+	    else {
                 output[out_index] = 0;
             }
         }
@@ -1003,18 +937,11 @@ __global__ void convToGemmHalfInputIrregular2(__half * const __restrict__ output
             const int condition = (fi < skip_offset);
             const int in_index = condition * fi + (!condition) * (((fi - skip_offset + 1) * skip_every) / (skip_every - 1)
                                  + (((fi - skip_offset + 1) * skip_every) % (skip_every - 1) > 0) + skip_offset - 1);
-           // int in_index;
-           // if(fi < skip_offset) {
-           //     in_index = fi;
-           // } else {
-            //    in_index = ((fi - skip_offset + 1) * skip_every) / (skip_every - 1)
-                   //             + (((fi - skip_offset + 1) * skip_every) % (skip_every - 1) > 0) + skip_offset - 1;
-           // }
+      
             const int ch = in_index / (KW * KH);
             const int i = (in_index % (KW * KH)) / KW;
             const int j = in_index % KW;
             const int out_index = ((fi * N + n) * H_out + h) * W_out + w;
-            //const int out_index = ((n * reduced_filter_elem + fi) * H_out + h) * W_out + w;
             if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W) {
                 output[out_index] = input[((n * C + ch) * H + (inH + i)) * W + (inW + j)];
             } else {
@@ -1032,11 +959,8 @@ __global__ void createReducedFiltersHalfRegular(__half * output,
                                          const int skip_every, const int skip_offset, const float fac) {
 
   const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-  //const int stride = blockDim.x * gridDim.x;
-  
-  //#pragma unroll
-  //for (int i = tx; i < NF; i += stride) {
-    const int fIdx = tx / reduced_filter_elem; //filter index
+
+  const int fIdx = tx / reduced_filter_elem; //filter index
   if(fIdx < NF) {
     const int offset = tx % reduced_filter_elem; //offset within filter
     const int ch = (offset * channels) / reduced_filter_elem;
@@ -1045,15 +969,9 @@ __global__ void createReducedFiltersHalfRegular(__half * output,
     const int in_index = condition * offset + (!condition) * (((offset - channel_offset + 1) * skip_every) / (skip_every - 1)
                           + (((offset - channel_offset + 1) * skip_every) % (skip_every - 1) > 0) + channel_offset - 1);
       
-     // int in_index;
-     // if(offset < channel_offset) {
-      //  in_index = offset;
-     //} else {
-       //  in_index = ((offset - channel_offset + 1) * skip_every) / (skip_every - 1)
-         //         + (((offset - channel_offset + 1) * skip_every) % (skip_every - 1) > 0) + channel_offset -1;
-    // }
     output[fIdx * reduced_filter_elem + offset] =  __hmul(__float2half_rn(fac), input[num_filter_elem * fIdx + in_index]); 
  }
+  
 }
 
 __global__ void createReducedFiltersHalfIrregular(__half * output,
@@ -1061,21 +979,20 @@ __global__ void createReducedFiltersHalfIrregular(__half * output,
                      const int num_filter_elem, const int reduced_filter_elem,
                      const int skip_every, const int skip_offset, const float fac) {
 
-      const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
-     //const int stride = blockDim.x * gridDim.x;
-      //#pragma unroll
-      //for (int i = tx; i < NF; i += stride) { 
+  const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
+  const int fIdx = tx / reduced_filter_elem; //filter index
   
-      const int fIdx = tx / reduced_filter_elem; //filter index
-      if(fIdx < NF) {
-        const int offset = tx % reduced_filter_elem; //offset within filter
-        const int condition = (offset < skip_offset);
-        int in_index = condition * offset + (!condition) * (((offset - skip_offset + 1) * skip_every) / (skip_every - 1)
+  if(fIdx < NF) {
+
+    const int offset = tx % reduced_filter_elem; //offset within filter
+    const int condition = (offset < skip_offset);
+    
+    int in_index = condition * offset + (!condition) * (((offset - skip_offset + 1) * skip_every) / (skip_every - 1)
                      + (((offset - skip_offset + 1) * skip_every) % (skip_every - 1) > 0) + skip_offset - 1);
-        //}
-        output[fIdx * reduced_filter_elem + offset] =  __hmul(__float2half_rn(fac), input[num_filter_elem * fIdx + in_index]); 
-    //}
+        
+    output[fIdx * reduced_filter_elem + offset] =  __hmul(__float2half_rn(fac), input[num_filter_elem * fIdx + in_index]); 
   }
+      
 }
 
 
@@ -1102,7 +1019,7 @@ __global__ void convToGemmApprox(float * const __restrict__ output,
       for(int j = 0; j < KW; j++) {
 	const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
 	if(filter_elem_num % skip_every != skip_every-1) { //are we including this filter element?
-	  const int output_col = filter_elem_num - (filter_elem_num/skip_every); //calculate output column, taking skipping into account
+	  const int output_col = filter_elem_num - (filter_elem_num/skip_every); //cal output column, taking skipping into account
 	  if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
 	    output[((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
 	  else
@@ -1120,8 +1037,6 @@ void* tensorConvPerfCuda(void* input_ptr, void* filter_ptr,
 			 int horizontal_stride, int conv_mode, int conv_groups,
 			 int row, int col, int start){
 
-  //////INFO("*** TensorConvolution (output perforation) \n");
-  //Event("Conv");
   Tensor* input = (Tensor*)input_ptr;
   Tensor* filter = (Tensor*)filter_ptr;
   //FIXME: Current hack to preserve backward compatibilty
@@ -1134,10 +1049,8 @@ void* tensorConvPerfCuda(void* input_ptr, void* filter_ptr,
   hostToDeviceCopy(input);
   hostToDeviceCopy(filter);
 
-  //Event("H2F_start");
   convertToFP32(input);
   convertToFP32(filter);
-  //Event("H2F_end");
   
   long int n, c, h, w; // output dimensions
   n = input->dims.dim_sizes[0];
@@ -1211,14 +1124,14 @@ void* tensorConvPerfCuda(void* input_ptr, void* filter_ptr,
 
     freeTensor(output);
     cudaFree(convData);
-  } else if(col > 1){
+  }
+  else if(col > 1){
     output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, //input->data_type,
 				     CUDNN_TENSOR_NCHW, n, c, h, w_eff);
 
     // NOTE: Changing output tensor placement from host to device
     changeTensorPlacement(output, DEVICE);
-    // NOTE: Necessary to insert the above call for every output tensor
-    //total number of filter elem
+
     const long int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
 
     float * convData;
@@ -1540,7 +1453,8 @@ void* tensorConvApprox(void* input_ptr, void* filter_ptr,
     cudaFree(convData);
     cudaFree(reducedFilter);
   } else {
-      INFO("FP32 BASELINE\n");
+
+      //INFO("FP32 BASELINE\n");
       Tensor *output = (Tensor*)create4DTensor((cudnnDataType_t) float_type,
                                CUDNN_TENSOR_NCHW, n, c, h, w);
     changeTensorPlacement(output, DEVICE);
@@ -1986,14 +1900,12 @@ void* tensorConvApproxHalf2(void* input_ptr, void* filter_ptr,
         freeTensor(output);
         cudaFree(convData);
   }
-//    INFO("CONV DONE\n");
+
   profileEvent("H2F_start");
   convertToFP32_offline(new_output);
-  //convertToFP32(input);
-  //convertToFP32(filter);
+
   profileEvent("H2F_end");
-  //profileEvent("#Conv_end");
-  //INFO("CONVOLUTION END\n");
+
   return new_output;
 }
 
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/debug.cc b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/debug.cc
index ebb7e73f2b5a019954e7390f3eb8fadc96a3719e..3e4aecb824a93b932ef2146380b86496f71b0f28 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/debug.cc
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/debug.cc
@@ -3,8 +3,9 @@
 #ifndef RUNTIME_DEBUG
 #define RUNTIME_DEBUG
 
-#define LOG_DEBUG 1 // Sets the debug logging to true
+#define LOG_DEBUG 0 // Sets the debug logging to true
 #define LOG_INFO 1  // Sets the info logging to true
+#define LOG_ERROR 1  // Print Errors 
 #define ASSERT_FLAG // Sets assertions to true (opposite of NDEBUG macro)
 
 #include "debug.h"
@@ -35,7 +36,7 @@ void DEBUG(const char *format, ...) {
 }
 
 void ERROR(const char *format, ...) {
-  if (!LOG_DEBUG) // Don't print if logging info is disabled
+  if (!LOG_ERROR) // Don't print if logging info is disabled
     return;
   va_list args;
   va_start(args, format);
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/error.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/error.cu
index 6b8ee15a42106b2d6857065941324e50157763d5..7a700b435efe464153fbba7997662c7dfa970385 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/error.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/error.cu
@@ -55,7 +55,7 @@ void readOpenTunerFlags(const char* file_name){
   
   FILE* fp = fopen(file_name, "r");
   if(fp == NULL){
-    INFO("\nWARNING: File 'opentuner_flags' not found \n\n\n");
+    DEBUG("\n WARNING: File 'opentuner_flags' not found \n\n\n");
     return;
   }
     
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/half_precision_api.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/half_precision_api.cu
index 1990967deff6bc85cc8c9fc666ab497fb6d77991..f24e8b58dbeb5a49e0eaf51cfac1f2d2f3148caa 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/half_precision_api.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/half_precision_api.cu
@@ -49,8 +49,8 @@ void* tensorHalfGemm(void* lhs_ptr, void* rhs_ptr){
   Tensor* lhs = (Tensor*) lhs_ptr;
   Tensor* rhs = (Tensor*) rhs_ptr;
 
-  INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
-  INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
+  DEBUG("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
+  DEBUG("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
 
   hostToDeviceCopy(lhs);
   hostToDeviceCopy(rhs);
@@ -76,7 +76,7 @@ void* tensorHalfGemm(void* lhs_ptr, void* rhs_ptr){
 
   int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
   // Dimension-note: Check if k is same across the two tensors
-  INFO("m = %d, n = %d, k = %d \n", m, n, k);
+  DEBUG("m = %d, n = %d, k = %d \n", m, n, k);
   if(rhs_k != k){
     ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
   }
@@ -115,14 +115,10 @@ void* tensorHalfGemm(void* lhs_ptr, void* rhs_ptr){
 
   //h2f((half*) output_half->gpu_data, output->num_elems, (float*) output->gpu_data);
 
-
   profileEvent("H2F_end");
 
-
   profileEvent("#tensorHalfGemm_end");
 
-
-
   return output;
 }
 
@@ -263,18 +259,14 @@ void* tensorHalfConvolution(void* input_ptr, void* filter_ptr,
 				     output->tensor_half_desc,
 				     output->gpu_half_data));
 
-
   profileEvent("H2F_start");
 
   convertToFP32_offline(output);
 
   profileEvent("H2F_end");
 
-
-  
   profileEvent("#tensorHalfConv_end");
 
-
   return output;
 }
 
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/init_api.cc b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/init_api.cc
index 284a75c444f54a0f3aa3412c8cd177d4ebad4e2e..8b5c4aaf93db40c038c4a9a30569318ae00d6be1 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/init_api.cc
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/init_api.cc
@@ -31,14 +31,14 @@ void llvm_hpvm_initTensorRt(int gpuid) {
 
   if (!runtime_initialized) {
 
-    printf("INITIALIZING GPU %d \n", gpuid);
+    INFO("INITIALIZING GPU %d \n", gpuid);
     // NOTE: Setting the target GPU. Can we use multiple GPUs?
     checkCudaErrors(cudaSetDevice(gpuid));
     // Initializing cuDNN and cuBlas handles
     checkCudaErrors(cublasCreate(&cublasHandle));
     checkCUDNN(cudnnCreate(&cudnnHandle));
 
-    printf("CREATED HANDLES %d \n", gpuid);
+    DEBUG("CREATED HANDLES %d \n", gpuid);
 
 #ifdef PROMISE_TUNER_ENABLED
     //    readOpenTunerFlags("opentuner_flags");
@@ -46,7 +46,7 @@ void llvm_hpvm_initTensorRt(int gpuid) {
     readOpenTunerFlags("promise_flags");
     initializeAutotuner();
 
-    printf("Read PROMISE FLAGS %d \n", gpuid);
+    DEBUG("Read PROMISE FLAGS %d \n", gpuid);
 
 #endif
 
@@ -57,7 +57,7 @@ void llvm_hpvm_initTensorRt(int gpuid) {
     runtime_initialized = true;
   }
 
-  printf("DONE INTIALIZING GPU %d \n", gpuid);
+  INFO("DONE INTIALIZING GPU %d \n\n", gpuid);
 }
 
 void llvm_hpvm_cleanupTensorRt() {
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/profiling.cc b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/profiling.cc
index 18ebcfe4ef7e532e4657303baef6ea585b402a18..8683cbb416428f4691a10d2d9cd57a7252421899 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/profiling.cc
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/profiling.cc
@@ -64,7 +64,7 @@ void profileEvent(const char *event_name, bool compare_previous = false) {
   std::chrono::duration<double, std::ratio<1>> current_time =
       time_reading - zero_time;
 
-  INFO("AbsoluteTime, Event = %s, Time = %f \n", event_name,
+  DEBUG("AbsoluteTime, Event = %s, Time = %f \n", event_name,
        current_time.count());
   profile_data.append(event_name);
   profile_data.append(event_count);
@@ -77,7 +77,7 @@ void profileEvent(const char *event_name, bool compare_previous = false) {
 
     profile_data.append("\t");
     profile_data.append(std::to_string(duration_time.count()));
-    INFO("TimeDuration, Event = %s, Time = %f \n", event_name,
+    DEBUG("TimeDuration, Event = %s, Time = %f \n", event_name,
          duration_time.count());
   }
 
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc
index 898d92c18cb8ad0b2df7a6d0c9d905c9649c53c1..9250810a2010a235074c0d29b8fe8bd63650324c 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc
@@ -1,8 +1,13 @@
-/* This file includes the API implementation of the HPVM tensor runtime built for CPU
-**
-**  Author: Hashim Sharif
-**  Email: hsharif3@illinois.edu
-*/
+//===--------------------------- tensor_runtime_cpu.cc --------------------===//
+//
+//===----------------------------------------------------------------------===//
+//   
+//  This file  consists of the custom implementation of non-approximated and 
+// approximated  versions of tensor operations to execute on CPUs. The 
+// software approximations implemented for tensor convolutions are feature 
+// sampling and perforation for FP32 compute precisions only.  
+//
+//===----------------------------------------------------------------------===//
 
 #include <algorithm>
 #include <cfloat>
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu
index 5635107d09644b22afc54175848b6e44b9c83406..319936b482c455af2fcc0280adb15d7c126c088a 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu
@@ -68,8 +68,8 @@ void* tensorAdd(void* x_ptr, void* bias_ptr){
   convertToFP32(bias);
 
   
-  INFO("x->num_elems = %d \n", x->num_elems);
-  INFO("bias->num_elems = %d \n", bias->num_elems);
+  DEBUG("x->num_elems = %d \n", x->num_elems);
+  DEBUG("bias->num_elems = %d \n", bias->num_elems);
 
   if(cudnnHandle == NULL){
     ERROR("cudnnHandle NOT initialized!! \n");    
@@ -132,7 +132,7 @@ void* tensorConvolution(void* input_ptr, void* filter_ptr,
   convertToFP32(filter);
 
   
-  INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);  
+  DEBUG("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);  
 
   checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
 
@@ -363,8 +363,8 @@ void* tensorGemmGPU(void* lhs_ptr, void* rhs_ptr ){
   Tensor* rhs = (Tensor*) rhs_ptr;
 
 
-  INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
-  INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
+  DEBUG("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
+  DEBUG("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
 
   // FIXIT: Need to be more aware of the implications of alpha and beta
   float alpha = 1.0f, beta = 0.0f;
@@ -382,7 +382,7 @@ void* tensorGemmGPU(void* lhs_ptr, void* rhs_ptr ){
 
   int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
   // Dimension-note: Check if k is same across the two tensors
-  INFO("m = %d, n = %d, k = %d \n", m, n, k);
+  DEBUG("m = %d, n = %d, k = %d \n", m, n, k);
   if(rhs_k != k){
     ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
   }
@@ -450,7 +450,7 @@ void* tensorGemmGPU(void* lhs_ptr, void* rhs_ptr ){
 
 void* tensorRelu(void* input_ptr){
 
-  INFO("*** TensorRelu \n");
+  DEBUG("*** TensorRelu \n");
   profileEvent("Relu");
 
   Tensor* input = (Tensor*) input_ptr;
@@ -700,7 +700,7 @@ void** tensorSplit(void* tensor_ptr, int num_splits, int split_dim){
   
   for(unsigned int i = 0; i < num_splits; i++){
 
-    INFO("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
+    DEBUG("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
 	 dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
 
     Tensor* split = (Tensor*) create4DTensor(tensor->data_type, tensor->data_format,
@@ -708,7 +708,7 @@ void** tensorSplit(void* tensor_ptr, int num_splits, int split_dim){
     
     size_t copy_start = i * copy_size;
     size_t copy_stride = num_splits * copy_size;
-    INFO("copy_size = %d, copy_start = %d, copy_stride = %d, tensor->size_in_bytes = %d \n",
+    DEBUG("copy_size = %d, copy_start = %d, copy_stride = %d, tensor->size_in_bytes = %d \n",
 	 copy_size, copy_start, copy_stride, tensor->size_in_bytes);
 
     int index = 0;
@@ -758,7 +758,7 @@ void* tensorConcat(void** tensors_ptr, int num_splits, int split_dim){
   Tensor* output = (Tensor*) create4DTensor(tensors[0]->data_type, tensors[0]->data_format,
 					 dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
 
-  INFO("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
+  DEBUG("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
        dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
 
 
@@ -768,7 +768,7 @@ void* tensorConcat(void** tensors_ptr, int num_splits, int split_dim){
   }
   
   size_t copy_stride = num_splits * copy_size;
-  INFO("copy_size = %d, num_copies = %d, copy_stride = %d, output->size_in_bytes = %d \n",
+  DEBUG("copy_size = %d, num_copies = %d, copy_stride = %d, output->size_in_bytes = %d \n",
        copy_size, num_copies, copy_stride, output->size_in_bytes);
 
   for(unsigned int i = 0; i < num_copies; i++){
@@ -804,7 +804,7 @@ void* tensorLRN(void* input_ptr, unsigned int LRN_window,
   cudnnLRNDescriptor_t LRNDesc;
   checkCUDNN(cudnnCreateLRNDescriptor(&LRNDesc));
 
-  INFO("window = %d, LRN_alpha = %f, LRN_beta = %f, LRN_k = %f \n",
+  DEBUG("window = %d, LRN_alpha = %f, LRN_beta = %f, LRN_k = %f \n",
        LRN_window, LRN_alpha, LRN_beta, LRN_k);
  
   
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_utils.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_utils.cu
index 6bbccfabaf22a395e91748be22e1eaddcf32c0ba..2bc62057b5c13161475b50b4a750da49146b97ce 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_utils.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_utils.cu
@@ -220,7 +220,7 @@ void set4DTensorDescriptor(struct Tensor* tensor, int data_format, size_t dim1_s
   			     &size1, &size2, &size3, &size4,
   			     &nStride, &cStride, &hStride, &wStride);
 			   
-  INFO("nStride = %d, cStride = %d, hStride = %d, wStride = %d \n",
+  DEBUG("nStride = %d, cStride = %d, hStride = %d, wStride = %d \n",
   	 nStride, cStride, hStride, wStride);
 }
 
@@ -238,16 +238,16 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
   }
 
   for(int i = 0; i < num_dims; i++){
-    INFO("strides[%d] = %d \n", i, strides[i]);
+    DEBUG("strides[%d] = %d \n", i, strides[i]);
   }
 
   int* const_dims = (int*) malloc(sizeof(int) * num_dims);
   for(int j = 0 ; j < num_dims; j++){
     const_dims[j] = (int) dim_sizes[j];
-    INFO("const_dim = %d \n", const_dims[j]);
+    DEBUG("const_dim = %d \n", const_dims[j]);
   }
   
-  INFO("data_type = %d, cuDNN_value = %d \n", tensor->data_type, CUDNN_DATA_FLOAT); 
+  DEBUG("data_type = %d, cuDNN_value = %d \n", tensor->data_type, CUDNN_DATA_FLOAT); 
   // For certain operations, the strides may need to change - in which case the descriptor
   // needs to be reinitialized
   checkCUDNN(cudnnSetTensorNdDescriptor(tensor->tensor_desc,
@@ -340,7 +340,7 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
     if(tensor->data_placement != DEVICE){
       cudaMemcpy(tensor->gpu_data, tensor->host_data, tensor->size_in_bytes,
 		 cudaMemcpyHostToDevice);
-      INFO("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
+      DEBUG("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
       tensor->data_placement = DEVICE;
     }
     else{
@@ -355,7 +355,7 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
     if(tensor->data_placement != HOST){
       cudaMemcpy(tensor->host_data, tensor->gpu_data, tensor->size_in_bytes,
 		 cudaMemcpyDeviceToHost);  
-      INFO("Moving %d bytes from GPU to host \n", tensor->size_in_bytes);
+      DEBUG("Moving %d bytes from GPU to host \n", tensor->size_in_bytes);
       tensor->data_placement = HOST;
     }
     else{
@@ -375,13 +375,13 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
     
     if(srcTensor->data_placement == HOST){
       memcpy(dstTensor->host_data, srcTensor->host_data, srcTensor->size_in_bytes);  
-      INFO("Moving %d bytes from host to host \n", srcTensor->size_in_bytes);
+      DEBUG("Moving %d bytes from host to host \n", srcTensor->size_in_bytes);
       dstTensor->data_placement = HOST;
     }
     else if (srcTensor->data_placement == DEVICE){
       cudaMemcpy(dstTensor->gpu_data, srcTensor->gpu_data, srcTensor->size_in_bytes,
 		 cudaMemcpyDeviceToDevice);
-      INFO("Moving %d bytes from GPU to GPU \n", srcTensor->size_in_bytes);
+      DEBUG("Moving %d bytes from GPU to GPU \n", srcTensor->size_in_bytes);
       dstTensor->data_placement = DEVICE;
     }
     
@@ -409,7 +409,7 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
       if(tensor->data_placement != DEVICE){
 	cudaMemcpy(tensor->gpu_data, tensor->host_data, tensor->size_in_bytes,
 		   cudaMemcpyHostToDevice);
-	INFO("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
+	DEBUG("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
 	tensor->data_placement = DEVICE;
       }
       else{
@@ -426,7 +426,7 @@ void setTensorDescriptor(struct Tensor* tensor, int num_dims,
   if(tensor == NULL)
     return;
   
-  printf("**** cur_type = %d , half_type = %d \n", tensor->cur_type, half_type);
+  //printf("**** cur_type = %d , half_type = %d \n", tensor->cur_type, half_type);
 
   if (ONLINE_PROFILING){
     if (tensor->cur_type == half_type)
diff --git a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/wrapper_runtime.cu b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/wrapper_runtime.cu
index 6759ab3b8eb340ff136238a6643c9e38a7621c7d..f9fee629e1192ee985064a5f968376d1381d9af9 100644
--- a/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/wrapper_runtime.cu
+++ b/hpvm/projects/hpvm-tensor-rt/tensor_runtime/src/wrapper_runtime.cu
@@ -145,7 +145,7 @@ extern "C"{
 	switch (activation_id) {
 	case -1:
 	  { // No activation
-	    INFO("No activation Function\n");
+	    //INFO("No activation Function\n");
 	    activation_out = add_out;
 	  }
 	  break;
@@ -259,6 +259,8 @@ extern "C"{
 			  // NOTE: out_min, out_max are only relevant for ClippedRelu
 			  float out_min, float out_max){
 
+    INFO ("*** Conv Layer \n");
+    
     NodeConfiguration *NodeConf = RC->getNodeConfiguration(hpvm_node_id);
 
     if (NodeConf->isPROMISENodeConfiguration()) {
@@ -333,9 +335,9 @@ extern "C"{
 	GPUConf->getApproxChoices();
 
 	
-	printf("*** Convolution \n ApproxChoice = %d \n  BatchNorm = %d \n CONV = %d \n", ApproxChoices[0].first,
-	       GPUNodeConfiguration::TENSOR_OP::BATCHNORM,
-	       GPUNodeConfiguration::TENSOR_OP::CONV);
+	//printf("*** Convolution \n ApproxChoice = %d \n  BatchNorm = %d \n CONV = %d \n", ApproxChoices[0].first,
+	//	       GPUNodeConfiguration::TENSOR_OP::BATCHNORM,
+	//       GPUNodeConfiguration::TENSOR_OP::CONV);
 
 	// Check for convolution as first operation
 	CUSTOM_ASSERT((ApproxChoices.size() >= 1) &&
@@ -363,7 +365,7 @@ extern "C"{
 	switch (activation_id) {
 	case -1:
 	  { // No activation
-	    INFO("No activation Function\n");
+	    //INFO("No activation Function\n");
 	    activation_out = add_out;
 	  }
 	  break;
@@ -411,13 +413,6 @@ extern "C"{
 	      // If we remove the asserts, we can have all cases handled by a single call
 	      CUSTOM_ASSERT((ApproxChoices.back().first == GPUNodeConfiguration::TENSOR_OP::POOL_MAX) &&
 			    "Expected POOL_MAX in provided Conv layer configuration");
-
-	      /*pool_out =
-		handleTensorPoolingApproximationTuples(ApproxChoices.back().second,
-						       activation_out, pool_id,
-						       pool_size, pool_size, 0, 0,
-						       pool_size, pool_size);
-	      */
 	      
 	      pool_out = handleTensorPoolingApproximationTuples(ApproxChoices.back().second,
 								activation_out, pool_id,
@@ -488,6 +483,8 @@ extern "C"{
 			// NOTE: out_min and out_max are only relevant for ClippedRelu
 			float out_min, float out_max){ 
 
+    INFO ("*** Dense Layer \n");
+    
     NodeConfiguration *NodeConf = RC->getNodeConfiguration(hpvm_node_id);
 
     if (NodeConf->isPROMISENodeConfiguration()) {
@@ -573,7 +570,7 @@ extern "C"{
 	  { // No activation
 	    CUSTOM_ASSERT((ApproxChoices.size() == 2) &&
 			  "Incorrect number of operations in provided FC layer configuration");
-	    INFO("No activation Function\n");
+	    //INFO("No activation Function\n");
 	    activation_out = add_out;
 	  }
 	  break;
@@ -625,8 +622,9 @@ extern "C"{
 
 
   void* wrapper_tensorRelu(const char* hpvm_node_id, void* input_ptr){
-    //  return tensorRelu(input_ptr);
 
+    INFO("*** Relu Operation \n");
+    
     // Only mapped to GPU - get a GPU configuration
     GPUNodeConfiguration *GPUConf =
       (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
@@ -693,7 +691,8 @@ extern "C"{
   void* wrapper_tensorBatchNorm(const char* hpvm_node_id,
 				void* input_ptr, void* gamma_ptr, void* beta_ptr,
 				void* mean_ptr, void* variance_ptr, double epsilon){
-    
+
+    INFO("*** BatchNorm Operation \n");
 
     // Only mapped to GPU - get a GPU configuration
     GPUNodeConfiguration *GPUConf =
@@ -704,11 +703,10 @@ extern "C"{
 						   int> > > > &ApproxChoices =
 
     GPUConf->getApproxChoices();
-    
 
-    printf("*** BatchNorm \n ApproxChoice = %d \n  BatchNorm = %d \n CONV = %d \n", ApproxChoices[0].first,
-	       GPUNodeConfiguration::TENSOR_OP::BATCHNORM,
-	       GPUNodeConfiguration::TENSOR_OP::CONV);
+    // printf("*** BatchNorm \n ApproxChoice = %d \n  BatchNorm = %d \n CONV = %d \n", ApproxChoices[0].first,
+    //	       GPUNodeConfiguration::TENSOR_OP::BATCHNORM,
+    //	       GPUNodeConfiguration::TENSOR_OP::CONV);
 
     // Approximation choices must be for a batchnorm operation
     CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
@@ -723,8 +721,8 @@ extern "C"{
 
 
   void* wrapper_tensorAdd(const char* hpvm_node_id, void* input_ptr, void* bias_ptr){
-    //  return tensorAdd(input_ptr, bias_ptr);
 
+   
     // Only mapped to GPU - get a GPU configuration
     GPUNodeConfiguration *GPUConf =
       (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
@@ -753,6 +751,8 @@ extern "C"{
 			      int vertical_pad, int horizontal_pad,
 			      int vertical_stride, int horizontal_stride){
 
+    INFO("*** TensorPooling Operation \n");
+    
     //  return tensorPooling(input_ptr, poolFunction, window_height, window_width,
     //		       vertical_pad, horizontal_pad, vertical_stride, horizontal_stride);
 
diff --git a/hpvm/scripts/llvm_installer.sh b/hpvm/scripts/llvm_installer.sh
index 6867cf64f4d8cb7c28a43ed3c3b85e4dc1b403cf..21ed6ee6d13ef83e0cc62f643d8e674e7c0e5a90 100755
--- a/hpvm/scripts/llvm_installer.sh
+++ b/hpvm/scripts/llvm_installer.sh
@@ -28,18 +28,120 @@ LLVM_SRC="llvm-$VERSION.src"
 
 HPVM_RT=hpvm-rt/hpvm-rt.bc
 
-read_yn "Build and install HPVM automatically?" AUTOMATE
 
-echo
-read -p "Number of threads: " NUM_THREADS
+TARGET=all
+TARGET_INPUT=all
+FLAGGED=false
+DOWNLOAD_WEIGHTS=false
+
+# Get flags
+while getopts 'hmj:t:' opt; do
+  case $opt in
+    h) 
+      echo
+      echo
+      echo "This is the help menu for HPVM installation"
+      echo
+      echo "There are 3 options for installation:"
+      echo
+      echo "-m is a manual installation flag. This will require you to install HPVM manually by running cmake and make manually." 
+      echo "For more details, refer to README.md. Defaults to automatic installation."
+      echo
+      echo "-j is the threads flag. Accepts one argument: how many threads to build with." 
+      echo "To build with 2 threads, enter -j2. Defaults to 2 threads."
+      echo
+      echo "-t is the build target flag. Accepts one argument: which build target(s) you would like to build to." 
+      echo "For single target, enter -a ARM. For multiple targets, enter -t \"X86;ARM\"." 
+      echo "Supports the following targets: AArch64, AMDGPU, ARM, BPF, Hexagon, Mips, MSP430, NVPTX, PowerPC, Sparc, SystemZ, X86, XCore."
+      echo "Defaults to targeting all supported architectures."
+      echo
+      echo "If no flags are provided, the script will use command line prompts for all options."
+      echo
+      exit
+      ;;
+    m) 
+      AUTOMATE=false
+      FLAGGED=true
+      ;;
+    j) 
+      if ! [[ $OPTARG =~ ^[0-9]+$ ]]; then
+        echo "Invalid argument for # of threads: $OPTARG"
+        exit -1;
+      else
+        NUM_THREADS=$OPTARG
+        FLAGGED=true
+      fi
+      ;;
+    t) 
+      TARGET=$OPTARG
+      FLAGGED=true
+      ;;
+  esac
+done
+
+if $FLAGGED; then
+  echo "Running with the following options:"
+  echo Automated: $AUTOMATE
+  echo Threads: $NUM_THREADS
+  echo Targets: $TARGET
+  echo Download Weights: $DOWNLOAD_WEIGHTS
+  echo
+else
+  echo "No Flags found. Using command line prompts."
+  read -p "Build and install HPVM automatically? (y or n): " AUTOMATE_INPUT
+
+  if [[ $AUTOMATE_INPUT == "" ]]; then
+    echo "No input given. Using default: $AUTOMATE"
+  elif [[ ! $AUTOMATE_INPUT == "y" ]] && [[ ! $AUTOMATE_INPUT == "n" ]]; then 
+    echo "Invalid input. Using default: $AUTOMATE"
+  elif [[ $AUTOMATE_INPUT == "n" ]]; then
+    AUTOMATE=false
+  fi
+
 
-if [ ! $NUM_THREADS -gt 0 ]; then
-  NUM_THREADS = 2
   echo
-  echo Using $NUM_THREADS threads by default.   
+  read -p "Number of threads: " NUM_THREADS_INPUT
+
+  if [[ $NUM_THREADS_INPUT == "" ]]; then
+    echo "No input given. Using default: $NUM_THREADS"
+  elif ! [[ $NUM_THREADS_INPUT =~ ^[0-9]+$ ]]; then
+    echo "Given input is not an integer. Using default: $NUM_THREADS"
+  elif [ ! $NUM_THREADS_INPUT -gt 0 ]; then
+    echo "Given input is not greater than 0. Using default: $NUM_THREADS"
+  else
+    NUM_THREADS=$NUM_THREADS_INPUT
+  fi
+  
+  echo
+  echo 
+  echo "Supports the following options: AArch64, AMDGPU, ARM, BPF, Hexagon, Mips, MSP430, NVPTX, PowerPC, Sparc, SystemZ, X86, XCore."
+  echo "If building for multiple targets, seperate options with semicolon:"
+  echo "e.g. X86;ARM"
+  read -p "Build target: " TARGET_INPUT
+  if [[ $TARGET_INPUT == "" ]]; then
+    echo "No input given. Using default: $TARGET"
+  else
+    TARGET=$TARGET_INPUT
+  fi
+  echo
+  
+  read_yn "Download weights necessary to run DNN benchmarks?" LOAD_WEIGHTS
+  if [[ $LOAD_WEIGHTS == "" ]]; then
+    echo "No input given. Weights will not be downloaded."
+  elif [[ $LOAD_WEIGHTS == "n" ]]; then
+    echo "Weights will not be downloaded."
+  else
+    DOWNLOAD_WEIGHTS=$LOAD_WEIGHTS
+  fi
   echo
-fi
 
+  echo "Running with the following options:"
+  echo Automated: $AUTOMATE
+  echo Threads: $NUM_THREADS
+  echo Targets: $TARGET
+  echo Download Weights: $DOWNLOAD_WEIGHTS
+  echo
+fi
 
 if [ -d $LLVM_SRC ]; then
     echo Found $LLVM_SRC, not dowloading it again!
@@ -113,9 +215,16 @@ cd $CURRENT_DIR/llvm_patches
 
 echo Patches applied.
 
-if [ ! $AUTOMATE == "y" ]; then
+if ! $AUTOMATE ; then
   echo
-  echo HPVM not installed. Exiting. 
+  echo "HPVM not installed."
+  echo "To complete installation, follow these instructions:"
+  echo "  - Create and navigate to a folder \"./build\" "
+  echo "  - Run \"cmake ../llvm [options]\". Find potential options in README.md."
+  echo "  - Run \"make -j<number of threads>\" and then \"make install\""
+  echo "For more details refer to README.md."
+  echo 
+  echo "Exiting."
   exit  
 fi
 
@@ -136,15 +245,18 @@ if [ ! -d $INSTALL_DIR ]; then
 fi
 
 cd $BUILD_DIR
-echo cmake ../$LLVM_SRC -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DLLVM_TARGETS_TO_BUILD="X86"  -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR
-cmake ../$LLVM_SRC -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DLLVM_TARGETS_TO_BUILD="X86"  -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR
+echo cmake ../$LLVM_SRC -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DLLVM_TARGETS_TO_BUILD=$TARGET  -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR
+cmake ../$LLVM_SRC -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DLLVM_TARGETS_TO_BUILD=$TARGET  -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR
 
 echo make -j$NUM_THREADS
 make -j$NUM_THREADS
 #make install
 
-read_yn "Download weights necessary to run DNN benchmarks?" DOWNLOAD_WEIGHTS
 if [ $DOWNLOAD_WEIGHTS == "y" ]; then
+  echo
+  echo "Downloading weights for DNN benchmarks..."
+  echo
+
   # First get hands on gdown -- google drive downloader
   wget https://raw.githubusercontent.com/circulosmeos/gdown.pl/master/gdown.pl -O gdown.pl
   chmod +x ./gdown.pl
diff --git a/hpvm/set_paths.sh b/hpvm/set_paths.sh
new file mode 100644
index 0000000000000000000000000000000000000000..42d1be52949ebf780d9fd7836d0429aa970472a5
--- /dev/null
+++ b/hpvm/set_paths.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+# These paths can be modified by the HPVM user
+CUDA_TOOLKIT_PATH=/software/cuda-9.1/
+CUDA_INCLUDE_PATH=$CUDA_TOOLKIT_PATH/include
+CUDA_LIB_PATH=$CUDA_TOOLKIT_PATH/lib64/
+
+echo "Setting environment paths..."
+
+# Setting CUDA paths here
+export CUDA_BIN_PATH=$CUDA_TOOLKIT_PATH
+export CUDA_INCLUDE_PATH=$CUDA_INCLUDE_PATH
+export CUDNN_PATH=$CUDA_LIB_PATH
+export LIBRARY_PATH=$CUDA_LIB_PATH:$LIBRARY_PATH
+export LD_LIBRARY_PATH=$CUDA_LIB_PATH:$LD_LIBRARY_PATH
+
+echo "Finished setting environment paths!"
diff --git a/hpvm/test/dnn_benchmarks/CMakeLists.txt b/hpvm/test/dnn_benchmarks/CMakeLists.txt
index 536a85f1a05ddd460975416de44a35e598974766..887b2d1e6c3003cf886a907bcaf51c830dd0e423 100644
--- a/hpvm/test/dnn_benchmarks/CMakeLists.txt
+++ b/hpvm/test/dnn_benchmarks/CMakeLists.txt
@@ -48,63 +48,81 @@ set(HPVM_RT_PATH ${PROJECT_BINARY_DIR}/tools/hpvm/projects/hpvm-rt/hpvm-rt.ll)
 # Compile flags (clang++)
 set(CLANG_FLAGS -fno-exceptions -std=c++11 -O3)
 
-# Passes flags
+# All compilation uses HPVM_DEFAULT_PASSES.
 set(
-  HPVM_OPT_PASSES
-  -load LLVMBuildDFG.so
-  -load LLVMInPlaceDFGAnalysis.so
-  -load LLVMDFG2LLVM_CUDNN.so
-  -load LLVMDFG2LLVM_CPU.so
-  -load LLVMClearDFG.so
-  -inplace -dfg2llvm-cudnn -dfg2llvm-cpu -clearDFG
+  HPVM_DEFAULT_PASSES
+  LLVMBuildDFG
+  LLVMInPlaceDFGAnalysis
+  LLVMDFG2LLVM_CPU
+  LLVMFuseHPVMTensorNodes
+  LLVMClearDFG
+  LLVMGenHPVM
 )
 
-# Manually specify dependencies because we're not using cmake "normally"
-list(
-  APPEND DEPEND
-  clang opt llvm-link  # LLVM binaries
-  hpvm-rt.ll  # HPVM runtime
-  LLVMGenHPVM LLVMBuildDFG LLVMInPlaceDFGAnalysis LLVMDFG2LLVM_CUDNN LLVMDFG2LLVM_CPU LLVMClearDFG # Passes
-)
+set(WORK_DIR ${CMAKE_CURRENT_BINARY_DIR})
+set(test_compile_targets "")
+function(compile_single_benchmark target src_file extra_passes extra_dfg_flags)
+  foreach(pass ${HPVM_DEFAULT_PASSES} ${extra_passes})
+    list(APPEND LOAD_FILE_FLAGS "-load" "${pass}.so")
+  endforeach()
+  set(
+    HPVM_PASSES ${LOAD_FILE_FLAGS}
+    -buildDFG -inplace -hpvm-fuse ${extra_dfg_flags} -dfg2llvm-cpu -clearDFG
+  )
 
-file(GLOB entries ./benchmarks/*)
-set(test_targets "")
-foreach(entry ${entries})
-  if(IS_DIRECTORY ${entry})
-    file(GLOB src_files ${entry}/*.cpp)
-    foreach(src_file ${src_files})
-      get_filename_component(target "${src_file}" NAME_WE)
-      set(target "test_${target}")
-      list(APPEND test_targets ${target})
+  add_custom_command(
+    OUTPUT "${WORK_DIR}/${target}.ll" DEPENDS ${src_file} clang
+    COMMAND ${CMAKE_CXX_COMPILER} ${INCLUDE_COMPILER_STRINGS} ${CLANG_FLAGS} -emit-llvm -S ${src_file}
+      -o ${WORK_DIR}/${target}.ll
+  )
+  add_custom_command(
+    OUTPUT "${WORK_DIR}/${target}.hpvm.ll"
+    DEPENDS "${WORK_DIR}/${target}.ll" opt LLVMGenHPVM
+    COMMAND ${LLVM_OPT} -load LLVMGenHPVM.so -genhpvm -globaldce -S ${WORK_DIR}/${target}.ll
+      -o ${WORK_DIR}/${target}.hpvm.ll
+  )
+  add_custom_command(
+    OUTPUT "${WORK_DIR}/${target}.llvm.ll"
+    DEPENDS "${WORK_DIR}/${target}.hpvm.ll" opt ${HPVM_DEFAULT_PASSES} ${extra_passes}
+    COMMAND ${LLVM_OPT} ${HPVM_PASSES} -S ${WORK_DIR}/${target}.hpvm.ll
+      -o ${WORK_DIR}/${target}.llvm.ll
+  )
+  add_custom_command(
+    OUTPUT "${WORK_DIR}/${target}.linked.bc"
+    DEPENDS "${WORK_DIR}/${target}.llvm.ll" hpvm-rt.ll llvm-link
+    COMMAND ${LLVM_LINK} ${WORK_DIR}/${target}.llvm.ll ${HPVM_RT_PATH}
+      -o ${WORK_DIR}/${target}.linked.bc
+  )
+  add_custom_command(
+    OUTPUT "${WORK_DIR}/${target}"
+    DEPENDS "${WORK_DIR}/${target}.linked.bc" tensor_runtime gpu_profiler promise_profiler
+    COMMAND ${CMAKE_CXX_COMPILER}
+      ${WORK_DIR}/${target}.linked.bc
+      $<TARGET_FILE:tensor_runtime> $<TARGET_FILE:gpu_profiler> $<TARGET_FILE:promise_profiler>
+      -o ${WORK_DIR}/${target} ${LINKER_FLAGS}
+  )
+  add_custom_target(${target} DEPENDS "${WORK_DIR}/${target}")
 
-      set(WORK_DIR ${CMAKE_CURRENT_BINARY_DIR})
-      add_custom_command(
-        OUTPUT "${target}.ll" DEPENDS ${src_file}
-        COMMAND ${CMAKE_CXX_COMPILER} ${INCLUDE_COMPILER_STRINGS} ${CLANG_FLAGS} -emit-llvm -S ${src_file}
-          -o ${WORK_DIR}/${target}.ll
-      )
-      add_custom_command(
-        OUTPUT
-          "${WORK_DIR}/${target}.hpvm.ll"
-          "${WORK_DIR}/${target}_cudnn.bc"
-          "${WORK_DIR}/${target}_cudnn_linked.bc"
-          "${WORK_DIR}/${target}_cudnn_linked"
-        DEPENDS "${target}.ll"
-        COMMAND ${LLVM_OPT} -load LLVMGenHPVM.so -genhpvm -globaldce -S ${WORK_DIR}/${target}.ll
-          -o ${WORK_DIR}/${target}.hpvm.ll
-        COMMAND ${LLVM_OPT} ${HPVM_OPT_PASSES} ${WORK_DIR}/${target}.hpvm.ll
-          -o ${WORK_DIR}/${target}_cudnn.bc
-        COMMAND ${LLVM_LINK} ${WORK_DIR}/${target}_cudnn.bc ${HPVM_RT_PATH}
-          -o ${WORK_DIR}/${target}_cudnn_linked.bc
-        COMMAND ${CMAKE_CXX_COMPILER}
-          ${WORK_DIR}/${target}_cudnn_linked.bc
-          $<TARGET_FILE:tensor_runtime> $<TARGET_FILE:gpu_profiler> $<TARGET_FILE:promise_profiler>
-          -o ${WORK_DIR}/${target} ${LINKER_FLAGS}
-      )
-      add_custom_target(${target} DEPENDS "${WORK_DIR}/${target}_cudnn_linked")
-      add_dependencies(${target} ${DEPEND})
-    endforeach()
-  endif()
-endforeach(entry)
-message(STATUS "List of test dnn benchmarks: ${test_targets}")
+  set(test_compile_targets ${test_compile_targets} ${target} PARENT_SCOPE)
+endfunction(compile_single_benchmark)
 
+file(GLOB entries ./benchmarks/*)
+foreach(dir ${entries})
+  get_filename_component(dirname "${dir}" NAME)
+  compile_single_benchmark(
+    "test_${dirname}" ${dir}/${dirname}.cpp LLVMDFG2LLVM_CUDNN -dfg2llvm-cudnn
+  )
+  set(
+    loop_extra_flags
+    -dfg2llvm-wrapperapi
+      -quantization-levels-filename=${dir}/data/quant_ranges_rt.txt
+      -configuration-inputs-filename=${dir}/data/tuner_confs.txt
+  )
+  compile_single_benchmark(
+    "test_${dirname}_loop" ${dir}/${dirname}_loop.cpp
+    LLVMDFG2LLVM_WrapperAPI "${loop_extra_flags}"
+  )
+endforeach(dir)
+message(STATUS "List of test dnn benchmarks: ${test_compile_targets}")
+add_custom_target(dnn_benchmarks DEPENDS ${test_compile_targets})
+message(STATUS "Target name for compiling all dnn benchmarks: dnn_benchmarks")
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet.cpp
index 82d68baa6c436002c0a933b967bdb0dbf552c3d3..4dcd57c8164c8bd73280d6224c44bb8b9ec9d6f0 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet.cpp
@@ -192,7 +192,7 @@ void root(void *input, size_t input_bytes, void *conv2d_1_w,
 
   __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, conv2d_5_w,
-                     conv2d_5_b, dense_1_w, dense_1_b, 0);
+                     conv2d_5_b, dense_1_w, dense_1_b, 1, input);
 
   void *var_0 = __hpvm__createNodeND(0, var_0_node);
 
@@ -366,10 +366,11 @@ typedef struct __attribute__((__packed__)) {
 int main() {
 
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/alexnet_cifar10/";
+
   std::string input_path = dir_prefix + std::string("input.bin");
   void *input = readTrainedWeights(input_path.c_str(), 0, 5000, 3, 32, 32);
   std::string labels_path = dir_prefix + std::string("labels.bin");
-  uint8_t *labels = readLabels(labels_path.c_str(), 5000);
+  uint32_t *labels = readLabels3(labels_path.c_str(), 5000);
   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, 64, 3, 11, 11);
@@ -435,15 +436,14 @@ int main() {
   args->dense_1_w_bytes = 0;
   args->dense_1_b = dense_1_b;
   args->dense_1_b_bytes = 0;
-
   void *dfg = __hpvm__launch(0, root, (void *)args);
 
   __hpvm__wait(dfg);
+  void *result = static_cast<RootIn *>(args)->r.tensor;
 
-  void *result = static_cast<RootIn *>(args)->input;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
-  computeAccuracy2(labels, 5000, result);
+  computeAccuracy3(labels, result);
   return 0;
 }
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet_loop.cpp
index e60efe728da794b6ba73fc02dbb92b8277d4de7e..86b3e7eb93bb6040af97007741853ef6474ddb3d 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/alexnet_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 5, 5, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 1, 1);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -123,7 +123,7 @@ void var_13_node(void *t1, size_t bytes_t1) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -131,7 +131,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -139,7 +139,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_16_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -147,7 +147,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -155,7 +155,7 @@ void var_17_node(void *t1, size_t bytes_t1) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -163,7 +163,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -363,11 +363,12 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/alexnet_cifar10/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
   // void* input = readTrainedWeights(input_path.c_str(), 0,5000,3,32,32);
-  std::string labels_path = dir_prefix + std::string("labels32.bin");
+  std::string labels_path = dir_prefix + std::string("labels.bin");
   uint8_t *labels = readLabels(labels_path.c_str(), 5000);
   std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
   void *conv2d_1_w =
@@ -461,7 +462,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8e45529d84b54fc13f19e39f2da94538d54349aa
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/quant_ranges_rt.txt
@@ -0,0 +1,7 @@
+1 -1.88164262419 2.09340954985 -0.33087718 0.3323643 -0.7782218 0.6020472 -0.978641152382 0.998945295811 
+2 -0.978641152382 0.998945295811 -0.2095158 0.33543423 -0.45020863 0.30596754 -0.999703943729 0.999930202961 
+3 -0.999703943729 0.999930202961 -0.1715614 0.17037082 -0.6519161 0.5939945 -0.999933600426 0.999940037727 
+4 -0.999933600426 0.999940037727 -0.15575546 0.14456555 -0.55873865 0.4704539 -0.99999910593 0.999999344349 
+5 -0.99999910593 0.999999344349 -0.16108225 0.16864482 -0.22135437 0.10401678 -0.999434411526 0.999634206295 
+6 -0.999434411526 0.999634206295 -0.18183032 0.19018902 -0.07189204 0.106005594 -15.0765653801 19.4225852203 
+7 0 0 0 0 0 0 0 0
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a6d177c90d5a2890afa5387d4c2a50de1cb6c852
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet/data/tuner_confs.txt
@@ -0,0 +1,11 @@
+2000
++++++
+conf1 3.86 0 79.1 0.0
+1 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1
+2 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1
+3 gpu conv fp32 1 add fp32 1 tanh fp32 1 
+4 gpu conv fp32 1 add fp32 1 tanh fp32 1 
+5 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1
+6 gpu mul fp32 1 add fp32 1  
+7 gpu softmax fp32 1 
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2.cpp
index df193d37ebd3fef1a52f4472514c5a1d137a8f6e..bc1f9fa18e6faeed60d171ec90c4dc891136b1ad 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2.cpp
@@ -412,9 +412,10 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/alexnet2_cifar10/";
 
-  std::string labels_path = dir_prefix + std::string("labels32.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);
@@ -511,7 +512,7 @@ int main() {
 
     __hpvm__wait(dfg);
 
-    void *result = static_cast<RootIn *>(args)->input;
+    void *result = static_cast<RootIn *>(args)->r.tensor;
     hpvm_request_tensor(result, 0);
 
     uint32_t *labels = readLabelsBatch3(labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2_loop.cpp
index 9482c5860dfb6688de17228980de71b1ae7844c1..59161a118d6e9baa9196d045a072993c733b3697 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/alexnet2_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -123,7 +123,7 @@ void var_13_node(void *t1, size_t bytes_t1) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -131,7 +131,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -139,7 +139,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_16_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -147,7 +147,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -155,7 +155,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -163,7 +163,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -171,7 +171,7 @@ void var_19_node(void *t1, size_t bytes_t1) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -179,7 +179,7 @@ void var_20_node(void *t1, size_t bytes_t1) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -187,7 +187,7 @@ void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_22_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -412,9 +412,10 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/alexnet2_cifar10/";
 
-  std::string labels_path = dir_prefix + std::string("labels32.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);
@@ -520,7 +521,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..488c5521dce160487ef3f3ee149914047f6274b1
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/quant_ranges_rt.txt
@@ -0,0 +1,8 @@
+1 -1.8816435 2.0934134 -0.5421946 0.3710851 -0.06697306 0.040868897 -0.775027394891 0.779944300652 
+2 -0.775027394891 0.779944300652 -0.42474225 0.31460348 -0.3557253 -0.17281663 -0.808667064309 0.983953297734 
+3 -0.808667064309 0.983953297734 -0.44134507 0.79587924 -0.80424446 0.75330096 -0.995678424835 0.998566448689 
+4 -0.995678424835 0.998566448689 -0.2883836 0.31025785 -0.6353164 0.29015934 -0.993219196796 0.992379009724 
+5 -0.993219196796 0.992379009724 -0.2792431 0.37689754 -1.1379756 1.2391574 -0.999901354313 0.999910891056 
+6 -0.999901354313 0.999910891056 -0.27078503 0.27942517 -0.503003 0.12762362 -0.991036117375 0.971404970288 
+7 -0.991036117375 0.971404970288 -0.24273404 0.5845544 -0.53745 0.558251 -119.27973732 -25.2262819576
+8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d6f975869964e8bb666262923172eac42a43151
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet2/data/tuner_confs.txt
@@ -0,0 +1,12 @@
+2000
++++++
+conf1 2.64294896823 0 84.24999995 -0.05999995000000524
+1 gpu conv fp32 1 add fp32 1 tanh fp32 1 
+2 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1 
+3 gpu conv fp32 1 add fp32 1 tanh fp32 1 
+4 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1 
+5 gpu conv fp32 1 add fp32 1 tanh fp32 1 
+6 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1
+7 gpu mul fp32 1 add fp32 1 
+8 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
index 4c76cc7273f0d63718e324f17b22bbbd4f59b665..466e311577d1e1d46d2e0c6a2a624cc21900be4f 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
@@ -11,219 +11,219 @@
 #include <config.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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 4, 4);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 4, 4);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)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) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 1, 1);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_7_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_10_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)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) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_17_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_19_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_20_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_22_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_23_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_softmax(t1);
-  __visc__return(2, r, (size_t)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,
@@ -239,181 +239,181 @@ void root(void *input, size_t input_bytes, void *conv2d_1_w,
           void *dense_3_w, size_t dense_3_w_bytes, void *dense_3_b,
           size_t dense_3_b_bytes) {
 
-  __visc__hint(visc::CPU_TARGET);
-  __visc__attributes(17, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b,
+  __hpvm__hint(hpvm::CPU_TARGET);
+  __hpvm__attributes(17, 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, conv2d_5_w,
                      conv2d_5_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b,
                      dense_3_w, dense_3_b, 0);
 
-  void *var_0 = __visc__createNodeND(0, var_0_node);
+  void *var_0 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_1_node);
+  void *var_1 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_2_node);
+  void *var_2 = __hpvm__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);
+  __hpvm__edge(var_1, var_2, 1, 0, 0, 0);
+  __hpvm__edge(var_1, var_2, 1, 1, 1, 0);
 
-  void *var_3 = __visc__createNodeND(0, var_3_node);
+  void *var_3 = __hpvm__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);
+  __hpvm__edge(var_2, var_3, 1, 0, 0, 0);
+  __hpvm__edge(var_2, var_3, 1, 1, 1, 0);
 
-  void *var_4 = __visc__createNodeND(0, var_4_node);
+  void *var_4 = __hpvm__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);
+  __hpvm__edge(var_3, var_4, 1, 0, 0, 0);
+  __hpvm__edge(var_3, var_4, 1, 1, 1, 0);
+  __hpvm__bindIn(var_4, 6, 2, 0);
+  __hpvm__bindIn(var_4, 7, 3, 0);
 
-  void *var_5 = __visc__createNodeND(0, var_5_node);
+  void *var_5 = __hpvm__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);
+  __hpvm__edge(var_4, var_5, 1, 0, 0, 0);
+  __hpvm__edge(var_4, var_5, 1, 1, 1, 0);
+  __hpvm__bindIn(var_5, 8, 2, 0);
+  __hpvm__bindIn(var_5, 9, 3, 0);
 
-  void *var_6 = __visc__createNodeND(0, var_6_node);
+  void *var_6 = __hpvm__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);
+  __hpvm__edge(var_5, var_6, 1, 0, 0, 0);
+  __hpvm__edge(var_5, var_6, 1, 1, 1, 0);
 
-  void *var_7 = __visc__createNodeND(0, var_7_node);
+  void *var_7 = __hpvm__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);
+  __hpvm__edge(var_6, var_7, 1, 0, 0, 0);
+  __hpvm__edge(var_6, var_7, 1, 1, 1, 0);
 
-  void *var_8 = __visc__createNodeND(0, var_8_node);
+  void *var_8 = __hpvm__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, 10, 2, 0);
-  __visc__bindIn(var_8, 11, 3, 0);
+  __hpvm__edge(var_7, var_8, 1, 0, 0, 0);
+  __hpvm__edge(var_7, var_8, 1, 1, 1, 0);
+  __hpvm__bindIn(var_8, 10, 2, 0);
+  __hpvm__bindIn(var_8, 11, 3, 0);
 
-  void *var_9 = __visc__createNodeND(0, var_9_node);
+  void *var_9 = __hpvm__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);
-  __visc__bindIn(var_9, 12, 2, 0);
-  __visc__bindIn(var_9, 13, 3, 0);
+  __hpvm__edge(var_8, var_9, 1, 0, 0, 0);
+  __hpvm__edge(var_8, var_9, 1, 1, 1, 0);
+  __hpvm__bindIn(var_9, 12, 2, 0);
+  __hpvm__bindIn(var_9, 13, 3, 0);
 
-  void *var_10 = __visc__createNodeND(0, var_10_node);
+  void *var_10 = __hpvm__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);
+  __hpvm__edge(var_9, var_10, 1, 0, 0, 0);
+  __hpvm__edge(var_9, var_10, 1, 1, 1, 0);
 
-  void *var_11 = __visc__createNodeND(0, var_11_node);
+  void *var_11 = __hpvm__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, 14, 2, 0);
-  __visc__bindIn(var_11, 15, 3, 0);
+  __hpvm__edge(var_10, var_11, 1, 0, 0, 0);
+  __hpvm__edge(var_10, var_11, 1, 1, 1, 0);
+  __hpvm__bindIn(var_11, 14, 2, 0);
+  __hpvm__bindIn(var_11, 15, 3, 0);
 
-  void *var_12 = __visc__createNodeND(0, var_12_node);
+  void *var_12 = __hpvm__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);
-  __visc__bindIn(var_12, 16, 2, 0);
-  __visc__bindIn(var_12, 17, 3, 0);
+  __hpvm__edge(var_11, var_12, 1, 0, 0, 0);
+  __hpvm__edge(var_11, var_12, 1, 1, 1, 0);
+  __hpvm__bindIn(var_12, 16, 2, 0);
+  __hpvm__bindIn(var_12, 17, 3, 0);
 
-  void *var_13 = __visc__createNodeND(0, var_13_node);
+  void *var_13 = __hpvm__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);
+  __hpvm__edge(var_12, var_13, 1, 0, 0, 0);
+  __hpvm__edge(var_12, var_13, 1, 1, 1, 0);
 
-  void *var_14 = __visc__createNodeND(0, var_14_node);
+  void *var_14 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_15_node);
+  void *var_15 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_16_node);
+  void *var_16 = __hpvm__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);
+  __hpvm__edge(var_15, var_16, 1, 0, 0, 0);
+  __hpvm__edge(var_15, var_16, 1, 1, 1, 0);
 
-  void *var_17 = __visc__createNodeND(0, var_17_node);
+  void *var_17 = __hpvm__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);
+  __hpvm__edge(var_16, var_17, 1, 0, 0, 0);
+  __hpvm__edge(var_16, var_17, 1, 1, 1, 0);
 
-  void *var_18 = __visc__createNodeND(0, var_18_node);
+  void *var_18 = __hpvm__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, 22, 2, 0);
-  __visc__bindIn(var_18, 23, 3, 0);
+  __hpvm__edge(var_17, var_18, 1, 0, 0, 0);
+  __hpvm__edge(var_17, var_18, 1, 1, 1, 0);
+  __hpvm__bindIn(var_18, 22, 2, 0);
+  __hpvm__bindIn(var_18, 23, 3, 0);
 
-  void *var_19 = __visc__createNodeND(0, var_19_node);
+  void *var_19 = __hpvm__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__bindIn(var_19, 24, 2, 0);
-  __visc__bindIn(var_19, 25, 3, 0);
+  __hpvm__edge(var_18, var_19, 1, 0, 0, 0);
+  __hpvm__edge(var_18, var_19, 1, 1, 1, 0);
+  __hpvm__bindIn(var_19, 24, 2, 0);
+  __hpvm__bindIn(var_19, 25, 3, 0);
 
-  void *var_20 = __visc__createNodeND(0, var_20_node);
+  void *var_20 = __hpvm__createNodeND(0, var_20_node);
 
-  __visc__edge(var_19, var_20, 1, 0, 0, 0);
-  __visc__edge(var_19, var_20, 1, 1, 1, 0);
+  __hpvm__edge(var_19, var_20, 1, 0, 0, 0);
+  __hpvm__edge(var_19, var_20, 1, 1, 1, 0);
 
-  void *var_21 = __visc__createNodeND(0, var_21_node);
+  void *var_21 = __hpvm__createNodeND(0, var_21_node);
 
-  __visc__edge(var_20, var_21, 1, 0, 0, 0);
-  __visc__edge(var_20, var_21, 1, 1, 1, 0);
-  __visc__bindIn(var_21, 26, 2, 0);
-  __visc__bindIn(var_21, 27, 3, 0);
+  __hpvm__edge(var_20, var_21, 1, 0, 0, 0);
+  __hpvm__edge(var_20, var_21, 1, 1, 1, 0);
+  __hpvm__bindIn(var_21, 26, 2, 0);
+  __hpvm__bindIn(var_21, 27, 3, 0);
 
-  void *var_22 = __visc__createNodeND(0, var_22_node);
+  void *var_22 = __hpvm__createNodeND(0, var_22_node);
 
-  __visc__edge(var_21, var_22, 1, 0, 0, 0);
-  __visc__edge(var_21, var_22, 1, 1, 1, 0);
-  __visc__bindIn(var_22, 28, 2, 0);
-  __visc__bindIn(var_22, 29, 3, 0);
+  __hpvm__edge(var_21, var_22, 1, 0, 0, 0);
+  __hpvm__edge(var_21, var_22, 1, 1, 1, 0);
+  __hpvm__bindIn(var_22, 28, 2, 0);
+  __hpvm__bindIn(var_22, 29, 3, 0);
 
-  void *var_23 = __visc__createNodeND(0, var_23_node);
+  void *var_23 = __hpvm__createNodeND(0, var_23_node);
 
-  __visc__edge(var_22, var_23, 1, 0, 0, 0);
-  __visc__edge(var_22, var_23, 1, 1, 1, 0);
+  __hpvm__edge(var_22, var_23, 1, 0, 0, 0);
+  __hpvm__edge(var_22, var_23, 1, 1, 1, 0);
 
-  void *var_24 = __visc__createNodeND(0, var_24_node);
+  void *var_24 = __hpvm__createNodeND(0, var_24_node);
 
-  __visc__edge(var_23, var_24, 1, 0, 0, 0);
-  __visc__edge(var_23, var_24, 1, 1, 1, 0);
-  __visc__bindIn(var_24, 30, 2, 0);
-  __visc__bindIn(var_24, 31, 3, 0);
+  __hpvm__edge(var_23, var_24, 1, 0, 0, 0);
+  __hpvm__edge(var_23, var_24, 1, 1, 1, 0);
+  __hpvm__bindIn(var_24, 30, 2, 0);
+  __hpvm__bindIn(var_24, 31, 3, 0);
 
-  void *var_25 = __visc__createNodeND(0, var_25_node);
+  void *var_25 = __hpvm__createNodeND(0, var_25_node);
 
-  __visc__edge(var_24, var_25, 1, 0, 0, 0);
-  __visc__edge(var_24, var_25, 1, 1, 1, 0);
-  __visc__bindIn(var_25, 32, 2, 0);
-  __visc__bindIn(var_25, 33, 3, 0);
+  __hpvm__edge(var_24, var_25, 1, 0, 0, 0);
+  __hpvm__edge(var_24, var_25, 1, 1, 1, 0);
+  __hpvm__bindIn(var_25, 32, 2, 0);
+  __hpvm__bindIn(var_25, 33, 3, 0);
 
-  void *var_26 = __visc__createNodeND(0, var_26_node);
+  void *var_26 = __hpvm__createNodeND(0, var_26_node);
 
-  __visc__edge(var_25, var_26, 1, 0, 0, 0);
-  __visc__edge(var_25, var_26, 1, 1, 1, 0);
+  __hpvm__edge(var_25, var_26, 1, 0, 0, 0);
+  __hpvm__edge(var_25, var_26, 1, 1, 1, 0);
 
-  __visc__bindOut(var_26, 0, 0, 0);
-  __visc__bindOut(var_26, 1, 1, 0);
+  __hpvm__bindOut(var_26, 0, 0, 0);
+  __hpvm__bindOut(var_26, 1, 1, 0);
 }
 
 struct ret_t {
@@ -463,9 +463,9 @@ typedef struct __attribute__((__packed__)) {
 int main() {
 
   std::string dir_prefix =
-      std::string("/shared/hsharif3/alexnet_imagenet_tune/");
-  std::string input_path = dir_prefix + std::string("test_input.bin");
-  std::string labels_path = dir_prefix + std::string("test_labels.bin");
+      std::string(MODEL_PARAMS_DIR) + "/alexnet_imagenet/";
+  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, 64, 3, 11, 11);
@@ -519,7 +519,7 @@ int main() {
 
   uint32_t *labels = readLabels3(labels_path.c_str(), 1000);
 
-  __visc__init();
+  __hpvm__init();
   RootIn *args = static_cast<RootIn *>(malloc(sizeof(RootIn)));
 
   args->input = input;
@@ -557,14 +557,14 @@ int main() {
   args->dense_3_b = dense_3_b;
   args->dense_3_b_bytes = 0;
 
-  void *dfg = __visc__launch(0, root, (void *)args);
+  void *dfg = __hpvm__launch(0, root, (void *)args);
 
-  __visc__wait(dfg);
+  __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
-  __visc__cleanup();
+  __hpvm__cleanup();
   computeAccuracy3(labels, result);
   return 0;
 }
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet_loop.cpp
index abed45d5ff385e7117523e7d4e6e1b7a45b05018..340e0aa1194ac57e96eadd1669a97fa25fdd0c44 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/alexnet_imagenet_loop.cpp
@@ -11,219 +11,219 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 4, 4);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 4, 4);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)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) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_3_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 2, 2, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 1, 1);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_7_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_10_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)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) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_17_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_pool_max(t1, 3, 3, 0, 0, 2, 2);
+  __hpvm__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::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_19_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_20_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_22_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_23_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_relu(t1);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_relu(t1);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_mul(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_mul(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __visc__hint(visc::PROMISE_TARGET);
-  __visc__attributes(2, t1, t2, 0);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
+  __hpvm__attributes(2, t1, t2, 0);
 
-  void *r = __visc__tensor_add(t1, t2);
-  __visc__return(2, r, (size_t)0);
+  void *r = __hpvm__tensor_add(t1, t2);
+  __hpvm__return(2, r, (size_t)0);
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __visc__hint(visc::CUDNN_TARGET);
-  __visc__attributes(1, t1, 0);
+  __hpvm__hint(hpvm::CUDNN_TARGET);
+  __hpvm__attributes(1, t1, 0);
 
-  void *r = __visc__tensor_softmax(t1);
-  __visc__return(2, r, (size_t)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,
@@ -239,181 +239,181 @@ void root(void *input, size_t input_bytes, void *conv2d_1_w,
           void *dense_3_w, size_t dense_3_w_bytes, void *dense_3_b,
           size_t dense_3_b_bytes) {
 
-  __visc__hint(visc::CPU_TARGET);
-  __visc__attributes(17, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b,
+  __hpvm__hint(hpvm::CPU_TARGET);
+  __hpvm__attributes(17, 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, conv2d_5_w,
                      conv2d_5_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b,
                      dense_3_w, dense_3_b, 0);
 
-  void *var_0 = __visc__createNodeND(0, var_0_node);
+  void *var_0 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_1_node);
+  void *var_1 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_2_node);
+  void *var_2 = __hpvm__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);
+  __hpvm__edge(var_1, var_2, 1, 0, 0, 0);
+  __hpvm__edge(var_1, var_2, 1, 1, 1, 0);
 
-  void *var_3 = __visc__createNodeND(0, var_3_node);
+  void *var_3 = __hpvm__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);
+  __hpvm__edge(var_2, var_3, 1, 0, 0, 0);
+  __hpvm__edge(var_2, var_3, 1, 1, 1, 0);
 
-  void *var_4 = __visc__createNodeND(0, var_4_node);
+  void *var_4 = __hpvm__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);
+  __hpvm__edge(var_3, var_4, 1, 0, 0, 0);
+  __hpvm__edge(var_3, var_4, 1, 1, 1, 0);
+  __hpvm__bindIn(var_4, 6, 2, 0);
+  __hpvm__bindIn(var_4, 7, 3, 0);
 
-  void *var_5 = __visc__createNodeND(0, var_5_node);
+  void *var_5 = __hpvm__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);
+  __hpvm__edge(var_4, var_5, 1, 0, 0, 0);
+  __hpvm__edge(var_4, var_5, 1, 1, 1, 0);
+  __hpvm__bindIn(var_5, 8, 2, 0);
+  __hpvm__bindIn(var_5, 9, 3, 0);
 
-  void *var_6 = __visc__createNodeND(0, var_6_node);
+  void *var_6 = __hpvm__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);
+  __hpvm__edge(var_5, var_6, 1, 0, 0, 0);
+  __hpvm__edge(var_5, var_6, 1, 1, 1, 0);
 
-  void *var_7 = __visc__createNodeND(0, var_7_node);
+  void *var_7 = __hpvm__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);
+  __hpvm__edge(var_6, var_7, 1, 0, 0, 0);
+  __hpvm__edge(var_6, var_7, 1, 1, 1, 0);
 
-  void *var_8 = __visc__createNodeND(0, var_8_node);
+  void *var_8 = __hpvm__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, 10, 2, 0);
-  __visc__bindIn(var_8, 11, 3, 0);
+  __hpvm__edge(var_7, var_8, 1, 0, 0, 0);
+  __hpvm__edge(var_7, var_8, 1, 1, 1, 0);
+  __hpvm__bindIn(var_8, 10, 2, 0);
+  __hpvm__bindIn(var_8, 11, 3, 0);
 
-  void *var_9 = __visc__createNodeND(0, var_9_node);
+  void *var_9 = __hpvm__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);
-  __visc__bindIn(var_9, 12, 2, 0);
-  __visc__bindIn(var_9, 13, 3, 0);
+  __hpvm__edge(var_8, var_9, 1, 0, 0, 0);
+  __hpvm__edge(var_8, var_9, 1, 1, 1, 0);
+  __hpvm__bindIn(var_9, 12, 2, 0);
+  __hpvm__bindIn(var_9, 13, 3, 0);
 
-  void *var_10 = __visc__createNodeND(0, var_10_node);
+  void *var_10 = __hpvm__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);
+  __hpvm__edge(var_9, var_10, 1, 0, 0, 0);
+  __hpvm__edge(var_9, var_10, 1, 1, 1, 0);
 
-  void *var_11 = __visc__createNodeND(0, var_11_node);
+  void *var_11 = __hpvm__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, 14, 2, 0);
-  __visc__bindIn(var_11, 15, 3, 0);
+  __hpvm__edge(var_10, var_11, 1, 0, 0, 0);
+  __hpvm__edge(var_10, var_11, 1, 1, 1, 0);
+  __hpvm__bindIn(var_11, 14, 2, 0);
+  __hpvm__bindIn(var_11, 15, 3, 0);
 
-  void *var_12 = __visc__createNodeND(0, var_12_node);
+  void *var_12 = __hpvm__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);
-  __visc__bindIn(var_12, 16, 2, 0);
-  __visc__bindIn(var_12, 17, 3, 0);
+  __hpvm__edge(var_11, var_12, 1, 0, 0, 0);
+  __hpvm__edge(var_11, var_12, 1, 1, 1, 0);
+  __hpvm__bindIn(var_12, 16, 2, 0);
+  __hpvm__bindIn(var_12, 17, 3, 0);
 
-  void *var_13 = __visc__createNodeND(0, var_13_node);
+  void *var_13 = __hpvm__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);
+  __hpvm__edge(var_12, var_13, 1, 0, 0, 0);
+  __hpvm__edge(var_12, var_13, 1, 1, 1, 0);
 
-  void *var_14 = __visc__createNodeND(0, var_14_node);
+  void *var_14 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_15_node);
+  void *var_15 = __hpvm__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);
+  __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 = __visc__createNodeND(0, var_16_node);
+  void *var_16 = __hpvm__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);
+  __hpvm__edge(var_15, var_16, 1, 0, 0, 0);
+  __hpvm__edge(var_15, var_16, 1, 1, 1, 0);
 
-  void *var_17 = __visc__createNodeND(0, var_17_node);
+  void *var_17 = __hpvm__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);
+  __hpvm__edge(var_16, var_17, 1, 0, 0, 0);
+  __hpvm__edge(var_16, var_17, 1, 1, 1, 0);
 
-  void *var_18 = __visc__createNodeND(0, var_18_node);
+  void *var_18 = __hpvm__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, 22, 2, 0);
-  __visc__bindIn(var_18, 23, 3, 0);
+  __hpvm__edge(var_17, var_18, 1, 0, 0, 0);
+  __hpvm__edge(var_17, var_18, 1, 1, 1, 0);
+  __hpvm__bindIn(var_18, 22, 2, 0);
+  __hpvm__bindIn(var_18, 23, 3, 0);
 
-  void *var_19 = __visc__createNodeND(0, var_19_node);
+  void *var_19 = __hpvm__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__bindIn(var_19, 24, 2, 0);
-  __visc__bindIn(var_19, 25, 3, 0);
+  __hpvm__edge(var_18, var_19, 1, 0, 0, 0);
+  __hpvm__edge(var_18, var_19, 1, 1, 1, 0);
+  __hpvm__bindIn(var_19, 24, 2, 0);
+  __hpvm__bindIn(var_19, 25, 3, 0);
 
-  void *var_20 = __visc__createNodeND(0, var_20_node);
+  void *var_20 = __hpvm__createNodeND(0, var_20_node);
 
-  __visc__edge(var_19, var_20, 1, 0, 0, 0);
-  __visc__edge(var_19, var_20, 1, 1, 1, 0);
+  __hpvm__edge(var_19, var_20, 1, 0, 0, 0);
+  __hpvm__edge(var_19, var_20, 1, 1, 1, 0);
 
-  void *var_21 = __visc__createNodeND(0, var_21_node);
+  void *var_21 = __hpvm__createNodeND(0, var_21_node);
 
-  __visc__edge(var_20, var_21, 1, 0, 0, 0);
-  __visc__edge(var_20, var_21, 1, 1, 1, 0);
-  __visc__bindIn(var_21, 26, 2, 0);
-  __visc__bindIn(var_21, 27, 3, 0);
+  __hpvm__edge(var_20, var_21, 1, 0, 0, 0);
+  __hpvm__edge(var_20, var_21, 1, 1, 1, 0);
+  __hpvm__bindIn(var_21, 26, 2, 0);
+  __hpvm__bindIn(var_21, 27, 3, 0);
 
-  void *var_22 = __visc__createNodeND(0, var_22_node);
+  void *var_22 = __hpvm__createNodeND(0, var_22_node);
 
-  __visc__edge(var_21, var_22, 1, 0, 0, 0);
-  __visc__edge(var_21, var_22, 1, 1, 1, 0);
-  __visc__bindIn(var_22, 28, 2, 0);
-  __visc__bindIn(var_22, 29, 3, 0);
+  __hpvm__edge(var_21, var_22, 1, 0, 0, 0);
+  __hpvm__edge(var_21, var_22, 1, 1, 1, 0);
+  __hpvm__bindIn(var_22, 28, 2, 0);
+  __hpvm__bindIn(var_22, 29, 3, 0);
 
-  void *var_23 = __visc__createNodeND(0, var_23_node);
+  void *var_23 = __hpvm__createNodeND(0, var_23_node);
 
-  __visc__edge(var_22, var_23, 1, 0, 0, 0);
-  __visc__edge(var_22, var_23, 1, 1, 1, 0);
+  __hpvm__edge(var_22, var_23, 1, 0, 0, 0);
+  __hpvm__edge(var_22, var_23, 1, 1, 1, 0);
 
-  void *var_24 = __visc__createNodeND(0, var_24_node);
+  void *var_24 = __hpvm__createNodeND(0, var_24_node);
 
-  __visc__edge(var_23, var_24, 1, 0, 0, 0);
-  __visc__edge(var_23, var_24, 1, 1, 1, 0);
-  __visc__bindIn(var_24, 30, 2, 0);
-  __visc__bindIn(var_24, 31, 3, 0);
+  __hpvm__edge(var_23, var_24, 1, 0, 0, 0);
+  __hpvm__edge(var_23, var_24, 1, 1, 1, 0);
+  __hpvm__bindIn(var_24, 30, 2, 0);
+  __hpvm__bindIn(var_24, 31, 3, 0);
 
-  void *var_25 = __visc__createNodeND(0, var_25_node);
+  void *var_25 = __hpvm__createNodeND(0, var_25_node);
 
-  __visc__edge(var_24, var_25, 1, 0, 0, 0);
-  __visc__edge(var_24, var_25, 1, 1, 1, 0);
-  __visc__bindIn(var_25, 32, 2, 0);
-  __visc__bindIn(var_25, 33, 3, 0);
+  __hpvm__edge(var_24, var_25, 1, 0, 0, 0);
+  __hpvm__edge(var_24, var_25, 1, 1, 1, 0);
+  __hpvm__bindIn(var_25, 32, 2, 0);
+  __hpvm__bindIn(var_25, 33, 3, 0);
 
-  void *var_26 = __visc__createNodeND(0, var_26_node);
+  void *var_26 = __hpvm__createNodeND(0, var_26_node);
 
-  __visc__edge(var_25, var_26, 1, 0, 0, 0);
-  __visc__edge(var_25, var_26, 1, 1, 1, 0);
+  __hpvm__edge(var_25, var_26, 1, 0, 0, 0);
+  __hpvm__edge(var_25, var_26, 1, 1, 1, 0);
 
-  __visc__bindOut(var_26, 0, 0, 0);
-  __visc__bindOut(var_26, 1, 1, 0);
+  __hpvm__bindOut(var_26, 0, 0, 0);
+  __hpvm__bindOut(var_26, 1, 1, 0);
 }
 
 struct ret_t {
@@ -463,9 +463,10 @@ typedef struct __attribute__((__packed__)) {
 int main() {
 
   std::string dir_prefix =
-      std::string("/shared/hsharif3/alexnet_imagenet_tune/");
-  std::string input_path = dir_prefix + std::string("test_input.bin");
-  std::string labels_path = dir_prefix + std::string("test_labels.bin");
+      std::string(MODEL_PARAMS_DIR) + "/alexnet_imagenet/";
+
+  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, 64, 3, 11, 11);
@@ -519,7 +520,7 @@ int main() {
 
   // uint32_t* labels = readLabels3(labels_path.c_str(), 1000);
 
-  __visc__init();
+  __hpvm__init();
   RootIn *args = static_cast<RootIn *>(malloc(sizeof(RootIn)));
 
   // args->input = input;
@@ -576,11 +577,11 @@ int main() {
       args->input = input;
       args->input_bytes = 0;
 
-      void *dfg = __visc__launch(0, root, (void *)args);
+      void *dfg = __hpvm__launch(0, root, (void *)args);
 
-      __visc__wait(dfg);
+      __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
@@ -590,7 +591,7 @@ int main() {
   }
 
   stopProfiling();
-  __visc__cleanup();
+  __hpvm__cleanup();
 
   return 0;
 }
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..897937563bac79bdc4592c6a6e7ce46e41e75920
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/quant_ranges_rt.txt
@@ -0,0 +1,10 @@
+1 0.0 255.0 0.5811487324237921 -0.5503702693581581 1.648145 -2.802485 0.0 1572.3096923828125
+2 0.0 1572.3096923828125 0.26272463005783797 -0.2867645202279091 0.501206 -0.47985682 0.0 3183.7813264160477
+3 0.0 3183.7813264160477 0.15785247704386754 -0.16606662392616273 0.5545839 -0.42038992 0.0 1765.4451872558668
+4 0.0 1765.4451872558668 0.11035470351576919 -0.10464580833911895 0.9042998 -1.4275751 0.0 1345.5418548586083
+5 0.0 1345.5418548586083 0.10250756608694818 -0.09240880391001702 2.4040315 -0.45662758 0.0 1227.3563232421875
+6 0.0 1227.3563232421875 0.02963459612801672 -0.030517672039568428 0.09377053 -0.07124679 0.0 1034.5966391601676
+7 0.0 1034.5966391601676 0.039147199764847845 -0.038392101023346184 0.1841282 -0.050027702 0.0 839.0697069702154
+8 0.0 839.0697069702154 0.08549865524470925 -0.05494491942599416 0.15416704 -0.16314922 -608.3993963623047 1082.8444653320819
+9 0 0 0 0 0 0 0 0
+
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..377bc6a5628a5f869ccab9723838622afcbb210c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/alexnet_imagenet/data/tuner_confs.txt
@@ -0,0 +1,13 @@
+750.80768325
++++++
+conf1 1.0 0 79.1 0.0
+1 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1
+2 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1
+3 gpu conv fp32 1 add fp32 1 relu fp32 1 
+4 gpu conv fp32 1 add fp32 1 relu fp32 1 
+5 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1
+6 gpu mul fp32 1 add fp32 1 relu fp32 1
+7 gpu mul fp32 1 add fp32 1 relu fp32 1
+8 gpu mul fp32 1 add fp32 1
+9 gpu softmax fp32 1 
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2a94f5c018eb44a397ea09e6f7ab3681d0c3c0f6
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/quant_ranges_rt.txt
@@ -0,0 +1,4 @@
+1 0 1 -1 1 -1 1 -1 1
+2 -1 1 -1 1 -1 1 -1 1
+3 -1 1 -1 1 -1 1 -1 1
+4 -1 1 -1 1 -1 1 -1 1
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f2a85f352fe024f0fcf7828c259f8549f6461e24
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/data/tuner_confs.txt
@@ -0,0 +1,9 @@
+2000
++++++
+conf1 1 0 99.69 0
+1 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1 
+2 gpu conv fp32 1 add fp32 1 tanh fp32 1 pool_max fp32 1 
+3 gpu mul fp32 1 add fp32 1 tanh fp32 1 
+4 gpu mul fp32 1 add fp32 1 tanh fp32 1 
+5 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist.cpp b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist.cpp
index 29564cfd423494f2d9aed778a20d010deb6fa265..3613e9f1325d73e7515a88f3e198bcd32821224c 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist.cpp
@@ -265,33 +265,34 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/lenet_mnist/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.bin");
+  std::string labels_path = dir_prefix + std::string("labels.bin");
 
-  std::string conv2d_1_w_path = dir_prefix + std::string("conv1.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, 1, 5, 5);
-  std::string conv2d_1_b_path = dir_prefix + std::string("conv1_bias.bin");
+  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("conv2.bin");
+  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("conv2_bias.bin");
+  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 dense_1_w_path = dir_prefix + std::string("fc1.bin");
+  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("fc1_bias.bin");
+  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("fc2.bin");
+  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("fc2_bias.bin");
+  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 *input = readTrainedWeights(input_path.c_str(), 0, 5000, 1, 28, 28);
 
@@ -323,7 +324,7 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist_loop.cpp
index 8a5356581093ac574282463bed311997eae89552..9a8bfbc68fcaad4b369223c53e98121e9934b27d 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/lenet_mnist/lenet_mnist_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 2, 2, 1, 1);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_tanh(t1);
@@ -265,33 +265,34 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/lenet_mnist/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.bin");
+  std::string labels_path = dir_prefix + std::string("labels.bin");
 
-  std::string conv2d_1_w_path = dir_prefix + std::string("conv1.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, 1, 5, 5);
-  std::string conv2d_1_b_path = dir_prefix + std::string("conv1_bias.bin");
+  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("conv2.bin");
+  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("conv2_bias.bin");
+  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 dense_1_w_path = dir_prefix + std::string("fc1.bin");
+  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("fc1_bias.bin");
+  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("fc2.bin");
+  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("fc2_bias.bin");
+  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* input = readTrainedWeights(input_path.c_str(), 0, 5000,1,28,28);
 
@@ -340,7 +341,7 @@ int main() {
 
     __hpvm__wait(dfg);
 
-    void *result = static_cast<RootIn *>(args)->input;
+    void *result = static_cast<RootIn *>(args)->r.tensor;
     hpvm_request_tensor(result, 0);
 
     llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..75211f858c1cc9eb6a186dc7f90c143ea820ef67
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/quant_ranges_rt.txt
@@ -0,0 +1,15 @@
+1 -1.9892114 2.126797 -2.19630692005 1.34758170414  0.0  0.0  -60.892750473 51.9925691605 
+2 0.0 5.71354155397 -0.931772116065 1.07742589378   0.0  0.0 -6.51858950329 6.81084251881 
+3 0.0 4.93213940287 -0.531654466152 0.57537904036   0.0  0.0  -4.48263123512 3.96730119753 
+4 0.0 4.10326339769 -0.362340988219 0.407691390038   0.0  0.0  -4.04261828327 3.8867793293 
+5 0.0 5.38322130251 -0.313120054901 0.293576799393   0.0  0.0  -5.92146921539 4.33867932415 
+6 0.0 4.31673815441 -0.232992478013 0.258029025793   0.0  0.0  -4.20778994751 3.93243697071 
+7 0.0 5.8304081068 -0.202337772191 0.189983081758   0.0  0.0  -6.29828691578 4.84813511753 
+8 0.0 4.44641780996 -0.174427356511 0.176958308667  0.0  0.0   -4.34791088581 3.61443646955 
+9 0.0 4.5180956049 -0.145467961878 0.15256431669   0.0  0.0   -3.02877027559 2.94873657799 
+10 0.0 6.34857563496 -0.130258745223 0.135582433432   0.0  0.0  -4.22931008053 3.53150463724 
+11 0.0 5.22100311041 -0.119001727596 0.125363747835   0.0  0.0  -4.03820378017 4.00400940704 
+12 0.0 5.73249834776 -0.108397216856 0.116256686077    0.0  0.0  -3.31110151148 4.46293323326 
+13 0.0 7.24049821186 -0.0862374496162 0.0885944995135   0.0  0.0  -4.17543139458 6.2043294754 
+14 0.0 7.81395883465 -0.0681302513927 0.0700202777982    0.0  0.0  -10.9205664234 2.64429125786 
+15 0.0 2.86920666504 -0.223010196954 0.14426593782 -0.1654396 0.23336112 -12.2459499588 23.8053251343
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed02ddab0dbef2b21f785226b80f4eee7a1735cf
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/data/tuner_confs.txt
@@ -0,0 +1,175 @@
+1000
++++++
+conf1 1 0 84.8 0
+1 gpu conv fp32 1 
+2 gpu batchnorm fp32 1 
+3 gpu relu fp32 1 
+4 gpu group_conv fp32 1 
+5 gpu batchnorm fp32 1 
+6 gpu relu fp32 1 
+7 gpu conv fp32 1 
+8 gpu batchnorm fp32 1 
+9 gpu relu fp32 1 
+10 gpu group_conv fp32 1 
+11 gpu batchnorm fp32 1 
+12 gpu relu fp32 1 
+13 gpu conv fp32 1 
+14 gpu batchnorm fp32 1 
+15 gpu relu fp32 1 
+16 gpu group_conv fp32 1 
+17 gpu batchnorm fp32 1 
+18 gpu relu fp32 1 
+19 gpu conv fp32 1 
+20 gpu batchnorm fp32 1 
+21 gpu relu fp32 1 
+22 gpu group_conv fp32 1 
+23 gpu batchnorm fp32 1 
+24 gpu relu fp32 1 
+25 gpu conv fp32 1 
+26 gpu batchnorm fp32 1 
+27 gpu relu fp32 1 
+28 gpu group_conv fp32 1 
+29 gpu batchnorm fp32 1 
+30 gpu relu fp32 1 
+31 gpu conv fp32 1 
+32 gpu batchnorm fp32 1 
+33 gpu relu fp32 1 
+34 gpu group_conv fp32 1 
+35 gpu batchnorm fp32 1 
+36 gpu relu fp32 1 
+37 gpu conv fp32 1 
+38 gpu batchnorm fp32 1 
+39 gpu relu fp32 1 
+40 gpu group_conv fp32 1 
+41 gpu batchnorm fp32 1 
+42 gpu relu fp32 1 
+43 gpu conv fp32 1 
+44 gpu batchnorm fp32 1 
+45 gpu relu fp32 1 
+46 gpu group_conv fp32 1 
+47 gpu batchnorm fp32 1 
+48 gpu relu fp32 1 
+49 gpu conv fp32 1 
+50 gpu batchnorm fp32 1 
+51 gpu relu fp32 1 
+52 gpu group_conv fp32 1 
+53 gpu batchnorm fp32 1 
+54 gpu relu fp32 1 
+55 gpu conv fp32 1 
+56 gpu batchnorm fp32 1 
+57 gpu relu fp32 1 
+58 gpu group_conv fp32 1 
+59 gpu batchnorm fp32 1 
+60 gpu relu fp32 1 
+61 gpu conv fp32 1 
+62 gpu batchnorm fp32 1 
+63 gpu relu fp32 1 
+64 gpu group_conv fp32 1 
+65 gpu batchnorm fp32 1 
+66 gpu relu fp32 1 
+67 gpu conv fp32 1 
+68 gpu batchnorm fp32 1 
+69 gpu relu fp32 1 
+70 gpu group_conv fp32 1 
+71 gpu batchnorm fp32 1 
+72 gpu relu fp32 1 
+73 gpu conv fp32 1 
+74 gpu batchnorm fp32 1 
+75 gpu relu fp32 1 
+76 gpu group_conv fp32 1 
+77 gpu batchnorm fp32 1 
+78 gpu relu fp32 1 
+79 gpu conv fp32 1 
+80 gpu batchnorm fp32 1 
+81 gpu relu fp32 1 
+82 gpu pool_mean fp32 1 
+83 gpu mul fp32 1 add fp32 1 
+84 gpu softmax fp32 1
+-----
++++++
+conf2 1.5 0 84.8 0
+1 gpu conv fp16 1 
+2 gpu batchnorm fp16 1 
+3 gpu relu fp16 1 
+4 gpu group_conv fp16 1 
+5 gpu batchnorm fp16 1 
+6 gpu relu fp16 1 
+7 gpu conv fp16 1 
+8 gpu batchnorm fp16 1 
+9 gpu relu fp16 1 
+10 gpu group_conv fp16 1 
+11 gpu batchnorm fp16 1 
+12 gpu relu fp16 1 
+13 gpu conv fp16 1 
+14 gpu batchnorm fp16 1 
+15 gpu relu fp16 1 
+16 gpu group_conv fp16 1 
+17 gpu batchnorm fp16 1 
+18 gpu relu fp16 1 
+19 gpu conv fp16 1 
+20 gpu batchnorm fp16 1 
+21 gpu relu fp16 1 
+22 gpu group_conv fp16 1 
+23 gpu batchnorm fp16 1 
+24 gpu relu fp16 1 
+25 gpu conv fp16 1 
+26 gpu batchnorm fp16 1 
+27 gpu relu fp16 1 
+28 gpu group_conv fp16 1 
+29 gpu batchnorm fp16 1 
+30 gpu relu fp16 1 
+31 gpu conv fp16 1 
+32 gpu batchnorm fp16 1 
+33 gpu relu fp16 1 
+34 gpu group_conv fp16 1 
+35 gpu batchnorm fp16 1 
+36 gpu relu fp16 1 
+37 gpu conv fp16 1 
+38 gpu batchnorm fp16 1 
+39 gpu relu fp16 1 
+40 gpu group_conv fp16 1 
+41 gpu batchnorm fp16 1 
+42 gpu relu fp16 1 
+43 gpu conv fp16 1 
+44 gpu batchnorm fp16 1 
+45 gpu relu fp16 1 
+46 gpu group_conv fp16 1 
+47 gpu batchnorm fp16 1 
+48 gpu relu fp16 1 
+49 gpu conv fp16 1 
+50 gpu batchnorm fp16 1 
+51 gpu relu fp16 1 
+52 gpu group_conv fp16 1 
+53 gpu batchnorm fp16 1 
+54 gpu relu fp16 1 
+55 gpu conv fp16 1 
+56 gpu batchnorm fp16 1 
+57 gpu relu fp16 1 
+58 gpu group_conv fp16 1 
+59 gpu batchnorm fp16 1 
+60 gpu relu fp16 1 
+61 gpu conv fp16 1 
+62 gpu batchnorm fp16 1 
+63 gpu relu fp16 1 
+64 gpu group_conv fp16 1 
+65 gpu batchnorm fp16 1 
+66 gpu relu fp16 1 
+67 gpu conv fp16 1 
+68 gpu batchnorm fp16 1 
+69 gpu relu fp16 1 
+70 gpu group_conv fp16 1 
+71 gpu batchnorm fp16 1 
+72 gpu relu fp16 1 
+73 gpu conv fp16 1 
+74 gpu batchnorm fp16 1 
+75 gpu relu fp16 1 
+76 gpu group_conv fp16 1 
+77 gpu batchnorm fp16 1 
+78 gpu relu fp16 1 
+79 gpu conv fp16 1 
+80 gpu batchnorm fp16 1 
+81 gpu relu fp16 1 
+82 gpu pool_mean fp16 1 
+83 gpu mul fp16 1 add fp16 1 
+84 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet.cpp b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet.cpp
index f090669e4015854634e67b1380e3204e94034a11..b32dccabc2f29b54e8da35551f8d982cd13a378c 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet.cpp
@@ -1966,6 +1966,7 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/mobilenet/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
@@ -2502,7 +2503,7 @@ int main() {
   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);
   void *input = readTrainedWeights(input_path.c_str(), 0, 5000, 3, 32, 32);
-  uint8_t *labels = readLabels(labels_path.c_str(), 5000);
+  uint32_t *labels = readLabels3(labels_path.c_str(), 5000);
 
   __hpvm__init();
   RootIn *args = static_cast<RootIn *>(malloc(sizeof(RootIn)));
@@ -2788,10 +2789,10 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
-  computeAccuracy2(labels, 5000, result);
+  computeAccuracy3(labels, result);
   return 0;
 }
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet_loop.cpp
index 59044b6a1020d64509dd75bb636cce64275da249..047697767d9fa0d7f428a02eeb6b8a9566597137 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/mobilenet/mobilenet_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -63,7 +63,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -115,7 +115,7 @@ void var_11_node(void *t1, size_t bytes_t1) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -167,7 +167,7 @@ void var_17_node(void *t1, size_t bytes_t1) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -219,7 +219,7 @@ void var_23_node(void *t1, size_t bytes_t1) {
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -271,7 +271,7 @@ void var_29_node(void *t1, size_t bytes_t1) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -323,7 +323,7 @@ void var_35_node(void *t1, size_t bytes_t1) {
 }
 
 void var_36_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -375,7 +375,7 @@ void var_41_node(void *t1, size_t bytes_t1) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -427,7 +427,7 @@ void var_47_node(void *t1, size_t bytes_t1) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -479,7 +479,7 @@ void var_53_node(void *t1, size_t bytes_t1) {
 }
 
 void var_54_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -531,7 +531,7 @@ void var_59_node(void *t1, size_t bytes_t1) {
 }
 
 void var_60_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -583,7 +583,7 @@ void var_65_node(void *t1, size_t bytes_t1) {
 }
 
 void var_66_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -635,7 +635,7 @@ void var_71_node(void *t1, size_t bytes_t1) {
 }
 
 void var_72_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -687,7 +687,7 @@ void var_77_node(void *t1, size_t bytes_t1) {
 }
 
 void var_78_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
@@ -721,7 +721,7 @@ void var_81_node(void *t1, size_t bytes_t1) {
 }
 
 void var_82_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -729,7 +729,7 @@ void var_82_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_83_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -1966,9 +1966,6 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
-
-  // std::string dir_prefix =
-  // std::string("../../../../../projects/hpvm-tensor-rt/model_params/mobilenet_quant/");
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/mobilenet/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
@@ -2811,7 +2808,7 @@ int main() {
 
     __hpvm__wait(dfg);
 
-    void *result = static_cast<RootIn *>(args)->input;
+    void *result = static_cast<RootIn *>(args)->r.tensor;
     hpvm_request_tensor(result, 0);
 
     llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a7b14d7348f424556ba5e7bb52b6fdf9bbbd89c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/quant_ranges_rt.txt
@@ -0,0 +1,22 @@
+1 -0.5500815 0.60786617 -1.0248864 1.2929907 -0.36291853 0.2533059 0.0 0.753551840782 
+2 0.0 0.753551840782 -0.69884616 0.71849966 -0.2781147 0.45571187 0.0 1.01057458043 
+3 0.0 1.01057458043 -0.59568167 0.7714691 -0.8602873 0.19743633 -1.84771883726 1.87930787086 
+4 0.0 2.33981014252 -0.41976976 0.43748936 -0.7021962 0.3033103 0.0 1.04317724705 
+5 0.0 1.04317724705 -0.46757826 0.4635873 -0.20662616 0.1778044 -0.829483509064 0.786805033684 
+6 0.0 2.49733686686 -0.64404047 0.45383143 -0.819547 0.38550296 0.0 0.897360802293 
+7 0.0 0.897360802293 -0.41986948 0.33654243 -0.3563013 0.22371122 -0.957150224447 0.54919362247 
+8 0.0 2.37362146616 -0.4805263 0.50655717 -0.296758 0.7742441 0.0 3.01592136621 
+9 0.0 3.01592136621 -0.52083415 0.45517674 -0.20242067 0.8236838 -5.2759475708 5.79733039856 
+10 0.0 2.37362146616 -0.5338656 1.3395424 -0.20242067 0.8236838 -0.738995380998 2.33600783587 
+11 0.0 7.07933432579 -0.34429058 0.43629733 -1.0744808 0.056708273 0.0 1.58645607233 
+12 0.0 1.58645607233 -0.30342352 0.39493486 -0.44630566 0.6492069 -1.49672914267 1.29970229745 
+13 0.0 7.11914063454 -0.38351893 0.45775774 -1.4733055 -0.014426912 0.0 1.52876508832 
+14 0.0 1.52876508832 -0.25695276 0.45372736 -0.5259744 0.26591402 -1.59576894164 1.08074297309 
+15 0.0 6.94405080318 -0.55299705 0.5443531 -0.71790683 1.2730768 0.0 10.3651468277 
+16 0.0 10.3651468277 -0.4203967 0.48641303 -0.90653443 1.3546854 -22.372925148 17.2033731079 
+17 0.0 6.94405080318 -0.4365755 0.84913826 -0.90653443 1.3546851 -3.66810325861 4.87814051151 
+18 0.0 18.8401451111 -0.38657624 0.5228989 -1.2083547 0.76361173 0.0 19.1229192352 
+19 0.0 19.1229192352 -0.40857902 0.575035 -1.8731614 1.0960501 -31.3229312897 14.8234729958 
+20 0.0 23.7382488823 -0.33079496 0.5893278 -1.0234511 1.0016295 0.0 19.5892774963 
+21 0.0 19.5892774963 -0.27897888 0.38280907 -2.2086356 1.0066502 -34.4416886902 20.9890329933 
+22 0.0 10.8541981602 -1.5092047 1.0279838 -0.49379802 0.61032647 -40.9121678543 25.7082381058
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a414afad320525deb15bdd32f35c1a1ac4699be
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/data/tuner_confs.txt
@@ -0,0 +1,91 @@
+2000
++++++
+conf1 1 0 89.59 0
+1 gpu conv fp32 1 add fp32 1 relu fp32 1 
+2 gpu conv fp32 1 add fp32 1 relu fp32 1 
+3 gpu conv fp32 1 add fp32 1 
+4 gpu add fp32 1 
+5 gpu relu fp32 1 
+6 gpu conv fp32 1 add fp32 1 relu fp32 1 
+7 gpu conv fp32 1 add fp32 1 
+8 gpu add fp32 1 
+9 gpu relu fp32 1 
+10 gpu conv fp32 1 add fp32 1 relu fp32 1 
+11 gpu conv fp32 1 add fp32 1 
+12 gpu add fp32 1 
+13 gpu relu fp32 1 
+14 gpu conv fp32 1 add fp32 1 relu fp32 1 
+15 gpu conv fp32 1 add fp32 1 
+16 gpu conv fp32 1 add fp32 1 
+17 gpu add fp32 1 
+18 gpu relu fp32 1 
+19 gpu conv fp32 1 add fp32 1 relu fp32 1 
+20 gpu conv fp32 1 add fp32 1 
+21 gpu add fp32 1 
+22 gpu relu fp32 1 
+23 gpu conv fp32 1 add fp32 1 relu fp32 1 
+24 gpu conv fp32 1 add fp32 1 
+25 gpu add fp32 1 
+26 gpu relu fp32 1 
+27 gpu conv fp32 1 add fp32 1 relu fp32 1 
+28 gpu conv fp32 1 add fp32 1 
+29 gpu conv fp32 1 add fp32 1 
+30 gpu add fp32 1 
+31 gpu relu fp32 1 
+32 gpu conv fp32 1 add fp32 1 relu fp32 1 
+33 gpu conv fp32 1 add fp32 1 
+34 gpu add fp32 1 
+35 gpu relu fp32 1 
+36 gpu conv fp32 1 add fp32 1 relu fp32 1 
+37 gpu conv fp32 1 add fp32 1 
+38 gpu add fp32 1 
+39 gpu relu fp32 1 
+40 gpu pool_mean fp32 1 
+41 gpu mul fp32 1 add fp32 1 
+42 gpu softmax fp32 1
+-----
++++++
+conf2 1.5 0 89.59 0
+1 gpu conv fp16 1 add fp16 1 relu fp16 1 
+2 gpu conv fp16 1 add fp16 1 relu fp16 1 
+3 gpu conv fp16 1 add fp16 1 
+4 gpu add fp16 1 
+5 gpu relu fp16 1 
+6 gpu conv fp16 1 add fp16 1 relu fp16 1 
+7 gpu conv fp16 1 add fp16 1 
+8 gpu add fp16 1 
+9 gpu relu fp16 1 
+10 gpu conv fp16 1 add fp16 1 relu fp16 1 
+11 gpu conv fp16 1 add fp16 1 
+12 gpu add fp16 1 
+13 gpu relu fp16 1 
+14 gpu conv fp16 1 add fp16 1 relu fp16 1 
+15 gpu conv fp16 1 add fp16 1 
+16 gpu conv fp16 1 add fp16 1 
+17 gpu add fp16 1 
+18 gpu relu fp16 1 
+19 gpu conv fp16 1 add fp16 1 relu fp16 1 
+20 gpu conv fp16 1 add fp16 1 
+21 gpu add fp16 1 
+22 gpu relu fp16 1 
+23 gpu conv fp16 1 add fp16 1 relu fp16 1 
+24 gpu conv fp16 1 add fp16 1 
+25 gpu add fp16 1 
+26 gpu relu fp16 1 
+27 gpu conv fp16 1 add fp16 1 relu fp16 1 
+28 gpu conv fp16 1 add fp16 1 
+29 gpu conv fp16 1 add fp16 1 
+30 gpu add fp16 1 
+31 gpu relu fp16 1 
+32 gpu conv fp16 1 add fp16 1 relu fp16 1 
+33 gpu conv fp16 1 add fp16 1 
+34 gpu add fp16 1 
+35 gpu relu fp16 1 
+36 gpu conv fp16 1 add fp16 1 relu fp16 1 
+37 gpu conv fp16 1 add fp16 1 
+38 gpu add fp16 1 
+39 gpu relu fp16 1 
+40 gpu pool_mean fp16 1 
+41 gpu mul fp16 1 add fp16 1 
+42 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18.cpp b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18.cpp
index 09ca0f052985236a6e12fd9ea661d2a8640b48a0..d9f96cfdac18876b676369ba2c7c0e8f4e2ea986 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18.cpp
@@ -1227,11 +1227,12 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/resnet18_cifar10/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
   void *input = readTrainedWeights(input_path.c_str(), 0, 5000, 3, 32, 32);
-  std::string labels_path = dir_prefix + std::string("labels32.bin");
+  std::string labels_path = dir_prefix + std::string("labels.bin");
   uint32_t *labels = readLabels3(labels_path.c_str(), 5000);
   std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
   void *conv2d_1_w =
@@ -1462,7 +1463,7 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18_loop.cpp
index dc3097f992900e1263264ddf9da133ac25c6ab47..6bf5a58135d7fe7101c359a29f8909937d9bc8c7 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet18/resnet18_loop.cpp
@@ -12,7 +12,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(1);
 
@@ -21,7 +21,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(2);
 
@@ -30,7 +30,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(3);
 
@@ -39,7 +39,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(4);
 
@@ -48,7 +48,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(5);
 
@@ -57,7 +57,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(6);
 
@@ -66,7 +66,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(7);
 
@@ -75,7 +75,7 @@ void var_6_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(8);
 
@@ -102,7 +102,7 @@ void var_9_node(void *t1, size_t bytes_t1) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(11);
 
@@ -111,7 +111,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(12);
 
@@ -120,7 +120,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(13);
 
@@ -129,7 +129,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(14);
 
@@ -138,7 +138,7 @@ void var_13_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(15);
 
@@ -165,7 +165,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(18);
 
@@ -174,7 +174,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(19);
 
@@ -183,7 +183,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(20);
 
@@ -192,7 +192,7 @@ void var_19_node(void *t1, size_t bytes_t1) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(21);
 
@@ -201,7 +201,7 @@ void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(22);
 
@@ -228,7 +228,7 @@ void var_23_node(void *t1, size_t bytes_t1) {
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(25);
 
@@ -237,7 +237,7 @@ void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(26);
 
@@ -246,7 +246,7 @@ void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(27);
 
@@ -255,7 +255,7 @@ void var_26_node(void *t1, size_t bytes_t1) {
 }
 
 void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(28);
 
@@ -264,7 +264,7 @@ void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(29);
 
@@ -273,7 +273,7 @@ void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_29_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(30);
 
@@ -282,7 +282,7 @@ void var_29_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(31);
 
@@ -309,7 +309,7 @@ void var_32_node(void *t1, size_t bytes_t1) {
 }
 
 void var_33_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(34);
 
@@ -318,7 +318,7 @@ void var_33_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(35);
 
@@ -327,7 +327,7 @@ void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_35_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(36);
 
@@ -336,7 +336,7 @@ void var_35_node(void *t1, size_t bytes_t1) {
 }
 
 void var_36_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(37);
 
@@ -345,7 +345,7 @@ void var_36_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(38);
 
@@ -372,7 +372,7 @@ void var_39_node(void *t1, size_t bytes_t1) {
 }
 
 void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(41);
 
@@ -381,7 +381,7 @@ void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(42);
 
@@ -390,7 +390,7 @@ void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(43);
 
@@ -399,7 +399,7 @@ void var_42_node(void *t1, size_t bytes_t1) {
 }
 
 void var_43_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(44);
 
@@ -408,7 +408,7 @@ void var_43_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(45);
 
@@ -435,7 +435,7 @@ void var_46_node(void *t1, size_t bytes_t1) {
 }
 
 void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(48);
 
@@ -444,7 +444,7 @@ void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(49);
 
@@ -453,7 +453,7 @@ void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_49_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(50);
 
@@ -462,7 +462,7 @@ void var_49_node(void *t1, size_t bytes_t1) {
 }
 
 void var_50_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(51);
 
@@ -471,7 +471,7 @@ void var_50_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_51_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(52);
 
@@ -480,7 +480,7 @@ void var_51_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_52_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(53);
 
@@ -489,7 +489,7 @@ void var_52_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_53_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(54);
 
@@ -516,7 +516,7 @@ void var_55_node(void *t1, size_t bytes_t1) {
 }
 
 void var_56_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(57);
 
@@ -525,7 +525,7 @@ void var_56_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_57_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(58);
 
@@ -534,7 +534,7 @@ void var_57_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_58_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(59);
 
@@ -543,7 +543,7 @@ void var_58_node(void *t1, size_t bytes_t1) {
 }
 
 void var_59_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(60);
 
@@ -552,7 +552,7 @@ void var_59_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_60_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(61);
 
@@ -579,7 +579,7 @@ void var_62_node(void *t1, size_t bytes_t1) {
 }
 
 void var_63_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(64);
 
@@ -588,7 +588,7 @@ void var_63_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_64_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(65);
 
@@ -597,7 +597,7 @@ void var_64_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_65_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(66);
 
@@ -606,7 +606,7 @@ void var_65_node(void *t1, size_t bytes_t1) {
 }
 
 void var_66_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(67);
 
@@ -615,7 +615,7 @@ void var_66_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_67_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(68);
 
@@ -651,7 +651,7 @@ void var_70_node(void *t1, size_t bytes_t1) {
 }
 
 void var_71_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(72);
 
@@ -660,7 +660,7 @@ void var_71_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_72_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(73);
 
@@ -1305,7 +1305,7 @@ int main() {
 
   std::string input_path = dir_prefix + std::string("input.bin");
   // void* input = readTrainedWeights(input_path.c_str(), 0,5000,3,32,32);
-  std::string labels_path = dir_prefix + std::string("labels32.bin");
+  std::string labels_path = dir_prefix + std::string("labels.bin");
   // uint32_t* labels = readLabels3(labels_path.c_str(),5000);
 
   std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
@@ -1562,7 +1562,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e82fdcdca684bc5b836ab2cd80ea397766071d2c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/quant_ranges_rt.txt
@@ -0,0 +1,57 @@
+1 0 0 0 0 0 0 0 0
+2 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 0 0 0
+4 0 0 0 0 0 0 0 0
+5 0 0 0 0 0 0 0 0
+6 0 0 0 0 0 0 0 0
+7 0 0 0 0 0 0 0 0
+8 0 0 0 0 0 0 0 0
+9 0 0 0 0 0 0 0 0
+10 0 0 0 0 0 0 0 0
+11 0 0 0 0 0 0 0 0
+12 0 0 0 0 0 0 0 0
+13 0 0 0 0 0 0 0 0
+14 0 0 0 0 0 0 0 0
+15 0 0 0 0 0 0 0 0
+16 0 0 0 0 0 0 0 0
+17 0 0 0 0 0 0 0 0
+18 0 0 0 0 0 0 0 0
+19 0 0 0 0 0 0 0 0
+20 0 0 0 0 0 0 0 0
+21 0 0 0 0 0 0 0 0
+22 0 0 0 0 0 0 0 0
+23 0 0 0 0 0 0 0 0
+24 0 0 0 0 0 0 0 0
+25 0 0 0 0 0 0 0 0
+26 0 0 0 0 0 0 0 0
+27 0 0 0 0 0 0 0 0
+28 0 0 0 0 0 0 0 0
+29 0 0 0 0 0 0 0 0
+30 0 0 0 0 0 0 0 0
+31 0 0 0 0 0 0 0 0
+32 0 0 0 0 0 0 0 0
+33 0 0 0 0 0 0 0 0
+34 0 0 0 0 0 0 0 0
+35 0 0 0 0 0 0 0 0
+36 0 0 0 0 0 0 0 0
+37 0 0 0 0 0 0 0 0
+38 0 0 0 0 0 0 0 0
+39 0 0 0 0 0 0 0 0
+40 0 0 0 0 0 0 0 0
+41 0 0 0 0 0 0 0 0
+42 0 0 0 0 0 0 0 0
+43 0 0 0 0 0 0 0 0
+44 0 0 0 0 0 0 0 0
+45 0 0 0 0 0 0 0 0
+46 0 0 0 0 0 0 0 0
+47 0 0 0 0 0 0 0 0
+48 0 0 0 0 0 0 0 0
+49 0 0 0 0 0 0 0 0
+50 0 0 0 0 0 0 0 0
+51 0 0 0 0 0 0 0 0
+52 0 0 0 0 0 0 0 0
+53 0 0 0 0 0 0 0 0
+54 0 0 0 0 0 0 0 0
+55 0 0 0 0 0 0 0 0
+56 0 0 0 0 0 0 0 0
+57 0 0 0 0 0 0 0 0
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ede27ce6f5952d4d1be47640a46771d1f4c51ab2
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/data/tuner_confs.txt
@@ -0,0 +1,177 @@
+7161.053769000008
++++++
+conf1 1 1 75.7 0.0
+1 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+2 gpu batchnorm fp32 11
+3 gpu conv fp32 11 add fp32 1
+4 gpu batchnorm fp32 11
+5 gpu relu fp32 11
+6 gpu conv fp32 11 add fp32 1
+7 gpu batchnorm fp32 11
+8 gpu relu fp32 11
+9 gpu conv fp32 11 add fp32 1
+10 gpu batchnorm fp32 11
+11 gpu conv fp32 11 add fp32 1
+12 gpu batchnorm fp32 11
+13 gpu add fp32 11
+14 gpu relu fp32 11
+15 gpu conv fp32 11 add fp32 1
+16 gpu batchnorm fp32 11
+17 gpu relu fp32 11
+18 gpu conv fp32 11 add fp32 1
+19 gpu batchnorm fp32 11
+20 gpu relu fp32 11
+21 gpu conv fp32 11 add fp32 1
+22 gpu batchnorm fp32 11
+23 gpu add fp32 11
+24 gpu relu fp32 11
+25 gpu conv fp32 11 add fp32 1
+26 gpu batchnorm fp32 11
+27 gpu relu fp32 11
+28 gpu conv fp32 11 add fp32 1
+29 gpu batchnorm fp32 11
+30 gpu relu fp32 11
+31 gpu conv fp32 11 add fp32 1
+32 gpu batchnorm fp32 11
+33 gpu add fp32 11
+34 gpu relu fp32 11
+35 gpu conv fp32 11 add fp32 1
+36 gpu batchnorm fp32 11
+37 gpu relu fp32 11
+38 gpu conv fp32 11 add fp32 1
+39 gpu batchnorm fp32 11
+40 gpu relu fp32 11
+41 gpu conv fp32 11 add fp32 1
+42 gpu batchnorm fp32 11
+43 gpu conv fp32 11 add fp32 1
+44 gpu batchnorm fp32 11
+45 gpu add fp32 11
+46 gpu relu fp32 11
+47 gpu conv fp32 11 add fp32 1
+48 gpu batchnorm fp32 11
+49 gpu relu fp32 11
+50 gpu conv fp32 11 add fp32 1
+51 gpu batchnorm fp32 11
+52 gpu relu fp32 11
+53 gpu conv fp32 11 add fp32 1
+54 gpu batchnorm fp32 11
+55 gpu add fp32 11
+56 gpu relu fp32 11
+57 gpu conv fp32 11 add fp32 1
+58 gpu batchnorm fp32 11
+59 gpu relu fp32 11
+60 gpu conv fp32 11 add fp32 1
+61 gpu batchnorm fp32 11
+62 gpu relu fp32 11
+63 gpu conv fp32 11 add fp32 1
+64 gpu batchnorm fp32 11
+65 gpu add fp32 11
+66 gpu relu fp32 11
+67 gpu conv fp32 11 add fp32 1
+68 gpu batchnorm fp32 11
+69 gpu relu fp32 11
+70 gpu conv fp32 11 add fp32 1
+71 gpu batchnorm fp32 11
+72 gpu relu fp32 11
+73 gpu conv fp32 11 add fp32 1
+74 gpu batchnorm fp32 11
+75 gpu add fp32 11
+76 gpu relu fp32 11
+77 gpu conv fp32 11 add fp32 1
+78 gpu batchnorm fp32 11
+79 gpu relu fp32 11
+80 gpu conv fp32 11 add fp32 1
+81 gpu batchnorm fp32 11
+82 gpu relu fp32 11
+83 gpu conv fp32 11 add fp32 1
+84 gpu batchnorm fp32 11
+85 gpu conv fp32 11 add fp32 1
+86 gpu batchnorm fp32 11
+87 gpu add fp32 11
+88 gpu relu fp32 11
+89 gpu conv fp32 11 add fp32 1
+90 gpu batchnorm fp32 11
+91 gpu relu fp32 11
+92 gpu conv fp32 11 add fp32 1
+93 gpu batchnorm fp32 11
+94 gpu relu fp32 11
+95 gpu conv fp32 11 add fp32 1
+96 gpu batchnorm fp32 11
+97 gpu add fp32 11
+98 gpu relu fp32 11
+99 gpu conv fp32 11 add fp32 1
+100 gpu batchnorm fp32 11
+101 gpu relu fp32 11
+102 gpu conv fp32 11 add fp32 1
+103 gpu batchnorm fp32 11
+104 gpu relu fp32 11
+105 gpu conv fp32 11 add fp32 1
+106 gpu batchnorm fp32 11
+107 gpu add fp32 11
+108 gpu relu fp32 11
+109 gpu conv fp32 11 add fp32 1
+110 gpu batchnorm fp32 11
+111 gpu relu fp32 11
+112 gpu conv fp32 11 add fp32 1
+113 gpu batchnorm fp32 11
+114 gpu relu fp32 11
+115 gpu conv fp32 11 add fp32 1
+116 gpu batchnorm fp32 11
+117 gpu add fp32 11
+118 gpu relu fp32 11
+119 gpu conv fp32 11 add fp32 1
+120 gpu batchnorm fp32 11
+121 gpu relu fp32 11
+122 gpu conv fp32 11 add fp32 1
+123 gpu batchnorm fp32 11
+124 gpu relu fp32 11
+125 gpu conv fp32 11 add fp32 1
+126 gpu batchnorm fp32 11
+127 gpu add fp32 11
+128 gpu relu fp32 11
+129 gpu conv fp32 11 add fp32 1
+130 gpu batchnorm fp32 11
+131 gpu relu fp32 11
+132 gpu conv fp32 11 add fp32 1
+133 gpu batchnorm fp32 11
+134 gpu relu fp32 11
+135 gpu conv fp32 11 add fp32 1
+136 gpu batchnorm fp32 11
+137 gpu add fp32 11
+138 gpu relu fp32 11
+139 gpu conv fp32 11 add fp32 1
+140 gpu batchnorm fp32 11
+141 gpu relu fp32 11
+142 gpu conv fp32 11 add fp32 1
+143 gpu batchnorm fp32 11
+144 gpu relu fp32 11
+145 gpu conv fp32 11 add fp32 1
+146 gpu batchnorm fp32 11
+147 gpu conv fp32 11 add fp32 1
+148 gpu batchnorm fp32 11
+149 gpu add fp32 11
+150 gpu relu fp32 11
+151 gpu conv fp32 11 add fp32 1
+152 gpu batchnorm fp32 11
+153 gpu relu fp32 11
+154 gpu conv fp32 11 add fp32 1
+155 gpu batchnorm fp32 11
+156 gpu relu fp32 11
+157 gpu conv fp32 11 add fp32 1
+158 gpu batchnorm fp32 11
+159 gpu add fp32 11
+160 gpu relu fp32 11
+161 gpu conv fp32 11 add fp32 1
+162 gpu batchnorm fp32 11
+163 gpu relu fp32 11
+164 gpu conv fp32 11 add fp32 1
+165 gpu batchnorm fp32 11
+166 gpu relu fp32 11
+167 gpu conv fp32 11 add fp32 1
+168 gpu batchnorm fp32 11
+169 gpu add fp32 11
+170 gpu relu fp32 11
+171 gpu pool_max fp32 11
+172 gpu mul fp32 11 add fp32 1
+173 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
index a95fc1dbaf400c0b189babf75dc2b37df6a4587d..c4bd6be08b5afad0367e93f640c54b45e7d41938 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
@@ -4905,7 +4905,8 @@ typedef struct __attribute__((__packed__)) {
 
 int main() {
 
-  std::string dir_prefix = std::string("/home/hsharif3/resnet50_imagenet/");
+  std::string dir_prefix =
+      std::string(MODEL_PARAMS_DIR) + "/resnet50_imagenet/";
   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");
@@ -6733,7 +6734,7 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet_loop.cpp
index aa62ea8c9b41d7df6525ab24bf1bbe346a8b790f..42bad74ac39511a64ee4fd20e589cec5caf14836 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/resnet50_imagenet/resnet50_imagenet_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(1);
 
@@ -20,7 +20,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(2);
 
@@ -29,7 +29,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(3);
 
@@ -38,7 +38,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(4);
 
@@ -49,7 +49,7 @@ void var_3_node(void *t1, size_t bytes_t1) {
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                 size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                 size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(5);
 
@@ -58,7 +58,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(6);
 
@@ -67,7 +67,7 @@ void var_5_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(7);
 
@@ -78,7 +78,7 @@ void var_6_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                 size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                 size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(8);
 
@@ -87,7 +87,7 @@ void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_8_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(9);
 
@@ -96,7 +96,7 @@ void var_8_node(void *t1, size_t bytes_t1) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(10);
 
@@ -105,7 +105,7 @@ void var_9_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(11);
 
@@ -116,7 +116,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(12);
 
@@ -125,7 +125,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(13);
 
@@ -134,7 +134,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(14);
 
@@ -143,7 +143,7 @@ void var_13_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(15);
 
@@ -154,7 +154,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(16);
 
@@ -163,7 +163,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_16_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(17);
 
@@ -172,7 +172,7 @@ void var_16_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(18);
 
@@ -183,7 +183,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(19);
 
@@ -192,7 +192,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_19_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(20);
 
@@ -201,7 +201,7 @@ void var_19_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(21);
 
@@ -210,7 +210,7 @@ void var_20_node(void *t1, size_t bytes_t1) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(22);
 
@@ -219,7 +219,7 @@ void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_22_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(23);
 
@@ -230,7 +230,7 @@ void var_22_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_23_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(24);
 
@@ -239,7 +239,7 @@ void var_23_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_24_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(25);
 
@@ -248,7 +248,7 @@ void var_24_node(void *t1, size_t bytes_t1) {
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(26);
 
@@ -257,7 +257,7 @@ void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_26_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(27);
 
@@ -268,7 +268,7 @@ void var_26_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(28);
 
@@ -277,7 +277,7 @@ void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_28_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(29);
 
@@ -286,7 +286,7 @@ void var_28_node(void *t1, size_t bytes_t1) {
 }
 
 void var_29_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(30);
 
@@ -295,7 +295,7 @@ void var_29_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(31);
 
@@ -306,7 +306,7 @@ void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(32);
 
@@ -315,7 +315,7 @@ void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_32_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(33);
 
@@ -324,7 +324,7 @@ void var_32_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_33_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(34);
 
@@ -333,7 +333,7 @@ void var_33_node(void *t1, size_t bytes_t1) {
 }
 
 void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(35);
 
@@ -342,7 +342,7 @@ void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(36);
 
@@ -353,7 +353,7 @@ void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_36_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(37);
 
@@ -362,7 +362,7 @@ void var_36_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_37_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(38);
 
@@ -371,7 +371,7 @@ void var_37_node(void *t1, size_t bytes_t1) {
 }
 
 void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(39);
 
@@ -380,7 +380,7 @@ void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_39_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(40);
 
@@ -391,7 +391,7 @@ void var_39_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(41);
 
@@ -400,7 +400,7 @@ void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_41_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(42);
 
@@ -409,7 +409,7 @@ void var_41_node(void *t1, size_t bytes_t1) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(43);
 
@@ -418,7 +418,7 @@ void var_42_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_43_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(44);
 
@@ -429,7 +429,7 @@ void var_43_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(45);
 
@@ -438,7 +438,7 @@ void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(46);
 
@@ -447,7 +447,7 @@ void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_46_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(47);
 
@@ -456,7 +456,7 @@ void var_46_node(void *t1, size_t bytes_t1) {
 }
 
 void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(48);
 
@@ -465,7 +465,7 @@ void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(49);
 
@@ -476,7 +476,7 @@ void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_49_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(50);
 
@@ -485,7 +485,7 @@ void var_49_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_50_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(51);
 
@@ -494,7 +494,7 @@ void var_50_node(void *t1, size_t bytes_t1) {
 }
 
 void var_51_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(52);
 
@@ -503,7 +503,7 @@ void var_51_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_52_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(53);
 
@@ -514,7 +514,7 @@ void var_52_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_53_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(54);
 
@@ -523,7 +523,7 @@ void var_53_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_54_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(55);
 
@@ -532,7 +532,7 @@ void var_54_node(void *t1, size_t bytes_t1) {
 }
 
 void var_55_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(56);
 
@@ -541,7 +541,7 @@ void var_55_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_56_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(57);
 
@@ -552,7 +552,7 @@ void var_56_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_57_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(58);
 
@@ -561,7 +561,7 @@ void var_57_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_58_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(59);
 
@@ -570,7 +570,7 @@ void var_58_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_59_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(60);
 
@@ -581,7 +581,7 @@ void var_59_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_60_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(61);
 
@@ -590,7 +590,7 @@ void var_60_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_61_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(62);
 
@@ -599,7 +599,7 @@ void var_61_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_62_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(63);
 
@@ -608,7 +608,7 @@ void var_62_node(void *t1, size_t bytes_t1) {
 }
 
 void var_63_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(64);
 
@@ -617,7 +617,7 @@ void var_63_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_64_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(65);
 
@@ -628,7 +628,7 @@ void var_64_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_65_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(66);
 
@@ -637,7 +637,7 @@ void var_65_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_66_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(67);
 
@@ -646,7 +646,7 @@ void var_66_node(void *t1, size_t bytes_t1) {
 }
 
 void var_67_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(68);
 
@@ -655,7 +655,7 @@ void var_67_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_68_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(69);
 
@@ -666,7 +666,7 @@ void var_68_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_69_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(70);
 
@@ -675,7 +675,7 @@ void var_69_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_70_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(71);
 
@@ -684,7 +684,7 @@ void var_70_node(void *t1, size_t bytes_t1) {
 }
 
 void var_71_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(72);
 
@@ -693,7 +693,7 @@ void var_71_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_72_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(73);
 
@@ -704,7 +704,7 @@ void var_72_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_73_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(74);
 
@@ -713,7 +713,7 @@ void var_73_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_74_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(75);
 
@@ -722,7 +722,7 @@ void var_74_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_75_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(76);
 
@@ -731,7 +731,7 @@ void var_75_node(void *t1, size_t bytes_t1) {
 }
 
 void var_76_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(77);
 
@@ -740,7 +740,7 @@ void var_76_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_77_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(78);
 
@@ -751,7 +751,7 @@ void var_77_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_78_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(79);
 
@@ -760,7 +760,7 @@ void var_78_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_79_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(80);
 
@@ -769,7 +769,7 @@ void var_79_node(void *t1, size_t bytes_t1) {
 }
 
 void var_80_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(81);
 
@@ -778,7 +778,7 @@ void var_80_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_81_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(82);
 
@@ -789,7 +789,7 @@ void var_81_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_82_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(83);
 
@@ -798,7 +798,7 @@ void var_82_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_83_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(84);
 
@@ -807,7 +807,7 @@ void var_83_node(void *t1, size_t bytes_t1) {
 }
 
 void var_84_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(85);
 
@@ -816,7 +816,7 @@ void var_84_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_85_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(86);
 
@@ -827,7 +827,7 @@ void var_85_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_86_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(87);
 
@@ -836,7 +836,7 @@ void var_86_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_87_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(88);
 
@@ -845,7 +845,7 @@ void var_87_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_88_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(89);
 
@@ -854,7 +854,7 @@ void var_88_node(void *t1, size_t bytes_t1) {
 }
 
 void var_89_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(90);
 
@@ -863,7 +863,7 @@ void var_89_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_90_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(91);
 
@@ -874,7 +874,7 @@ void var_90_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_91_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(92);
 
@@ -883,7 +883,7 @@ void var_91_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_92_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(93);
 
@@ -892,7 +892,7 @@ void var_92_node(void *t1, size_t bytes_t1) {
 }
 
 void var_93_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(94);
 
@@ -901,7 +901,7 @@ void var_93_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_94_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(95);
 
@@ -912,7 +912,7 @@ void var_94_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_95_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(96);
 
@@ -921,7 +921,7 @@ void var_95_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_96_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(97);
 
@@ -930,7 +930,7 @@ void var_96_node(void *t1, size_t bytes_t1) {
 }
 
 void var_97_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(98);
 
@@ -939,7 +939,7 @@ void var_97_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_98_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(99);
 
@@ -950,7 +950,7 @@ void var_98_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_99_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
                  size_t bytes_t3, void *t4, size_t bytes_t4, void *t5,
                  size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(100);
 
@@ -959,7 +959,7 @@ void var_99_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2, void *t3,
 }
 
 void var_100_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(101);
 
@@ -968,7 +968,7 @@ void var_100_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_101_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(102);
 
@@ -977,7 +977,7 @@ void var_101_node(void *t1, size_t bytes_t1) {
 }
 
 void var_102_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(103);
 
@@ -986,7 +986,7 @@ void var_102_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_103_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(104);
 
@@ -997,7 +997,7 @@ void var_103_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_104_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(105);
 
@@ -1006,7 +1006,7 @@ void var_104_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_105_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(106);
 
@@ -1015,7 +1015,7 @@ void var_105_node(void *t1, size_t bytes_t1) {
 }
 
 void var_106_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(107);
 
@@ -1024,7 +1024,7 @@ void var_106_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_107_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(108);
 
@@ -1035,7 +1035,7 @@ void var_107_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_108_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(109);
 
@@ -1044,7 +1044,7 @@ void var_108_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_109_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(110);
 
@@ -1053,7 +1053,7 @@ void var_109_node(void *t1, size_t bytes_t1) {
 }
 
 void var_110_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(111);
 
@@ -1062,7 +1062,7 @@ void var_110_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_111_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(112);
 
@@ -1073,7 +1073,7 @@ void var_111_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_112_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(113);
 
@@ -1082,7 +1082,7 @@ void var_112_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_113_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(114);
 
@@ -1091,7 +1091,7 @@ void var_113_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_114_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(115);
 
@@ -1102,7 +1102,7 @@ void var_114_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_115_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(116);
 
@@ -1111,7 +1111,7 @@ void var_115_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_116_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(117);
 
@@ -1120,7 +1120,7 @@ void var_116_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_117_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(118);
 
@@ -1129,7 +1129,7 @@ void var_117_node(void *t1, size_t bytes_t1) {
 }
 
 void var_118_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(119);
 
@@ -1138,7 +1138,7 @@ void var_118_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_119_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(120);
 
@@ -1149,7 +1149,7 @@ void var_119_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_120_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(121);
 
@@ -1158,7 +1158,7 @@ void var_120_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_121_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(122);
 
@@ -1167,7 +1167,7 @@ void var_121_node(void *t1, size_t bytes_t1) {
 }
 
 void var_122_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(123);
 
@@ -1176,7 +1176,7 @@ void var_122_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_123_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(124);
 
@@ -1187,7 +1187,7 @@ void var_123_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_124_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(125);
 
@@ -1196,7 +1196,7 @@ void var_124_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_125_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(126);
 
@@ -1205,7 +1205,7 @@ void var_125_node(void *t1, size_t bytes_t1) {
 }
 
 void var_126_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(127);
 
@@ -1214,7 +1214,7 @@ void var_126_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_127_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(128);
 
@@ -1225,7 +1225,7 @@ void var_127_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_128_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(129);
 
@@ -1234,7 +1234,7 @@ void var_128_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_129_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(130);
 
@@ -1243,7 +1243,7 @@ void var_129_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_130_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(131);
 
@@ -1252,7 +1252,7 @@ void var_130_node(void *t1, size_t bytes_t1) {
 }
 
 void var_131_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(132);
 
@@ -1261,7 +1261,7 @@ void var_131_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_132_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(133);
 
@@ -1272,7 +1272,7 @@ void var_132_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_133_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(134);
 
@@ -1281,7 +1281,7 @@ void var_133_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_134_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(135);
 
@@ -1290,7 +1290,7 @@ void var_134_node(void *t1, size_t bytes_t1) {
 }
 
 void var_135_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(136);
 
@@ -1299,7 +1299,7 @@ void var_135_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_136_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(137);
 
@@ -1310,7 +1310,7 @@ void var_136_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_137_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(138);
 
@@ -1319,7 +1319,7 @@ void var_137_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_138_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(139);
 
@@ -1328,7 +1328,7 @@ void var_138_node(void *t1, size_t bytes_t1) {
 }
 
 void var_139_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(140);
 
@@ -1337,7 +1337,7 @@ void var_139_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_140_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(141);
 
@@ -1348,7 +1348,7 @@ void var_140_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_141_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(142);
 
@@ -1357,7 +1357,7 @@ void var_141_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_142_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(143);
 
@@ -1366,7 +1366,7 @@ void var_142_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_143_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(144);
 
@@ -1375,7 +1375,7 @@ void var_143_node(void *t1, size_t bytes_t1) {
 }
 
 void var_144_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(145);
 
@@ -1384,7 +1384,7 @@ void var_144_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_145_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(146);
 
@@ -1395,7 +1395,7 @@ void var_145_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_146_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(147);
 
@@ -1404,7 +1404,7 @@ void var_146_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_147_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(148);
 
@@ -1413,7 +1413,7 @@ void var_147_node(void *t1, size_t bytes_t1) {
 }
 
 void var_148_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(149);
 
@@ -1422,7 +1422,7 @@ void var_148_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_149_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(150);
 
@@ -1433,7 +1433,7 @@ void var_149_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_150_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(151);
 
@@ -1442,7 +1442,7 @@ void var_150_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_151_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(152);
 
@@ -1451,7 +1451,7 @@ void var_151_node(void *t1, size_t bytes_t1) {
 }
 
 void var_152_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(153);
 
@@ -1460,7 +1460,7 @@ void var_152_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_153_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(154);
 
@@ -1471,7 +1471,7 @@ void var_153_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_154_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(155);
 
@@ -1480,7 +1480,7 @@ void var_154_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_155_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(156);
 
@@ -1489,7 +1489,7 @@ void var_155_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_156_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(157);
 
@@ -1498,7 +1498,7 @@ void var_156_node(void *t1, size_t bytes_t1) {
 }
 
 void var_157_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(158);
 
@@ -1507,7 +1507,7 @@ void var_157_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_158_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(159);
 
@@ -1518,7 +1518,7 @@ void var_158_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_159_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(160);
 
@@ -1527,7 +1527,7 @@ void var_159_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_160_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(161);
 
@@ -1536,7 +1536,7 @@ void var_160_node(void *t1, size_t bytes_t1) {
 }
 
 void var_161_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(162);
 
@@ -1545,7 +1545,7 @@ void var_161_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_162_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(163);
 
@@ -1556,7 +1556,7 @@ void var_162_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_163_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(164);
 
@@ -1565,7 +1565,7 @@ void var_163_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_164_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(165);
 
@@ -1574,7 +1574,7 @@ void var_164_node(void *t1, size_t bytes_t1) {
 }
 
 void var_165_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(166);
 
@@ -1583,7 +1583,7 @@ void var_165_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_166_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(167);
 
@@ -1594,7 +1594,7 @@ void var_166_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_167_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(168);
 
@@ -1603,7 +1603,7 @@ void var_167_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_168_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(169);
 
@@ -1612,7 +1612,7 @@ void var_168_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_169_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(170);
 
@@ -1621,7 +1621,7 @@ void var_169_node(void *t1, size_t bytes_t1) {
 }
 
 void var_170_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(171);
 
@@ -1630,7 +1630,7 @@ void var_170_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_171_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(172);
 
@@ -1641,7 +1641,7 @@ void var_171_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_172_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(173);
 
@@ -1650,7 +1650,7 @@ void var_172_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_173_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(174);
 
@@ -1659,7 +1659,7 @@ void var_173_node(void *t1, size_t bytes_t1) {
 }
 
 void var_174_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(175);
 
@@ -1668,7 +1668,7 @@ void var_174_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_175_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(176);
 
@@ -1679,7 +1679,7 @@ void var_175_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_176_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(177);
 
@@ -1688,7 +1688,7 @@ void var_176_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_177_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(178);
 
@@ -1697,7 +1697,7 @@ void var_177_node(void *t1, size_t bytes_t1) {
 }
 
 void var_178_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(179);
 
@@ -1706,7 +1706,7 @@ void var_178_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_179_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(180);
 
@@ -1717,7 +1717,7 @@ void var_179_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_180_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(181);
 
@@ -1726,7 +1726,7 @@ void var_180_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_181_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(182);
 
@@ -1735,7 +1735,7 @@ void var_181_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_182_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(183);
 
@@ -1744,7 +1744,7 @@ void var_182_node(void *t1, size_t bytes_t1) {
 }
 
 void var_183_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(184);
 
@@ -1753,7 +1753,7 @@ void var_183_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_184_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(185);
 
@@ -1764,7 +1764,7 @@ void var_184_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_185_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(186);
 
@@ -1773,7 +1773,7 @@ void var_185_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_186_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(187);
 
@@ -1782,7 +1782,7 @@ void var_186_node(void *t1, size_t bytes_t1) {
 }
 
 void var_187_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(188);
 
@@ -1791,7 +1791,7 @@ void var_187_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_188_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(189);
 
@@ -1802,7 +1802,7 @@ void var_188_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_189_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(190);
 
@@ -1811,7 +1811,7 @@ void var_189_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_190_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(191);
 
@@ -1820,7 +1820,7 @@ void var_190_node(void *t1, size_t bytes_t1) {
 }
 
 void var_191_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(192);
 
@@ -1829,7 +1829,7 @@ void var_191_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_192_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(193);
 
@@ -1840,7 +1840,7 @@ void var_192_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_193_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(194);
 
@@ -1849,7 +1849,7 @@ void var_193_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_194_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(195);
 
@@ -1858,7 +1858,7 @@ void var_194_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_195_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(196);
 
@@ -1869,7 +1869,7 @@ void var_195_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_196_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(197);
 
@@ -1878,7 +1878,7 @@ void var_196_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_197_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(198);
 
@@ -1887,7 +1887,7 @@ void var_197_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_198_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(199);
 
@@ -1896,7 +1896,7 @@ void var_198_node(void *t1, size_t bytes_t1) {
 }
 
 void var_199_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(200);
 
@@ -1905,7 +1905,7 @@ void var_199_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_200_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(201);
 
@@ -1916,7 +1916,7 @@ void var_200_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_201_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(202);
 
@@ -1925,7 +1925,7 @@ void var_201_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_202_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(203);
 
@@ -1934,7 +1934,7 @@ void var_202_node(void *t1, size_t bytes_t1) {
 }
 
 void var_203_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(204);
 
@@ -1943,7 +1943,7 @@ void var_203_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_204_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(205);
 
@@ -1954,7 +1954,7 @@ void var_204_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_205_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(206);
 
@@ -1963,7 +1963,7 @@ void var_205_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_206_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(207);
 
@@ -1972,7 +1972,7 @@ void var_206_node(void *t1, size_t bytes_t1) {
 }
 
 void var_207_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(208);
 
@@ -1981,7 +1981,7 @@ void var_207_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_208_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(209);
 
@@ -1992,7 +1992,7 @@ void var_208_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_209_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(210);
 
@@ -2001,7 +2001,7 @@ void var_209_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_210_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(211);
 
@@ -2010,7 +2010,7 @@ void var_210_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_211_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(212);
 
@@ -2019,7 +2019,7 @@ void var_211_node(void *t1, size_t bytes_t1) {
 }
 
 void var_212_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(213);
 
@@ -2028,7 +2028,7 @@ void var_212_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_213_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(214);
 
@@ -2039,7 +2039,7 @@ void var_213_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_214_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(215);
 
@@ -2048,7 +2048,7 @@ void var_214_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_215_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(216);
 
@@ -2057,7 +2057,7 @@ void var_215_node(void *t1, size_t bytes_t1) {
 }
 
 void var_216_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(217);
 
@@ -2066,7 +2066,7 @@ void var_216_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_217_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(218);
 
@@ -2077,7 +2077,7 @@ void var_217_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_218_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(219);
 
@@ -2086,7 +2086,7 @@ void var_218_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_219_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(220);
 
@@ -2095,7 +2095,7 @@ void var_219_node(void *t1, size_t bytes_t1) {
 }
 
 void var_220_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(221);
 
@@ -2104,7 +2104,7 @@ void var_220_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_221_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(222);
 
@@ -2115,7 +2115,7 @@ void var_221_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 void var_222_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
                   void *t3, size_t bytes_t3, void *t4, size_t bytes_t4,
                   void *t5, size_t bytes_t5) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(5, t1, t2, t3, t4, t5, 0);
   __hpvm__node_id(223);
 
@@ -2124,7 +2124,7 @@ void var_222_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2,
 }
 
 void var_223_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(224);
 
@@ -2133,7 +2133,7 @@ void var_223_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_224_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(225);
 
@@ -2142,7 +2142,7 @@ void var_224_node(void *t1, size_t bytes_t1) {
 }
 
 void var_225_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(226);
 
@@ -2151,7 +2151,7 @@ void var_225_node(void *t1, size_t bytes_t1) {
 }
 
 void var_226_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(227);
 
@@ -2160,7 +2160,7 @@ void var_226_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_227_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
   __hpvm__node_id(228);
 
@@ -2169,7 +2169,7 @@ void var_227_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_228_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
   __hpvm__node_id(229);
 
@@ -5134,7 +5134,8 @@ typedef struct __attribute__((__packed__)) {
 
 int main() {
 
-  std::string dir_prefix = std::string("/home/hsharif3/resnet50_imagenet/");
+  std::string dir_prefix =
+      std::string(MODEL_PARAMS_DIR) + "/resnet50_imagenet/";
   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");
@@ -6979,7 +6980,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19f5523523f3b9fc7b8f81c69112630003d5597e
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/quant_ranges_rt.txt
@@ -0,0 +1,15 @@
+1 -1.8816367 2.0934217 -0.53275156 0.49437004 -0.6403629 0.2490165 0.0 1.35908746719 
+2 0.0 1.35908746719 -0.2688396 0.20639156 -0.7745511 0.82006615 0.0 2.52123117924 
+3 0.0 2.52123117924 -0.16776876 0.14878987 -0.35283303 0.5154362 0.0 1.20119857848 
+4 0.0 1.20119857848 -0.088948585 0.114222586 -0.30250227 0.36856708 0.0 1.03598809302 
+5 0.0 1.03598809302 -0.07739562 0.10973293 -0.15568458 0.17634983 0.0 0.300495595038 
+6 0.0 0.300495595038 -0.051649556 0.05435231 -0.07395447 0.07996062 0.0 0.11490475405 
+7 0.0 0.11490475405 -0.043513633 0.07577866 -0.06921874 0.02660573 0.0 0.16232508488 
+8 0.0 0.16232508488 -0.033842053 0.045218028 -0.022827804 0.023845317 0.0 0.124249965735 
+9 0.0 0.124249965735 -0.02211613 0.032084666 -0.02699063 0.03773564 0.0 0.174634486511 
+10 0.0 0.174634486511 -0.01979376 0.034854397 -0.036107242 0.07056531 0.0 0.575175762177 
+11 0.0 0.575175762177 -0.03452098 0.046055835 -0.051925894 0.07039055 0.0 0.771875114441 
+12 0.0 0.771875114441 -0.025946895 0.040090334 -0.06049362 0.12658806 0.0 1.17285169065 
+13 0.0 1.17285169065 -0.021766115 0.03315237 -0.20705001 0.117947325 0.0 2.00157693863 
+14 0.0 2.00157693863 -0.042597745 0.046707444 -0.21937433 0.2545502 0.0 2.00236111879 
+15 0.0 2.00236111879 -0.32550547 0.30829763 -1.1787822 1.2378151 -18.2514705467 24.1736344528
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c9a6612a5df150f58c69e1a7faeaf83ed5c7d605
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/data/tuner_confs.txt
@@ -0,0 +1,38 @@
++++++
+conf1 1 0 90.19 0
+1 gpu conv fp32 1 add fp32 1 relu fp32 1 
+2 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+3 gpu conv fp32 1 add fp32 1 relu fp32 1 
+4 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+5 gpu conv fp32 1 add fp32 1 relu fp32 1 
+6 gpu conv fp32 1 add fp32 1 relu fp32 1 
+7 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+8 gpu conv fp32 1 add fp32 1 relu fp32 1 
+9 gpu conv fp32 1 add fp32 1 relu fp32 1 
+10 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+11 gpu conv fp32 1 add fp32 1 relu fp32 1 
+12 gpu conv fp32 1 add fp32 1 relu fp32 1 
+13 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+14 gpu mul fp32 1 add fp32 1 relu fp32 1 
+15 gpu mul fp32 1 add fp32 1 
+16 gpu softmax fp32 1
+-----
++++++
+conf2 1.5 0 90.19 0
+1 gpu conv fp16 1 add fp16 1 relu fp16 1 
+2 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+3 gpu conv fp16 1 add fp16 1 relu fp16 1 
+4 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+5 gpu conv fp16 1 add fp16 1 relu fp16 1 
+6 gpu conv fp16 1 add fp16 1 relu fp16 1 
+7 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+8 gpu conv fp16 1 add fp16 1 relu fp16 1 
+9 gpu conv fp16 1 add fp16 1 relu fp16 1 
+10 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+11 gpu conv fp16 1 add fp16 1 relu fp16 1 
+12 gpu conv fp16 1 add fp16 1 relu fp16 1 
+13 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+14 gpu mul fp16 1 add fp16 1 relu fp16 1 
+15 gpu mul fp16 1 add fp16 1 
+16 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
index 03c647d74244cf72adce8ddc11d361904f1a5da8..f1533c75b4b838f5b86dfbf915cfd359b9682636 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
@@ -828,9 +828,10 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/vgg16_cifar10/";
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.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, 64, 3, 3, 3);
@@ -1000,7 +1001,7 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   computeAccuracy3(labels, result);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10_loop.cpp
index 21e263e098bfe9d3fdade2774461ec8705a32a04..059bff6d22a51853090700072d4cf3915ed5f796 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar10/vgg16_cifar10_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -123,7 +123,7 @@ void var_13_node(void *t1, size_t bytes_t1) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -131,7 +131,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -139,7 +139,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_16_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -147,7 +147,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -155,7 +155,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -163,7 +163,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -171,7 +171,7 @@ void var_19_node(void *t1, size_t bytes_t1) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -179,7 +179,7 @@ void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -187,7 +187,7 @@ void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_22_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -195,7 +195,7 @@ void var_22_node(void *t1, size_t bytes_t1) {
 }
 
 void var_23_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -203,7 +203,7 @@ void var_23_node(void *t1, size_t bytes_t1) {
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -211,7 +211,7 @@ void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -219,7 +219,7 @@ void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -227,7 +227,7 @@ void var_26_node(void *t1, size_t bytes_t1) {
 }
 
 void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -235,7 +235,7 @@ void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -243,7 +243,7 @@ void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_29_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -251,7 +251,7 @@ void var_29_node(void *t1, size_t bytes_t1) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -259,7 +259,7 @@ void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -267,7 +267,7 @@ void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_32_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -275,7 +275,7 @@ void var_32_node(void *t1, size_t bytes_t1) {
 }
 
 void var_33_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -283,7 +283,7 @@ void var_33_node(void *t1, size_t bytes_t1) {
 }
 
 void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -291,7 +291,7 @@ void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -299,7 +299,7 @@ void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_36_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -307,7 +307,7 @@ void var_36_node(void *t1, size_t bytes_t1) {
 }
 
 void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -315,7 +315,7 @@ void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -323,7 +323,7 @@ void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_39_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -331,7 +331,7 @@ void var_39_node(void *t1, size_t bytes_t1) {
 }
 
 void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -339,7 +339,7 @@ void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -347,7 +347,7 @@ void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -355,7 +355,7 @@ void var_42_node(void *t1, size_t bytes_t1) {
 }
 
 void var_43_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -363,7 +363,7 @@ void var_43_node(void *t1, size_t bytes_t1) {
 }
 
 void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -371,7 +371,7 @@ void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -379,7 +379,7 @@ void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_46_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -387,7 +387,7 @@ void var_46_node(void *t1, size_t bytes_t1) {
 }
 
 void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -395,7 +395,7 @@ void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -828,9 +828,10 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/vgg16_cifar10/";
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.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, 64, 3, 3, 3);
@@ -1012,7 +1013,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/quant_ranges_rt.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/quant_ranges_rt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e78e0a2bf8517734a4f42200b411829b5e39877
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/quant_ranges_rt.txt
@@ -0,0 +1,15 @@
+1 -1.7829767 1.9456929 -0.7450515 0.71249133 -1.5885142 0.275554 0.0 8.190712 
+2 0.0 8.190712 -0.30790088 0.43504623 -1.4242363 1.2602744 0.0 19.023172 
+3 0.0 19.023172 -0.29189092 0.26958522 -1.0527138 0.9075671 0.0 14.428051 
+4 0.0 14.428051 -0.15521508 0.1829038 -0.845419 1.9358484 0.0 23.065294 
+5 0.0 23.065294 -0.13149762 0.14811686 -0.7162557 1.0370971 0.0 15.165984 
+6 0.0 15.165984 -0.06236292 0.08321518 -0.9067523 0.9922458 0.0 13.664733 
+7 0.0 13.664733 -0.06471479 0.1024472 -0.15943134 0.7988499 0.0 19.025272 
+8 0.0 19.025272 -0.06320205 0.08291938 -0.32540628 0.5203079 0.0 6.727217 
+9 0.0 6.727217 -0.037707984 0.051601283 -0.25622904 0.11251946 0.0 3.2003012 
+10 0.0 3.2003012 -0.056007143 0.09549151 -0.11591503 0.06267536 0.0 4.321189 
+11 0.0 4.321189 -0.060094673 0.10868926 -0.105962686 0.09584572 0.0 2.936297 
+12 0.0 2.936297 -0.034618977 0.05792674 -0.4237576 0.11035452 0.0 4.87262 
+13 0.0 4.87262 -0.035480656 0.058295887 -0.21477045 0.14263579 0.0 10.32133 
+14 0.0 10.32133 -0.08929961 0.11301676 -0.20798548 0.47405547 0.0 13.91 
+15 0.0 13.91 -0.6627122 0.35539475 -1.0631907 0.9830786 -70.45701 87.34367 
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2662b4ba78dc54686d61f45242fb38f4ca75402c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/data/tuner_confs.txt
@@ -0,0 +1,39 @@
+2000
++++++
+conf1 1 0 90.19 0
+1 gpu conv fp32 1 add fp32 1 relu fp32 1 
+2 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+3 gpu conv fp32 1 add fp32 1 relu fp32 1 
+4 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+5 gpu conv fp32 1 add fp32 1 relu fp32 1 
+6 gpu conv fp32 1 add fp32 1 relu fp32 1 
+7 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+8 gpu conv fp32 1 add fp32 1 relu fp32 1 
+9 gpu conv fp32 1 add fp32 1 relu fp32 1 
+10 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+11 gpu conv fp32 1 add fp32 1 relu fp32 1 
+12 gpu conv fp32 1 add fp32 1 relu fp32 1 
+13 gpu conv fp32 1 add fp32 1 relu fp32 1 pool_max fp32 1 
+14 gpu mul fp32 1 add fp32 1 relu fp32 1 
+15 gpu mul fp32 1 add fp32 1 
+16 gpu softmax fp32 1
+-----
++++++
+conf2 1.5 0 90.19 0
+1 gpu conv fp16 1 add fp16 1 relu fp16 1 
+2 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+3 gpu conv fp16 1 add fp16 1 relu fp16 1 
+4 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+5 gpu conv fp16 1 add fp16 1 relu fp16 1 
+6 gpu conv fp16 1 add fp16 1 relu fp16 1 
+7 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+8 gpu conv fp16 1 add fp16 1 relu fp16 1 
+9 gpu conv fp16 1 add fp16 1 relu fp16 1 
+10 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+11 gpu conv fp16 1 add fp16 1 relu fp16 1 
+12 gpu conv fp16 1 add fp16 1 relu fp16 1 
+13 gpu conv fp16 1 add fp16 1 relu fp16 1 pool_max fp16 1 
+14 gpu mul fp16 1 add fp16 1 relu fp16 1 
+15 gpu mul fp16 1 add fp16 1 
+16 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
index dcbf59d3fd8a88fd2ef700de6a7a62b64c543b29..41fe9ae0f34c5c5086f8c16491f5035d5a382702 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
@@ -828,10 +828,11 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/vgg16_cifar100/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.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, 64, 3, 3, 3);
@@ -993,8 +994,7 @@ int main() {
   void *dfg = __hpvm__launch(0, root, (void *)args);
 
   __hpvm__wait(dfg);
-
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100_loop.cpp
index e2820d8c04990ef6aeac13fd9b63a7bba97a28ef..3a853d3a0f5399057164594951a884222a02e105 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_cifar100/vgg16_cifar100_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -123,7 +123,7 @@ void var_13_node(void *t1, size_t bytes_t1) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -131,7 +131,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -139,7 +139,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_16_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -147,7 +147,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -155,7 +155,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -163,7 +163,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -171,7 +171,7 @@ void var_19_node(void *t1, size_t bytes_t1) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -179,7 +179,7 @@ void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -187,7 +187,7 @@ void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_22_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -195,7 +195,7 @@ void var_22_node(void *t1, size_t bytes_t1) {
 }
 
 void var_23_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -203,7 +203,7 @@ void var_23_node(void *t1, size_t bytes_t1) {
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -211,7 +211,7 @@ void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -219,7 +219,7 @@ void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -227,7 +227,7 @@ void var_26_node(void *t1, size_t bytes_t1) {
 }
 
 void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -235,7 +235,7 @@ void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -243,7 +243,7 @@ void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_29_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -251,7 +251,7 @@ void var_29_node(void *t1, size_t bytes_t1) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -259,7 +259,7 @@ void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -267,7 +267,7 @@ void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_32_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -275,7 +275,7 @@ void var_32_node(void *t1, size_t bytes_t1) {
 }
 
 void var_33_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -283,7 +283,7 @@ void var_33_node(void *t1, size_t bytes_t1) {
 }
 
 void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -291,7 +291,7 @@ void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -299,7 +299,7 @@ void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_36_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -307,7 +307,7 @@ void var_36_node(void *t1, size_t bytes_t1) {
 }
 
 void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -315,7 +315,7 @@ void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -323,7 +323,7 @@ void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_39_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -331,7 +331,7 @@ void var_39_node(void *t1, size_t bytes_t1) {
 }
 
 void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -339,7 +339,7 @@ void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -347,7 +347,7 @@ void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -355,7 +355,7 @@ void var_42_node(void *t1, size_t bytes_t1) {
 }
 
 void var_43_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -363,7 +363,7 @@ void var_43_node(void *t1, size_t bytes_t1) {
 }
 
 void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -371,7 +371,7 @@ void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -379,7 +379,7 @@ void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_46_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -387,7 +387,7 @@ void var_46_node(void *t1, size_t bytes_t1) {
 }
 
 void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -395,7 +395,7 @@ void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -828,10 +828,11 @@ typedef struct __attribute__((__packed__)) {
 } RootIn;
 
 int main() {
+
   std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/vgg16_cifar100/";
 
   std::string input_path = dir_prefix + std::string("input.bin");
-  std::string labels_path = dir_prefix + std::string("labels32.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, 64, 3, 3, 3);
@@ -1013,7 +1014,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
diff --git a/hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/quant_ranges2.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/data/quant_ranges_rt.txt
similarity index 100%
rename from hpvm/projects/hpvm-tensor-rt/autotuner/data/vgg16_imagenet/quant_ranges2.txt
rename to hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/data/quant_ranges_rt.txt
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/data/tuner_confs.txt b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/data/tuner_confs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cf93cd1286cb6f1358a46cde5991d19ab451c78a
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/data/tuner_confs.txt
@@ -0,0 +1,21 @@
+19194.623482
++++++
+conf1 1 1 72.84 0.0
+1 gpu conv fp32 11 add fp32 1 relu fp32 1
+2 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+3 gpu conv fp32 11 add fp32 1 relu fp32 1
+4 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+5 gpu conv fp32 11 add fp32 1 relu fp32 1
+6 gpu conv fp32 11 add fp32 1 relu fp32 1
+7 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+8 gpu conv fp32 11 add fp32 1 relu fp32 1
+9 gpu conv fp32 11 add fp32 1 relu fp32 1
+10 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+11 gpu conv fp32 11 add fp32 1 relu fp32 1
+12 gpu conv fp32 11 add fp32 1 relu fp32 1
+13 gpu conv fp32 11 add fp32 1 relu fp32 1 pool_max fp32 1
+14 gpu mul fp32 11 add fp32 1 relu fp32 1
+15 gpu mul fp32 11 add fp32 1 relu fp32 1
+16 gpu mul fp32 11 add fp32 1
+17 gpu softmax fp32 1
+-----
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
index 27db61ee7285432aa22d908679559f8bd8166d6c..f269aa9091521809751cd2214a46d039379c0114 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
@@ -877,7 +877,7 @@ typedef struct __attribute__((__packed__)) {
 
 int main() {
 
-  std::string dir_prefix = std::string("/home/hsharif3/vgg16_imagenet/");
+  std::string dir_prefix = std::string(MODEL_PARAMS_DIR) + "/vgg16_imagenet/";
   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");
@@ -1053,7 +1053,7 @@ int main() {
 
   __hpvm__wait(dfg);
 
-  void *result = static_cast<RootIn *>(args)->input;
+  void *result = static_cast<RootIn *>(args)->r.tensor;
   hpvm_request_tensor(result, 0);
 
   __hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet_loop.cpp b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet_loop.cpp
index 0740cf0f3a06d227f86c66b45eb22b4fd5485292..2bd129300adc5ffb609df1e46c951630d682b883 100644
--- a/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet_loop.cpp
+++ b/hpvm/test/dnn_benchmarks/benchmarks/vgg16_imagenet/vgg16_imagenet_loop.cpp
@@ -11,7 +11,7 @@
 #include <config.h>
 
 void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -19,7 +19,7 @@ void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -27,7 +27,7 @@ void var_1_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_2_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -35,7 +35,7 @@ void var_2_node(void *t1, size_t bytes_t1) {
 }
 
 void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -43,7 +43,7 @@ void var_3_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -51,7 +51,7 @@ void var_4_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_5_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -59,7 +59,7 @@ void var_5_node(void *t1, size_t bytes_t1) {
 }
 
 void var_6_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -67,7 +67,7 @@ void var_6_node(void *t1, size_t bytes_t1) {
 }
 
 void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -75,7 +75,7 @@ void var_7_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -83,7 +83,7 @@ void var_8_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_9_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -91,7 +91,7 @@ void var_9_node(void *t1, size_t bytes_t1) {
 }
 
 void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -99,7 +99,7 @@ void var_10_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -107,7 +107,7 @@ void var_11_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_12_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -115,7 +115,7 @@ void var_12_node(void *t1, size_t bytes_t1) {
 }
 
 void var_13_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -123,7 +123,7 @@ void var_13_node(void *t1, size_t bytes_t1) {
 }
 
 void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -131,7 +131,7 @@ void var_14_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -139,7 +139,7 @@ void var_15_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_16_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -147,7 +147,7 @@ void var_16_node(void *t1, size_t bytes_t1) {
 }
 
 void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -155,7 +155,7 @@ void var_17_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -163,7 +163,7 @@ void var_18_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_19_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -171,7 +171,7 @@ void var_19_node(void *t1, size_t bytes_t1) {
 }
 
 void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -179,7 +179,7 @@ void var_20_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -187,7 +187,7 @@ void var_21_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_22_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -195,7 +195,7 @@ void var_22_node(void *t1, size_t bytes_t1) {
 }
 
 void var_23_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -203,7 +203,7 @@ void var_23_node(void *t1, size_t bytes_t1) {
 }
 
 void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -211,7 +211,7 @@ void var_24_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -219,7 +219,7 @@ void var_25_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_26_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -227,7 +227,7 @@ void var_26_node(void *t1, size_t bytes_t1) {
 }
 
 void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -235,7 +235,7 @@ void var_27_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -243,7 +243,7 @@ void var_28_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_29_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -251,7 +251,7 @@ void var_29_node(void *t1, size_t bytes_t1) {
 }
 
 void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -259,7 +259,7 @@ void var_30_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -267,7 +267,7 @@ void var_31_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_32_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -275,7 +275,7 @@ void var_32_node(void *t1, size_t bytes_t1) {
 }
 
 void var_33_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -283,7 +283,7 @@ void var_33_node(void *t1, size_t bytes_t1) {
 }
 
 void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -291,7 +291,7 @@ void var_34_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -299,7 +299,7 @@ void var_35_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_36_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -307,7 +307,7 @@ void var_36_node(void *t1, size_t bytes_t1) {
 }
 
 void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -315,7 +315,7 @@ void var_37_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -323,7 +323,7 @@ void var_38_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_39_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -331,7 +331,7 @@ void var_39_node(void *t1, size_t bytes_t1) {
 }
 
 void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_convolution(t1, t2, 1, 1, 1, 1);
@@ -339,7 +339,7 @@ void var_40_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -347,7 +347,7 @@ void var_41_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_42_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -355,7 +355,7 @@ void var_42_node(void *t1, size_t bytes_t1) {
 }
 
 void var_43_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
@@ -363,7 +363,7 @@ void var_43_node(void *t1, size_t bytes_t1) {
 }
 
 void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -371,7 +371,7 @@ void var_44_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -379,7 +379,7 @@ void var_45_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_46_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -387,7 +387,7 @@ void var_46_node(void *t1, size_t bytes_t1) {
 }
 
 void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -395,7 +395,7 @@ void var_47_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -403,7 +403,7 @@ void var_48_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_49_node(void *t1, size_t bytes_t1) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(1, t1, 0);
 
   void *r = __hpvm__tensor_relu(t1);
@@ -411,7 +411,7 @@ void var_49_node(void *t1, size_t bytes_t1) {
 }
 
 void var_50_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_mul(t1, t2);
@@ -419,7 +419,7 @@ void var_50_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
 }
 
 void var_51_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
-  __hpvm__hint(hpvm::PROMISE_TARGET);
+  __hpvm__hint(hpvm::TENSOR_TARGET);
   __hpvm__attributes(2, t1, t2, 0);
 
   void *r = __hpvm__tensor_add(t1, t2);
@@ -877,7 +877,8 @@ typedef struct __attribute__((__packed__)) {
 
 int main() {
 
-  std::string dir_prefix = std::string("/home/hsharif3/vgg16_imagenet_tune/");
+  std::string dir_prefix =
+      std::string(MODEL_PARAMS_DIR) + "/vgg16_imagenet/";
   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");
@@ -1068,7 +1069,7 @@ int main() {
 
       __hpvm__wait(dfg);
 
-      void *result = static_cast<RootIn *>(args)->input;
+      void *result = static_cast<RootIn *>(args)->r.tensor;
       hpvm_request_tensor(result, 0);
 
       llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);