diff --git a/tests/ExecuteNetwork/ExecuteNetwork.cpp b/tests/ExecuteNetwork/ExecuteNetwork.cpp
index 02a21c3..e9ebd0d 100644
--- a/tests/ExecuteNetwork/ExecuteNetwork.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetwork.cpp
@@ -1,1077 +1,91 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
-#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
 #include "ExecuteNetworkProgramOptions.hpp"
-#include <armnn/IAsyncExecutionCallback.hpp>
-#include <AsyncExecutionCallback.hpp>
-
+#include "ArmNNExecutor.hpp"
+#if defined(ARMNN_TFLITE_DELEGATE)
+#include "TfliteExecutor.hpp"
+#endif
 #include <armnn/Logging.hpp>
-#include <armnnUtils/Filesystem.hpp>
-#include <armnnUtils/TContainer.hpp>
-#include <ProfilingOptionsConverter.hpp>
-#include <InferenceTest.hpp>
 
-#if defined(ARMNN_SERIALIZER)
-#include "armnnDeserializer/IDeserializer.hpp"
-#endif
-#if defined(ARMNN_TF_LITE_PARSER)
-#include "armnnTfLiteParser/ITfLiteParser.hpp"
-#endif
-#if defined(ARMNN_ONNX_PARSER)
-#include "armnnOnnxParser/IOnnxParser.hpp"
-#endif
-#if defined(ARMNN_TFLITE_DELEGATE)
-#include <armnn_delegate.hpp>
-#include <DelegateOptions.hpp>
 
-#include <tensorflow/lite/builtin_ops.h>
-#include <tensorflow/lite/c/builtin_op_data.h>
-#include <tensorflow/lite/c/common.h>
-#include <tensorflow/lite/optional_debug_tools.h>
-#include <tensorflow/lite/kernels/builtin_op_kernels.h>
-#include <tensorflow/lite/interpreter.h>
-#include <tensorflow/lite/kernels/register.h>
-#endif
-
-#include <future>
-
-/**
- * Given a measured duration and a threshold time tell the user whether we succeeded or not.
- *
- * @param duration the measured inference duration.
- * @param thresholdTime the threshold time in milliseconds.
- * @return false if the measured time exceeded the threshold.
- */
-bool CheckInferenceTimeThreshold(const std::chrono::duration<double, std::milli>& duration,
-                                 const double& thresholdTime)
+std::unique_ptr<IExecutor> BuildExecutor(ProgramOptions& programOptions)
 {
-    ARMNN_LOG(info) << "Inference time: " << std::setprecision(2)
-                    << std::fixed << duration.count() << " ms\n";
-    // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
-    if (thresholdTime != 0.0)
+    if (programOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate ||
+        programOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter)
     {
-        ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2)
-                        << std::fixed << thresholdTime << " ms";
-        auto thresholdMinusInference = thresholdTime - duration.count();
-        ARMNN_LOG(info) << "Threshold time - Inference time: " << std::setprecision(2)
-                        << std::fixed << thresholdMinusInference << " ms" << "\n";
-       if (thresholdMinusInference < 0)
-        {
-            std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
-            ARMNN_LOG(fatal) << errorMessage;
-            return false;
-        }
-    }
-    return true;
-}
-
 #if defined(ARMNN_TFLITE_DELEGATE)
-int TfLiteDelegateMainImpl(const ExecuteNetworkParams& params, const armnn::IRuntime::CreationOptions runtimeOptions)
-{
-    // Build model and corresponding interpreter
-    using namespace tflite;
-
-    std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile(params.m_ModelPath.c_str());
-
-    auto tfLiteInterpreter =  std::make_unique<Interpreter>();
-    tflite::ops::builtin::BuiltinOpResolver resolver;
-
-    tflite::InterpreterBuilder builder(*model, resolver);
-    builder(&tfLiteInterpreter);
-    tfLiteInterpreter->AllocateTensors();
-
-    int status = 0;
-
-    // Create & populate Armnn Delegate, then register it to TfLiteInterpreter
-    if (params.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate)
-    {
-        // Create the Armnn Delegate
-        // Populate a DelegateOptions from the ExecuteNetworkParams.
-        armnnDelegate::DelegateOptions delegateOptions = params.ToDelegateOptions();
-        delegateOptions.SetExternalProfilingParams(
-            arm::pipe::ConvertExternalProfilingOptions(runtimeOptions.m_ProfilingOptions));
-
-        std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
-                theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
-                                 armnnDelegate::TfLiteArmnnDelegateDelete);
-        // Register armnn_delegate to TfLiteInterpreter
-        status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate));
-        if (status != kTfLiteOk)
-        {
-            ARMNN_LOG(fatal) << "Could not register ArmNN TfLite Delegate to TfLiteInterpreter!";
-            return EXIT_FAILURE;
-        }
+        return std::make_unique<TfLiteExecutor>(programOptions.m_ExNetParams);
+#else
+        ARMNN_LOG(fatal) << "Not built with Arm NN Tensorflow-Lite delegate support.";
+            return nullptr;
+#endif
     }
     else
     {
-        std::cout << "Running on TfLite without ArmNN delegate\n";
+        return std::make_unique<ArmNNExecutor>(programOptions.m_ExNetParams, programOptions.m_RuntimeOptions);
     }
-
-    const size_t numInputs = params.m_InputNames.size();
-    // Populate input tensor of interpreter
-    for(unsigned int inputIndex = 0; inputIndex < numInputs; ++inputIndex)
-    {
-        // Load (or generate) input data for inference
-        armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ? armnn::EmptyOptional() :
-            armnn::MakeOptional<std::string>(params.m_InputTensorDataFilePaths[inputIndex]);
-
-        int input = tfLiteInterpreter->inputs()[inputIndex];
-        TfLiteIntArray* inputDims = tfLiteInterpreter->tensor(input)->dims;
-
-        unsigned int inputSize = 1;
-        if (params.m_InputTensorShapes.size() > 0)
-        {
-            inputSize = params.m_InputTensorShapes[inputIndex]->GetNumElements();
-        }
-        else
-        {
-            for (unsigned int dim = 0; dim < static_cast<unsigned int>(inputDims->size); ++dim)
-            {
-                inputSize *= inputDims->data[dim];
-            }
-        }
-
-        if (params.m_InputTypes[inputIndex].compare("float") == 0)
-        {
-            auto inputData = tfLiteInterpreter->typed_tensor<float>(input);
-
-            if(inputData == NULL)
-            {
-                ARMNN_LOG(fatal) << "Input tensor is null, input type: "
-                                    "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect.";
-                return EXIT_FAILURE;
-            }
-
-            std::vector<float> tensorData;
-            PopulateTensorWithDataGeneric<float>(tensorData,
-                                                 inputSize,
-                                                 dataFile,
-                                                 [](const std::string& s)
-                                                 { return std::stof(s); });
-
-            std::copy(tensorData.begin(), tensorData.end(), inputData);
-        }
-        else if (params.m_InputTypes[inputIndex].compare("qsymms8") == 0 ||
-                 params.m_InputTypes[inputIndex].compare("qasymms8") == 0)
-        {
-            auto inputData = tfLiteInterpreter->typed_tensor<int8_t>(input);
-
-            if(inputData == NULL)
-            {
-                ARMNN_LOG(fatal) << "Input tensor is null, input type: "
-                                    "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect.";
-                return EXIT_FAILURE;
-            }
-
-            std::vector<int8_t> tensorData;
-            PopulateTensorWithDataGeneric<int8_t>(tensorData,
-                                                  inputSize,
-                                                  dataFile,
-                                                  [](const std::string& s)
-                                                  { return armnn::numeric_cast<int8_t>(std::stoi(s)); });
-
-            std::copy(tensorData.begin(), tensorData.end(), inputData);
-        }
-        else if (params.m_InputTypes[inputIndex].compare("int") == 0)
-        {
-            auto inputData = tfLiteInterpreter->typed_tensor<int32_t>(input);
-
-            if(inputData == NULL)
-            {
-                ARMNN_LOG(fatal) << "Input tensor is null, input type: "
-                                    "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect.";
-                return EXIT_FAILURE;
-            }
-
-            std::vector<int32_t> tensorData;
-            PopulateTensorWithDataGeneric<int32_t>(tensorData,
-                                                   inputSize,
-                                                   dataFile,
-                                                   [](const std::string& s)
-                                                   { return std::stoi(s); });
-
-            std::copy(tensorData.begin(), tensorData.end(), inputData);
-        }
-        else if (params.m_InputTypes[inputIndex].compare("qasymm8") == 0 ||
-                 params.m_InputTypes[inputIndex].compare("qasymmu8") == 0)
-        {
-            auto inputData = tfLiteInterpreter->typed_tensor<uint8_t>(input);
-
-            if(inputData == NULL)
-            {
-                ARMNN_LOG(fatal) << "Input tensor is null, input type: "
-                                    "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect.";
-                return EXIT_FAILURE;
-            }
-
-            std::vector<uint8_t> tensorData;
-            PopulateTensorWithDataGeneric<uint8_t>(tensorData,
-                                                   inputSize,
-                                                   dataFile,
-                                                   [](const std::string& s)
-                                                   { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
-
-            std::copy(tensorData.begin(), tensorData.end(), inputData);
-        }
-        else
-        {
-            ARMNN_LOG(fatal) << "Unsupported input tensor data type \"" << params.m_InputTypes[inputIndex] << "\". ";
-            return EXIT_FAILURE;
-        }
-    }
-
-    // Run inference, print the output of the inference
-    for (size_t x = 0; x < params.m_Iterations; x++)
-    {
-        // Start timer to record inference time in milliseconds.
-        const auto start_time = armnn::GetTimeNow();
-        // Run the inference
-        status = tfLiteInterpreter->Invoke();
-        const auto duration = armnn::GetTimeDuration(start_time);
-
-        // The TFLite interpreter's outputs might be in a different order than the user inputted output names.
-        std::map<unsigned int, int> paramToTfliteOutputIndex;
-        for (unsigned int paramIndex = 0; paramIndex < params.m_OutputNames.size(); ++paramIndex)
-        {
-            paramToTfliteOutputIndex[paramIndex] = -1;
-            for (unsigned int tfLiteIndex = 0; tfLiteIndex < tfLiteInterpreter->outputs().size(); ++tfLiteIndex)
-            {
-                if (params.m_OutputNames[paramIndex] == tfLiteInterpreter->GetOutputName(tfLiteIndex))
-                {
-                    paramToTfliteOutputIndex[paramIndex] = tfLiteIndex;
-                }
-            }
-        }
-
-        // Print out the output
-        for (unsigned int paramOutputIndex = 0; paramOutputIndex < params.m_OutputNames.size(); ++paramOutputIndex)
-        {
-            int outputIndex = paramToTfliteOutputIndex[paramOutputIndex];
-            if (outputIndex == -1)
-            {
-                std::cout << fmt::format("Output name: {} doesn't exist.", params.m_OutputNames[paramOutputIndex]) <<
-                std::endl;
-                continue;
-            }
-            auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex];
-            TfLiteIntArray* outputDims = tfLiteInterpreter->tensor(tfLiteDelegateOutputId)->dims;
-            // If we've been asked to write to a file then set a file output stream. Otherwise use stdout.
-            FILE* outputTensorFile = stdout;
-            if (!params.m_OutputTensorFiles.empty())
-            {
-                outputTensorFile = fopen(params.m_OutputTensorFiles[outputIndex].c_str(), "w");
-                if (outputTensorFile == NULL)
-                {
-                    ARMNN_LOG(fatal) << "Specified output tensor file, \"" <<
-                                     params.m_OutputTensorFiles[outputIndex] <<
-                                     "\", cannot be created. Defaulting to stdout. " <<
-                                     "Error was: " << std::strerror(errno);
-                    outputTensorFile = stdout;
-                }
-                else
-                {
-                    ARMNN_LOG(info) << "Writing output " << outputIndex << "' of iteration: " << x+1 << " to file: '"
-                                    << params.m_OutputTensorFiles[outputIndex] << "'";
-                }
-            }
-            long outputSize = 1;
-            for (unsigned int dim = 0; dim < static_cast<unsigned int>(outputDims->size); ++dim)
-            {
-                outputSize *=  outputDims->data[dim];
-            }
-
-            std::cout << tfLiteInterpreter->GetOutputName(outputIndex) << ": ";
-            if (params.m_OutputTypes[paramOutputIndex].compare("float") == 0)
-            {
-                auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
-                if(tfLiteDelageOutputData == NULL)
-                {
-                    ARMNN_LOG(fatal) << "Output tensor is null, output type: "
-                                        "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect.";
-                    return EXIT_FAILURE;
-                }
-
-                if (!params.m_DontPrintOutputs)
-                {
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%f ", tfLiteDelageOutputData[i]);
-                    }
-                }
-            }
-            else if (params.m_OutputTypes[paramOutputIndex].compare("int") == 0)
-            {
-                auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteDelegateOutputId);
-                if(tfLiteDelageOutputData == NULL)
-                {
-                    ARMNN_LOG(fatal) << "Output tensor is null, output type: "
-                                        "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect.";
-                    return EXIT_FAILURE;
-                }
-
-                if (!params.m_DontPrintOutputs)
-                {
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]);
-                    }
-                }
-            }
-            else if (params.m_OutputTypes[paramOutputIndex].compare("qsymms8") == 0 ||
-                     params.m_OutputTypes[paramOutputIndex].compare("qasymms8") == 0)
-            {
-                auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<int8_t>(tfLiteDelegateOutputId);
-                if(tfLiteDelageOutputData == NULL)
-                {
-                    ARMNN_LOG(fatal) << "Output tensor is null, output type: "
-                                        "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect.";
-                    return EXIT_FAILURE;
-                }
-
-                if (!params.m_DontPrintOutputs)
-                {
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]);
-                    }
-                }
-            }
-            else if (params.m_OutputTypes[paramOutputIndex].compare("qasymm8") == 0 ||
-                     params.m_OutputTypes[paramOutputIndex].compare("qasymmu8") == 0)
-            {
-                auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<uint8_t>(tfLiteDelegateOutputId);
-                if(tfLiteDelageOutputData == NULL)
-                {
-                    ARMNN_LOG(fatal) << "Output tensor is null, output type: "
-                                        "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect.";
-                    return EXIT_FAILURE;
-                }
-
-                if (!params.m_DontPrintOutputs)
-                {
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%u ", tfLiteDelageOutputData[i]);
-                    }
-                }
-            }
-            else
-            {
-                ARMNN_LOG(fatal) << "Output tensor is null, output type: "
-                                    "\"" << params.m_OutputTypes[paramOutputIndex] <<
-                                 "\" may be incorrect. Output type can be specified with -z argument";
-                return EXIT_FAILURE;
-            }
-            std::cout << std::endl;
-        }
-        CheckInferenceTimeThreshold(duration, params.m_ThresholdTime);
-    }
-
-    return status;
-}
-#endif
-template<typename TParser, typename TDataType>
-int MainImpl(const ExecuteNetworkParams& params,
-             const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
-{
-    using namespace std::chrono;
-
-    std::vector<std::vector<armnnUtils::TContainer>> inputs;
-    std::vector<std::vector<armnnUtils::TContainer>> outputs;
-
-    try
-    {
-        // Creates an InferenceModel, which will parse the model and load it into an IRuntime.
-        typename InferenceModel<TParser, TDataType>::Params inferenceModelParams;
-        inferenceModelParams.m_ModelPath                      = params.m_ModelPath;
-        inferenceModelParams.m_AllowExpandedDims              = params.m_AllowExpandedDims;
-        inferenceModelParams.m_IsModelBinary                  = params.m_IsModelBinary;
-        inferenceModelParams.m_ComputeDevices                 = params.m_ComputeDevices;
-        inferenceModelParams.m_DynamicBackendsPath            = params.m_DynamicBackendsPath;
-        inferenceModelParams.m_PrintIntermediateLayers        = params.m_PrintIntermediate;
-        inferenceModelParams.m_VisualizePostOptimizationModel = params.m_EnableLayerDetails;
-        inferenceModelParams.m_ParseUnsupported               = params.m_ParseUnsupported;
-        inferenceModelParams.m_InferOutputShape               = params.m_InferOutputShape;
-        inferenceModelParams.m_EnableFastMath                 = params.m_EnableFastMath;
-        inferenceModelParams.m_SaveCachedNetwork              = params.m_SaveCachedNetwork;
-        inferenceModelParams.m_CachedNetworkFilePath          = params.m_CachedNetworkFilePath;
-        inferenceModelParams.m_NumberOfThreads                = params.m_NumberOfThreads;
-        inferenceModelParams.m_MLGOTuningFilePath             = params.m_MLGOTuningFilePath;
-        inferenceModelParams.m_AsyncEnabled                   = params.m_Concurrent;
-        inferenceModelParams.m_ThreadPoolSize                 = params.m_ThreadPoolSize;
-        inferenceModelParams.m_OutputDetailsToStdOut          = params.m_OutputDetailsToStdOut;
-        inferenceModelParams.m_OutputDetailsOnlyToStdOut      = params.m_OutputDetailsOnlyToStdOut;
-        inferenceModelParams.m_ImportInputsIfAligned          = params.m_ImportInputsIfAligned;
-
-        for(const std::string& inputName: params.m_InputNames)
-        {
-            inferenceModelParams.m_InputBindings.push_back(inputName);
-        }
-
-        for(unsigned int i = 0; i < params.m_InputTensorShapes.size(); ++i)
-        {
-            inferenceModelParams.m_InputShapes.push_back(*params.m_InputTensorShapes[i]);
-        }
-
-        for(const std::string& outputName: params.m_OutputNames)
-        {
-            inferenceModelParams.m_OutputBindings.push_back(outputName);
-        }
-
-        inferenceModelParams.m_SubgraphId          = params.m_SubgraphId;
-        inferenceModelParams.m_EnableFp16TurboMode = params.m_EnableFp16TurboMode;
-        inferenceModelParams.m_EnableBf16TurboMode = params.m_EnableBf16TurboMode;
-
-        InferenceModel<TParser, TDataType> model(inferenceModelParams,
-                                                 params.m_EnableProfiling,
-                                                 params.m_DynamicBackendsPath,
-                                                 runtime);
-
-        const size_t numInputs = inferenceModelParams.m_InputBindings.size();
-
-        armnn::Optional<QuantizationParams> qParams = params.m_QuantizeInput ?
-                                                      armnn::MakeOptional<QuantizationParams>(
-                                                          model.GetInputQuantizationParams()) :
-                                                      armnn::EmptyOptional();
-
-        if (params.m_InputTensorDataFilePaths.size() > numInputs)
-        {
-            ARMNN_LOG(info) << "Given network has " << numInputs << " input/s. One input-tensor-data file is required "
-                            << "for each input. The user provided "
-                            << params.m_InputTensorDataFilePaths.size()
-                            << " input-tensor-data file/s which will be used to fill the input/s.\n";
-        }
-
-        const size_t numOutputs = inferenceModelParams.m_OutputBindings.size();
-
-        // The user is allowed to specify the data type of each output tensor. It is used here to construct the
-        // result tensors for each iteration. It is possible for the user to specify a type that does not match
-        // the data type of the corresponding model output. It may not make sense, but it is historically allowed.
-        // The potential problem here is a buffer overrun when a larger data type is written into the space for a
-        // smaller one. Issue a warning to highlight the potential problem.
-        for (unsigned int outputIdx = 0; outputIdx < model.GetOutputBindingInfos().size(); ++outputIdx)
-        {
-            armnn::DataType type = model.GetOutputBindingInfo(outputIdx).second.GetDataType();
-            switch (type)
-            {
-                // --output-type only supports float, int,  qasymms8 or qasymmu8.
-                case armnn::DataType::Float32:
-                    if (params.m_OutputTypes[outputIdx].compare("float") != 0)
-                    {
-                        ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type Float32. The "
-                                           << "corresponding --output-type is " << params.m_OutputTypes[outputIdx] <<
-                                           ". This may cause unexpected problems or random failures.";
-                    }
-                    break;
-                case armnn::DataType::QAsymmU8:
-                    if (params.m_OutputTypes[outputIdx].compare("qasymmu8") != 0)
-                    {
-                        ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type QAsymmU8. The "
-                                           << "corresponding --output-type is " << params.m_OutputTypes[outputIdx] <<
-                                           ". This may cause unexpected problems or random failures.";
-                    }
-                    break;
-                case armnn::DataType::Signed32:
-                    if (params.m_OutputTypes[outputIdx].compare("int") != 0)
-                    {
-                        ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type Signed32. The "
-                                           << "corresponding --output-type is " << params.m_OutputTypes[outputIdx] <<
-                                           ". This may cause unexpected problems or random failures.";
-                    }
-                    break;
-                case armnn::DataType::QAsymmS8:
-                    if (params.m_OutputTypes[outputIdx].compare("qasymms8") != 0)
-                    {
-                        ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type QAsymmS8. The "
-                                           << "corresponding --output-type is " << params.m_OutputTypes[outputIdx] <<
-                                           ". This may cause unexpected problems or random failures.";
-                    }
-                    break;
-                default:
-                    break;
-            }
-        }
-
-        if (!params.m_ReuseBuffers)
-        {
-            for (unsigned int j = 0; j < params.m_Iterations; ++j)
-            {
-                std::vector<armnnUtils::TContainer> inputDataContainers;
-                for (unsigned int i = 0; i < numInputs; ++i)
-                {
-                    // If there are fewer input files given than required for the execution of
-                    // params.m_Iterations we simply start with the first input file again
-                    size_t inputFileIndex = j * numInputs + i;
-                    if (!params.m_InputTensorDataFilePaths.empty())
-                    {
-                        inputFileIndex = inputFileIndex % params.m_InputTensorDataFilePaths.size();
-                    }
-
-                    armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ?
-                                                            armnn::EmptyOptional() :
-                                                            armnn::MakeOptional<std::string>(
-                                                                    params.m_InputTensorDataFilePaths.at(
-                                                                            inputFileIndex));
-
-                    unsigned int numElements = model.GetInputSize(i);
-                    if (params.m_InputTensorShapes.size() > i && params.m_InputTensorShapes[i])
-                    {
-                        // If the user has provided a tensor shape for the current input,
-                        // override numElements
-                        numElements = params.m_InputTensorShapes[i]->GetNumElements();
-                    }
-
-                    armnnUtils::TContainer tensorData;
-                    PopulateTensorWithData(tensorData,
-                                           numElements,
-                                           params.m_InputTypes[i],
-                                           qParams,
-                                           dataFile);
-
-                    inputDataContainers.push_back(tensorData);
-                }
-                inputs.push_back(inputDataContainers);
-            }
-
-            for (unsigned int j = 0; j < params.m_Iterations; ++j)
-            {
-                std::vector<armnnUtils::TContainer> outputDataContainers;
-                for (unsigned int i = 0; i < numOutputs; ++i)
-                {
-                    if (params.m_OutputTypes[i].compare("float") == 0)
-                    {
-                        outputDataContainers.push_back(std::vector<float>(model.GetOutputSize(i)));
-                    }
-                    else if (params.m_OutputTypes[i].compare("int") == 0)
-                    {
-                        outputDataContainers.push_back(std::vector<int>(model.GetOutputSize(i)));
-                    }
-                    else if (params.m_OutputTypes[i].compare("qasymm8") == 0 ||
-                               params.m_OutputTypes[i].compare("qasymmu8") == 0)
-                    {
-                        outputDataContainers.push_back(std::vector<uint8_t>(model.GetOutputSize(i)));
-                    }
-                    else if (params.m_OutputTypes[i].compare("qasymms8") == 0)
-                    {
-                        outputDataContainers.push_back(std::vector<int8_t>(model.GetOutputSize(i)));
-                    }
-                    else
-                    {
-                        ARMNN_LOG(fatal) << "Unsupported tensor data type \"" << params.m_OutputTypes[i] << "\". ";
-                        return EXIT_FAILURE;
-                    }
-                }
-                outputs.push_back(outputDataContainers);
-            }
-        }
-        if (params.m_Iterations > 1)
-        {
-            std::stringstream msg;
-            msg << "Network will be executed " << params.m_Iterations;
-            if (params.m_Concurrent)
-            {
-                msg << " times in an asynchronous manner. ";
-            }
-            else
-            {
-                msg << " times successively. ";
-            }
-            msg << "The input-tensor-data files will be reused recursively if the user didn't provide enough to "
-                   "cover each execution.";
-            ARMNN_LOG(info) << msg.str();
-        }
-
-        // Synchronous execution
-        if (!params.m_Concurrent && !params.m_ReuseBuffers)
-        {
-            for (size_t x = 0; x < params.m_Iterations; x++)
-            {
-                // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
-                auto inference_duration = model.Run(inputs[x], outputs[x]);
-
-                if (params.m_GenerateTensorData)
-                {
-                    ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-                }
-                if (params.m_DontPrintOutputs)
-                {
-                    ARMNN_LOG(info) << "Printing outputs to console is disabled.";
-                }
-
-                // Print output tensors
-                const auto& infosOut = model.GetOutputBindingInfos();
-                for (size_t i = 0; i < numOutputs; i++)
-                {
-                    const armnn::TensorInfo& infoOut = infosOut[i].second;
-
-                    // We've made sure before that the number of output files either equals numOutputs, in which
-                    // case we override those files when processing the results of each iteration (only the result
-                    // of the last iteration will be stored), or there are enough
-                    // output files for each output of each iteration.
-                    size_t outputFileIndex = x * numOutputs + i;
-                    if (!params.m_OutputTensorFiles.empty())
-                    {
-                        outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
-                        ARMNN_LOG(info) << "Writing output " << i << " named: '"
-                                        << inferenceModelParams.m_OutputBindings[i]
-                                        << "' of iteration: " << x+1 << " to file: '"
-                                        << params.m_OutputTensorFiles[outputFileIndex] << "'";
-                    }
-                    auto outputTensorFile = params.m_OutputTensorFiles.empty()
-                                            ? ""
-                                            : params.m_OutputTensorFiles[outputFileIndex];
-
-                    TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
-                                          infoOut,
-                                          outputTensorFile,
-                                          params.m_DequantizeOutput,
-                                          !params.m_DontPrintOutputs);
-                    mapbox::util::apply_visitor(printer, outputs[x][i]);
-                }
-
-                ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
-                                << std::fixed << inference_duration.count() << " ms\n";
-
-                // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
-                if (params.m_ThresholdTime != 0.0)
-                {
-                    ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2)
-                                    << std::fixed << params.m_ThresholdTime << " ms";
-                    auto thresholdMinusInference = params.m_ThresholdTime - inference_duration.count();
-                    ARMNN_LOG(info) << "Threshold time - Inference time: " << std::setprecision(2)
-                                    << std::fixed << thresholdMinusInference << " ms" << "\n";
-
-                    if (thresholdMinusInference < 0)
-                    {
-                        std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
-                        ARMNN_LOG(fatal) << errorMessage;
-                    }
-                }
-            }
-        }
-        // Synchronous Execution using a single buffer for input and output data
-        else if(!params.m_Concurrent)
-        {
-            std::vector<armnnUtils::TContainer> input;
-            std::vector<armnnUtils::TContainer> output;
-
-            for (unsigned int i = 0; i < numInputs; ++i)
-            {
-                // If there are fewer input files given than required for the execution of
-                // params.m_Iterations we simply start with the first input file again
-                size_t inputFileIndex = numInputs + i;
-                if (!params.m_InputTensorDataFilePaths.empty())
-                {
-                    inputFileIndex = inputFileIndex % params.m_InputTensorDataFilePaths.size();
-                }
-
-                armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ?
-                                                        armnn::EmptyOptional() :
-                                                        armnn::MakeOptional<std::string>(
-                                                                params.m_InputTensorDataFilePaths.at(
-                                                                        inputFileIndex));
-
-                unsigned int numElements = model.GetInputSize(i);
-                if (params.m_InputTensorShapes.size() > i && params.m_InputTensorShapes[i])
-                {
-                    // If the user has provided a tensor shape for the current input,
-                    // override numElements
-                    numElements = params.m_InputTensorShapes[i]->GetNumElements();
-                }
-
-                armnnUtils::TContainer tensorData;
-                PopulateTensorWithData(tensorData,
-                                       numElements,
-                                       params.m_InputTypes[i],
-                                       qParams,
-                                       dataFile);
-
-                input.push_back(tensorData);
-            }
-
-            for (unsigned int i = 0; i < numOutputs; ++i)
-            {
-                if (params.m_OutputTypes[i].compare("float") == 0)
-                {
-                    output.push_back(std::vector<float>(model.GetOutputSize(i)));
-                } else if (params.m_OutputTypes[i].compare("int") == 0) {
-                    output.push_back(std::vector<int>(model.GetOutputSize(i)));
-                } else if (params.m_OutputTypes[i].compare("qasymm8") == 0 ||
-                           params.m_OutputTypes[i].compare("qasymmu8") == 0)
-                {
-                    output.push_back(std::vector<uint8_t>(model.GetOutputSize(i)));
-                } else if (params.m_OutputTypes[i].compare("qasymms8") == 0)
-                {
-                    output.push_back(std::vector<int8_t>(model.GetOutputSize(i)));
-                } else {
-                    ARMNN_LOG(fatal) << "Unsupported tensor data type \"" << params.m_OutputTypes[i] << "\". ";
-                    return EXIT_FAILURE;
-                }
-            }
-
-            std::vector<std::chrono::duration<double, std::milli>> timings;
-            timings.reserve(params.m_Iterations);
-            for (size_t x = 0; x < params.m_Iterations; x++)
-            {
-                // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
-                auto inference_duration = model.Run(input, output);
-                timings.push_back(inference_duration);
-            }
-
-            if (params.m_GenerateTensorData)
-            {
-                ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-            }
-            if (params.m_DontPrintOutputs)
-            {
-                ARMNN_LOG(info) << "Printing outputs to console is disabled.";
-            }
-
-            // Print output. This only needs to happen once as input is the same for each iteration.
-            const auto &infosOut = model.GetOutputBindingInfos();
-            for (size_t i = 0; i < numOutputs; i++)
-            {
-                const armnn::TensorInfo &infoOut = infosOut[i].second;
-
-                // We've made sure before that the number of output files either equals numOutputs, in which
-                // case we override those files when processing the results of each iteration (only the result
-                // of the last iteration will be stored), or there are enough
-                // output files for each output of each iteration.
-                size_t outputFileIndex = numOutputs + i;
-                if (!params.m_OutputTensorFiles.empty())
-                {
-                    outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
-                    ARMNN_LOG(info) << "Writing output " << i << " named: '"
-                                    << inferenceModelParams.m_OutputBindings[i] <<" to file: '"
-                                    << params.m_OutputTensorFiles[outputFileIndex] << "'";
-                }
-                auto outputTensorFile = params.m_OutputTensorFiles.empty()
-                                        ? ""
-                                        : params.m_OutputTensorFiles[outputFileIndex];
-
-                TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
-                                      infoOut,
-                                      outputTensorFile,
-                                      params.m_DequantizeOutput,
-                                      !params.m_DontPrintOutputs);
-                mapbox::util::apply_visitor(printer, output[i]);
-            }
-
-            for(auto inference: timings)
-            {
-
-                ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
-                                << std::fixed << inference.count() << " ms\n";
-
-                // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
-                if (params.m_ThresholdTime != 0.0)
-                {
-                    ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2)
-                                    << std::fixed << params.m_ThresholdTime << " ms";
-                    auto thresholdMinusInference = params.m_ThresholdTime - inference.count();
-                    ARMNN_LOG(info) << "Threshold time - Inference time: " << std::setprecision(2)
-                                    << std::fixed << thresholdMinusInference << " ms" << "\n";
-
-                    if (thresholdMinusInference < 0)
-                    {
-                        std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
-                        ARMNN_LOG(fatal) << errorMessage;
-                    }
-                }
-            }
-        }
-
-        // Asynchronous execution using the Arm NN thread pool
-        else if (params.m_ThreadPoolSize >= 1)
-        {
-            try
-            {
-                ARMNN_LOG(info) << "Asynchronous Execution with Arm NN thread pool...  \n";
-                armnn::AsyncCallbackManager callbackManager;
-                std::unordered_map<armnn::InferenceId, std::vector<armnnUtils::TContainer>&> inferenceOutputMap;
-
-                // Declare the latest and earliest inference times here to be used when calculating overall time
-                std::chrono::high_resolution_clock::time_point earliestStartTime;
-                std::chrono::high_resolution_clock::time_point latestEndTime =
-                    std::chrono::high_resolution_clock::now();
-
-                // For the asynchronous execution, we are adding a pool of working memory handles (1 per thread) in the
-                // LoadedNetwork with each scheduled inference having a specific priority
-                for (size_t i = 0; i < params.m_Iterations; ++i)
-                {
-                    std::shared_ptr<armnn::AsyncExecutionCallback> cb = callbackManager.GetNewCallback();
-                    inferenceOutputMap.insert({cb->GetInferenceId(), outputs[i]});
-                    model.RunAsync(inputs[i], outputs[i], cb);
-                }
-
-                // Check the results
-                unsigned int j = 0;
-                for (size_t iteration = 0; iteration < params.m_Iterations; ++iteration)
-                {
-                    auto cb = callbackManager.GetNotifiedCallback();
-
-                    // Get the results
-                    auto endTime = time_point_cast<std::chrono::milliseconds>(cb->GetEndTime());
-                    auto startTime = time_point_cast<std::chrono::milliseconds>(cb->GetStartTime());
-                    auto inferenceDuration = endTime - startTime;
-
-                    if (latestEndTime < cb->GetEndTime())
-                    {
-                        latestEndTime = cb->GetEndTime();
-                    }
-
-                    if (earliestStartTime.time_since_epoch().count() == 0)
-                    {
-                        earliestStartTime = cb->GetStartTime();
-                    }
-                    else if (earliestStartTime > cb->GetStartTime())
-                    {
-                        earliestStartTime = cb->GetStartTime();
-                    }
-
-                    if (params.m_GenerateTensorData)
-                    {
-                        ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-                    }
-                    if (params.m_DontPrintOutputs)
-                    {
-                        ARMNN_LOG(info) << "Printing outputs to console is disabled.";
-                    }
-
-                    // Print output tensors
-                    const auto& infosOut = model.GetOutputBindingInfos();
-                    for (size_t i = 0; i < numOutputs; i++)
-                    {
-                        // We've made sure before that the number of output files either equals numOutputs, in which
-                        // case we override those files when processing the results of each iteration (only the
-                        // result of the last iteration will be stored), or there are enough
-                        // output files for each output of each iteration.
-                        size_t outputFileIndex = iteration * numOutputs + i;
-                        if (!params.m_OutputTensorFiles.empty())
-                        {
-                            outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
-                            ARMNN_LOG(info) << "Writing output " << i << " named: '"
-                                            << inferenceModelParams.m_OutputBindings[i]
-                                            << "' of iteration: " << iteration+1 << " to file: '"
-                                            << params.m_OutputTensorFiles[outputFileIndex] << "'";
-                        }
-
-                        const armnn::TensorInfo& infoOut = infosOut[i].second;
-                        auto outputTensorFile = params.m_OutputTensorFiles.empty()
-                                                ? ""
-                                                : params.m_OutputTensorFiles[outputFileIndex];
-
-                        TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
-                                              infoOut,
-                                              outputTensorFile,
-                                              params.m_DequantizeOutput,
-                                              !params.m_DontPrintOutputs);
-                        mapbox::util::apply_visitor(printer, inferenceOutputMap.at(cb->GetInferenceId())[i]);
-                    }
-
-                    CheckInferenceTimeThreshold(inferenceDuration, params.m_ThresholdTime);
-                    ++j;
-                }
-                //print duration difference between overallStartTime and overallEndTime
-                auto overallEndTime = time_point_cast<std::chrono::milliseconds>(latestEndTime);
-                auto overallStartTime = time_point_cast<std::chrono::milliseconds>(earliestStartTime);
-                auto totalInferenceDuration = overallEndTime - overallStartTime;
-                ARMNN_LOG(info) << "\nOverall Inference time: " << std::setprecision(2)
-                                << std::fixed << totalInferenceDuration.count() << " ms\n";
-            }
-            catch (const armnn::Exception& e)
-            {
-                ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
-                return EXIT_FAILURE;
-            }
-        }
-        // Asynchronous execution using std::launch::async
-        else
-        {
-            try
-            {
-                ARMNN_LOG(info) << "Asynchronous Execution with std::launch:async...  \n";
-                std::vector<std::future<std::tuple<unsigned int,
-                    std::chrono::duration<double, std::milli>>>> inferenceResults;
-                inferenceResults.reserve(params.m_Iterations);
-
-                // Create WorkingMemHandles for each inference
-                std::vector<std::unique_ptr<armnn::experimental::IWorkingMemHandle>> workingMemHandles;
-                workingMemHandles.reserve(params.m_Iterations);
-                for (unsigned int i = 0; i < params.m_Iterations; ++i)
-                {
-                    workingMemHandles.push_back(model.CreateWorkingMemHandle());
-                }
-
-                // Run each inference in its own thread
-                // start a timer
-                const auto start_time = armnn::GetTimeNow();
-                for (unsigned int i = 0; i < params.m_Iterations; ++i)
-                {
-                    armnn::experimental::IWorkingMemHandle& workingMemHandleRef = *workingMemHandles[i].get();
-
-                    inferenceResults.push_back(std::async(
-                        std::launch::async, [&model, &workingMemHandleRef, &inputs, &outputs, i]() {
-                            return model.RunAsync(workingMemHandleRef, inputs[i], outputs[i], i);
-                        }
-                        ));
-                }
-
-                // Check the results
-                for (unsigned int j = 0; j < inferenceResults.size(); ++j)
-                {
-                    // Get the results
-                    auto inferenceResult = inferenceResults[j].get();
-                    auto inferenceDuration = std::get<1>(inferenceResult);
-                    auto inferenceID = std::get<0>(inferenceResult);
-
-                    if (params.m_GenerateTensorData)
-                    {
-                        ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-                    }
-                    if (params.m_DontPrintOutputs)
-                    {
-                        ARMNN_LOG(info) << "Printing outputs to console is disabled.";
-                    }
-
-                    // Print output tensors
-                    const auto& infosOut = model.GetOutputBindingInfos();
-                    for (size_t i = 0; i < numOutputs; i++)
-                    {
-                        // We've made sure before that the number of output files either equals numOutputs, in which
-                        // case we override those files when processing the results of each iteration (only the
-                        // result of the last iteration will be stored), or there are enough
-                        // output files for each output of each iteration.
-                        size_t outputFileIndex = j * numOutputs + i;
-                        if (!params.m_OutputTensorFiles.empty())
-                        {
-                            outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
-                            ARMNN_LOG(info) << "Writing output " << i << " named: '"
-                                            << inferenceModelParams.m_OutputBindings[i]
-                                            << "' of iteration: " << j+1 << " to file: '"
-                                            << params.m_OutputTensorFiles[outputFileIndex] << "'";
-                        }
-                        const armnn::TensorInfo& infoOut = infosOut[i].second;
-                        auto outputTensorFile = params.m_OutputTensorFiles.empty()
-                                                ? ""
-                                                : params.m_OutputTensorFiles[outputFileIndex];
-
-                        TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
-                                              infoOut,
-                                              outputTensorFile,
-                                              params.m_DequantizeOutput,
-                                              !params.m_DontPrintOutputs);
-                        mapbox::util::apply_visitor(printer, outputs[j][i]);
-                    }
-                    CheckInferenceTimeThreshold(inferenceDuration, params.m_ThresholdTime);
-                    ARMNN_LOG(info) << "Asynchronous Execution is finished for Inference ID: " << inferenceID << " \n";
-                }
-                // finish timer
-                const auto duration = armnn::GetTimeDuration(start_time);
-                ARMNN_LOG(info) << "\nOverall Inference time: " << std::setprecision(2)
-                                << std::fixed << duration.count() << " ms\n";
-            }
-            catch (const armnn::Exception& e)
-            {
-                ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
-                return EXIT_FAILURE;
-            }
-        }
-    }
-    catch (const armnn::Exception& e)
-    {
-        ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
-        return EXIT_FAILURE;
-    }
-
-    return EXIT_SUCCESS;
 }
 
 // MAIN
 int main(int argc, const char* argv[])
 {
     // Configures logging for both the ARMNN library and this test program.
-    #ifdef NDEBUG
+#ifdef NDEBUG
     armnn::LogSeverity level = armnn::LogSeverity::Info;
-    #else
+#else
     armnn::LogSeverity level = armnn::LogSeverity::Debug;
-    #endif
+#endif
     armnn::ConfigureLogging(true, true, level);
 
 
     // Get ExecuteNetwork parameters and runtime options from command line
     // This might throw an InvalidArgumentException if the user provided invalid inputs
-    ProgramOptions ProgramOptions;
-    try {
-        ProgramOptions.ParseOptions(argc, argv);
-    } catch (const std::exception &e){
+    ProgramOptions programOptions;
+    try
+    {
+        programOptions.ParseOptions(argc, argv);
+    }
+    catch (const std::exception& e)
+    {
         ARMNN_LOG(fatal) << e.what();
         return EXIT_FAILURE;
     }
 
-    if ((ProgramOptions.m_ExNetParams.m_OutputDetailsToStdOut ||
-         ProgramOptions.m_ExNetParams.m_OutputDetailsOnlyToStdOut)
-         && !ProgramOptions.m_ExNetParams.m_EnableProfiling)
+    std::vector<const void*> outputResults;
+
+    auto executor = BuildExecutor(programOptions);
+    if (!executor)
     {
-        ARMNN_LOG(fatal) << "You must enable profiling if you would like to output layer details";
         return EXIT_FAILURE;
     }
 
-    std::string modelFormat = ProgramOptions.m_ExNetParams.m_ModelFormat;
+    executor->PrintNetworkInfo();
+    outputResults = executor->Execute();
 
-    // Forward to implementation based on the parser type
-    if (modelFormat.find("armnn") != std::string::npos)
+    if (!programOptions.m_ExNetParams.m_ComparisonComputeDevices.empty() ||
+         programOptions.m_ExNetParams.m_CompareWithTflite)
     {
-    #if defined(ARMNN_SERIALIZER)
-        std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions));
-        return MainImpl<armnnDeserializer::IDeserializer, float>(ProgramOptions.m_ExNetParams, runtime);
-    #else
-        ARMNN_LOG(fatal) << "Not built with serialization support.";
-        return EXIT_FAILURE;
-    #endif
-    }
-    else if (modelFormat.find("onnx") != std::string::npos)
-    {
-    #if defined(ARMNN_ONNX_PARSER)
-        std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions));
-        return MainImpl<armnnOnnxParser::IOnnxParser, float>(ProgramOptions.m_ExNetParams, runtime);
-    #else
-        ARMNN_LOG(fatal) << "Not built with Onnx parser support.";
-        return EXIT_FAILURE;
-    #endif
-    }
-    else if(modelFormat.find("tflite") != std::string::npos)
-    {
-        if (ProgramOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteParser)
+        ExecuteNetworkParams comparisonParams = programOptions.m_ExNetParams;
+        comparisonParams.m_ComputeDevices = programOptions.m_ExNetParams.m_ComparisonComputeDevices;
+
+        if (programOptions.m_ExNetParams.m_CompareWithTflite)
         {
-            #if defined(ARMNN_TF_LITE_PARSER)
-                std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions));
-                return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(ProgramOptions.m_ExNetParams, runtime);
-            #else
-                ARMNN_LOG(fatal) << "Not built with Tensorflow-Lite parser support.";
-                return EXIT_FAILURE;
-            #endif
+            comparisonParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter;
         }
-        else if (ProgramOptions.m_ExNetParams.m_TfLiteExecutor ==
-                    ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate ||
-                ProgramOptions.m_ExNetParams.m_TfLiteExecutor ==
-                    ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter)
+
+        auto comparisonExecutor = BuildExecutor(programOptions);
+
+        if (!comparisonExecutor)
         {
-        #if defined(ARMNN_TFLITE_DELEGATE)
-            return TfLiteDelegateMainImpl(ProgramOptions.m_ExNetParams, ProgramOptions.m_RuntimeOptions);
-        #else
-            ARMNN_LOG(fatal) << "Not built with Arm NN Tensorflow-Lite delegate support.";
             return EXIT_FAILURE;
-        #endif
         }
-    }
-    else
-    {
-        ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
-                         << "'. Please include 'tflite' or 'onnx'";
-        return EXIT_FAILURE;
+
+        comparisonExecutor->PrintNetworkInfo();
+        comparisonExecutor->Execute();
+
+        comparisonExecutor->CompareAndPrintResult(outputResults);
     }
 }