Revert "IVGCVSW-6650 Refactor ExecuteNetwork"

This reverts commit 615e06f54a4c4139e81e289991ba4084aa2f69d3.

Reason for revert: <Breaking nightlies and tests>

Change-Id: I06a4a0119463188a653bb749033f78514645bd0c

13 files changed, 1616 insertions, 1678 deletions

diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 87a5b46024..4cb324f2c7 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -139,9 +139,6 @@ if (BUILD_ARMNN_SERIALIZER
         OR BUILD_ONNX_PARSER
         OR BUILD_ARMNN_TFLITE_DELEGATE)
     set(ExecuteNetwork_sources
-        ExecuteNetwork/IExecutor.hpp
-        ExecuteNetwork/ArmNNExecutor.cpp
-        ExecuteNetwork/ArmNNExecutor.hpp
         ExecuteNetwork/ExecuteNetwork.cpp
         ExecuteNetwork/ExecuteNetworkProgramOptions.cpp
         ExecuteNetwork/ExecuteNetworkProgramOptions.hpp
@@ -150,13 +147,6 @@ if (BUILD_ARMNN_SERIALIZER
         NetworkExecutionUtils/NetworkExecutionUtils.cpp
         NetworkExecutionUtils/NetworkExecutionUtils.hpp)
 
-    if(BUILD_ARMNN_TFLITE_DELEGATE)
-        set(ExecuteNetwork_sources
-            ${ExecuteNetwork_sources}
-            ExecuteNetwork/TfliteExecutor.cpp
-            ExecuteNetwork/TfliteExecutor.hpp)
-    endif()
-
     add_executable_ex(ExecuteNetwork ${ExecuteNetwork_sources})
     target_include_directories(ExecuteNetwork PRIVATE ../src/armnn)
     target_include_directories(ExecuteNetwork PRIVATE ../src/armnnUtils)
diff --git a/tests/ExecuteNetwork/ArmNNExecutor.cpp b/tests/ExecuteNetwork/ArmNNExecutor.cpp
deleted file mode 100644
index 626155e28c..0000000000
--- a/tests/ExecuteNetwork/ArmNNExecutor.cpp
+++ /dev/null
@@ -1,767 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-
-#include "ArmNNExecutor.hpp"
-#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
-
-#include <armnn/IAsyncExecutionCallback.hpp>
-#include <AsyncExecutionCallback.hpp>
-
-
-using namespace armnn;
-using namespace std::chrono;
-
-ArmNNExecutor::ArmNNExecutor(const ExecuteNetworkParams& params, armnn::IRuntime::CreationOptions runtimeOptions)
-: m_Params(params)
-{
-    runtimeOptions.m_EnableGpuProfiling = params.m_EnableProfiling;
-    runtimeOptions.m_DynamicBackendsPath = params.m_DynamicBackendsPath;
-    m_Runtime = armnn::IRuntime::Create(runtimeOptions);
-
-    auto parser = CreateParser();
-    auto network = parser->CreateNetwork(m_Params);
-    auto optNet = OptimizeNetwork(network.get());
-
-    m_IOInfo = GetIOInfo(network.get());
-    SetupInputsAndOutputs();
-
-    std::string errorMsg;
-    INetworkProperties networkProperties{m_Params.m_Concurrent, MemorySource::Undefined, MemorySource::Undefined};
-    m_Runtime->LoadNetwork(m_NetworkId, std::move(optNet), errorMsg, networkProperties);
-
-    if (m_Params.m_Iterations > 1)
-    {
-        std::stringstream msg;
-        msg << "Network will be executed " << m_Params.m_Iterations;
-        if (m_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();
-    }
-
-    if (m_Params.m_GenerateTensorData)
-    {
-        ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-    }
-
-    if (m_Params.m_DontPrintOutputs)
-    {
-        ARMNN_LOG(info) << "Printing outputs to console is disabled.";
-    }
-}
-
-void ArmNNExecutor::ExecuteAsync()
-{
-    std::vector<std::shared_ptr<armnn::IWorkingMemHandle>> memHandles;
-    std::unique_ptr<armnn::Threadpool> threadpool;
-    armnn::AsyncCallbackManager callbackManager;
-    std::unordered_map<armnn::InferenceId, const armnn::OutputTensors*> inferenceOutputMap;
-
-    for (size_t i = 0; i < m_Params.m_ThreadPoolSize; ++i)
-    {
-        memHandles.emplace_back(m_Runtime->CreateWorkingMemHandle(m_NetworkId));
-    }
-
-    threadpool = std::make_unique<armnn::Threadpool>(m_Params.m_ThreadPoolSize,
-                                                     m_Runtime.get(),
-                                                     memHandles);
-
-    ARMNN_LOG(info) << "Asynchronous execution with Arm NN thread pool...  \n";
-    // 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::max();
-    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 < m_Params.m_Iterations; ++i)
-    {
-        std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId);
-
-        std::shared_ptr<armnn::AsyncExecutionCallback> cb = callbackManager.GetNewCallback();
-        inferenceOutputMap.insert({cb->GetInferenceId(), &m_OutputTensorsVec[i]});
-        threadpool->Schedule(m_NetworkId,
-                             m_InputTensorsVec[i],
-                             m_OutputTensorsVec[i],
-                             armnn::QosExecPriority::Medium,
-                             cb);
-    }
-
-    // Check the results
-    for (size_t iteration = 0; iteration < m_Params.m_Iterations; ++iteration)
-    {
-        auto cb = callbackManager.GetNotifiedCallback();
-
-        // Get the results
-        if (earliestStartTime > cb->GetStartTime())
-        {
-            earliestStartTime = cb->GetStartTime();
-        }
-        if (latestEndTime < cb->GetEndTime())
-        {
-            latestEndTime = cb->GetEndTime();
-        }
-
-        auto startTime = time_point_cast<std::chrono::milliseconds>(cb->GetStartTime());
-        auto endTime = time_point_cast<std::chrono::milliseconds>(cb->GetEndTime());
-        auto inferenceDuration = endTime - startTime;
-        CheckInferenceTimeThreshold(inferenceDuration, m_Params.m_ThresholdTime);
-        if(!m_Params.m_DontPrintOutputs)
-        {
-            const armnn::OutputTensors* out = inferenceOutputMap[cb->GetInferenceId()];
-            PrintOutputTensors(out, iteration);
-        }
-    }
-    //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";
-
-}
-
-void ArmNNExecutor::ExecuteSync()
-{
-    for (size_t x = 0; x < m_Params.m_Iterations; x++)
-    {
-        std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId);
-
-        const auto start_time = armnn::GetTimeNow();
-        armnn::Status ret;
-        if (m_Params.m_ImportInputsIfAligned)
-        {
-             ret = m_Runtime->EnqueueWorkload(m_NetworkId,
-                                              m_InputTensorsVec[x],
-                                              m_OutputTensorsVec[x],
-                                              m_ImportedInputIds[x],
-                                              m_ImportedOutputIds[x]);
-        }
-        else
-        {
-            ret = m_Runtime->EnqueueWorkload(m_NetworkId,
-                                             m_InputTensorsVec[x],
-                                             m_OutputTensorsVec[x]);
-        }
-
-        const auto inferenceDuration = armnn::GetTimeDuration(start_time);
-
-        // if profiling is enabled print out the results
-        if(profiler && profiler->IsProfilingEnabled())
-        {
-            profiler->Print(std::cout);
-        }
-
-        if(ret == armnn::Status::Failure)
-        {
-            throw armnn::Exception("IRuntime::EnqueueWorkload failed");
-        }
-
-        if(!m_Params.m_DontPrintOutputs)
-        {
-            PrintOutputTensors(&m_OutputTensorsVec[x],  x);
-        }
-
-        // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
-        CheckInferenceTimeThreshold(inferenceDuration, m_Params.m_ThresholdTime);
-    }
-}
-
-std::vector<const void *> ArmNNExecutor::Execute()
-{
-    if(m_Params.m_ThreadPoolSize == 0)
-    {
-        ExecuteSync();
-    }
-    else
-    {
-        ExecuteAsync();
-    }
-    std::vector<const void *> results;
-    for (auto& output : m_OutputStorage)
-    {
-        results.push_back(output.m_Mem);
-    }
-
-    return results;
-}
-
-void ArmNNExecutor::PrintNetworkInfo()
-{
-    const std::vector<std::string>& inputNames = m_Params.m_InputNames.size() != 0 ?
-                                                 m_Params.m_InputNames :
-                                                 m_IOInfo.m_InputNames;
-    std::stringstream ss;
-    ss << "===== Network Info =====\n";
-    ss << "Inputs in order:\n" ;
-    for (const auto& inputName : inputNames)
-    {
-        const auto inputInfo = m_IOInfo.m_InputInfoMap[inputName].second;
-        ss <<  inputName << ", " << inputInfo.GetShape() << ", " << GetDataTypeName(inputInfo.GetDataType()) << "\n";
-    }
-
-    ss << "Outputs in order:\n" ;
-    for (const auto& outputName : m_IOInfo.m_OutputNames)
-    {
-        const auto outputInfo = m_IOInfo.m_OutputInfoMap[outputName].second;
-        ss <<  outputName << ", " << outputInfo.GetShape() << ", " << GetDataTypeName(outputInfo.GetDataType()) << "\n";
-        if (outputInfo.IsQuantized())
-        {
-            ss << "Quantization Offset: " << outputInfo.GetQuantizationOffset();
-            if (outputInfo.HasMultipleQuantizationScales())
-            {
-                ss << "Quantization scales: ";
-                for (const auto scale: outputInfo.GetQuantizationScales())
-                {
-                    ss << scale << ", ";
-                }
-                ss << "\n";
-            }
-            else
-            {
-                ss << "Quantization scale: " << outputInfo.GetQuantizationScale();
-            }
-        }
-    }
-
-    std::cout << ss.str() << std::endl;
-}
-
-void ArmNNExecutor::SetupInputsAndOutputs()
-{
-    const unsigned int noOfInputs = m_IOInfo.m_InputNames.size();
-
-    if (m_Params.m_InputNames.size() != 0 && m_Params.m_InputNames.size() != noOfInputs)
-    {
-        LogAndThrow("Number of input names does not match number of inputs\n");
-    }
-
-    const unsigned int inputFilePaths = m_Params.m_InputTensorDataFilePaths.size();
-    const std::vector<std::string>& inputNames = m_Params.m_InputNames.size() != 0 ?
-                                                 m_Params.m_InputNames :
-                                                 m_IOInfo.m_InputNames;
-    unsigned int noInputSets;
-
-    if (inputFilePaths != 0)
-    {
-        if (inputFilePaths % noOfInputs != 0)
-        {
-            LogAndThrow("Number of input files: " + std::to_string(inputFilePaths) +
-                        " not compatible with number of inputs: " + std::to_string(noOfInputs));
-        }
-        noInputSets = inputFilePaths / noOfInputs;
-        if (noInputSets != 1 && m_Params.m_ReuseBuffers)
-        {
-            LogAndThrow("Specifying multiple sets of inputs not compatible with ReuseBuffers");
-        }
-    }
-    else
-    {
-        noInputSets = 1;
-    }
-
-    const unsigned int noOfOutputs = m_IOInfo.m_OutputNames.size();
-    const unsigned int outputFilePaths = m_Params.m_OutputTensorFiles.size();
-    unsigned int noOutputSets;
-
-    if (outputFilePaths != 0)
-    {
-        if (outputFilePaths % noOfOutputs != 0)
-        {
-            LogAndThrow("Number of output files: " + std::to_string(outputFilePaths) +
-                        ", not compatible with number of outputs: " + std::to_string(noOfOutputs));
-        }
-        noOutputSets = outputFilePaths / noOfOutputs;
-
-        if (noOutputSets != 1 && m_Params.m_ReuseBuffers)
-        {
-            LogAndThrow("Specifying multiple sets of outputs not compatible with ReuseBuffers");
-        }
-    }
-    else
-    {
-        noOutputSets = 1;
-    }
-
-    if (m_Params.m_ThreadPoolSize != 0)
-    {
-        // The current implementation of the Threadpool does not allow binding of outputs to a thread
-        // So to ensure no two threads write to the same output at the same time, no output can be reused
-        noOutputSets = m_Params.m_Iterations;
-    }
-
-    if (m_Params.m_InputTensorDataFilePaths.size() > noOfInputs)
-    {
-        ARMNN_LOG(info) << "Given network has " << noOfInputs << " input/s. One input-tensor-data file is required "
-                        << "for each input. The user provided "
-                        << m_Params.m_InputTensorDataFilePaths.size()
-                        << " input-tensor-data file/s which will be used to fill the input/s.\n";
-    }
-
-    unsigned int inputCount = 0;
-    for(unsigned int inputSet = 0; inputSet  < noInputSets ; inputSet++)
-    {
-        armnn::InputTensors inputTensors;
-        for (const auto& inputName: inputNames)
-        {
-            armnn::BindingPointInfo bindingPointInfo;
-            try
-            {
-                bindingPointInfo = m_IOInfo.m_InputInfoMap.at(inputName);
-            }
-            catch (const std::out_of_range &e)
-            {
-                LogAndThrow("Input with inputName: " + inputName + " not found.");
-            }
-
-            const armnn::TensorInfo &tensorInfo = bindingPointInfo.second;
-            auto newInfo = armnn::TensorInfo{tensorInfo.GetShape(), tensorInfo.GetDataType(),
-                                             tensorInfo.GetQuantizationScale(),
-                                             tensorInfo.GetQuantizationOffset(),
-                                             true};
-
-            m_InputStorage.emplace_back(IOStorage{tensorInfo.GetNumBytes()});
-
-            const int bindingId = bindingPointInfo.first;
-            inputTensors.emplace_back(bindingId, armnn::ConstTensor{newInfo, m_InputStorage.back().m_Mem});
-
-            const armnn::Optional<std::string> dataFile = m_Params.m_GenerateTensorData ?
-                                                          armnn::EmptyOptional() :
-                                                          armnn::MakeOptional<std::string>(
-                                                                  m_Params.m_InputTensorDataFilePaths.at(inputCount++));
-
-            switch (tensorInfo.GetDataType())
-            {
-                case armnn::DataType::Float32:
-                {
-                    auto typedTensor = reinterpret_cast<float *>(m_InputStorage.back().m_Mem);
-                    PopulateTensorWithData<float>(typedTensor, tensorInfo.GetNumElements(), dataFile, inputName);
-                    break;
-                }
-                case armnn::DataType::QSymmS16:
-                {
-                    auto typedTensor = reinterpret_cast<int16_t *>(m_InputStorage.back().m_Mem);
-                    PopulateTensorWithData(typedTensor, tensorInfo.GetNumElements(), dataFile, inputName);
-                    break;
-                }
-                case armnn::DataType::QSymmS8:
-                {
-                    auto typedTensor = reinterpret_cast<int8_t *>(m_InputStorage.back().m_Mem);
-                    PopulateTensorWithData(typedTensor, tensorInfo.GetNumElements(), dataFile, inputName);
-                    break;
-                }
-                case armnn::DataType::QAsymmU8:
-                case armnn::DataType::QAsymmS8:
-                {
-                    auto typedTensor = reinterpret_cast<uint8_t *>(m_InputStorage.back().m_Mem);
-                    PopulateTensorWithData(typedTensor, tensorInfo.GetNumElements(), dataFile, inputName);
-                    break;
-                }
-                default:
-                {
-                }
-            }
-            m_InputTensorsVec.push_back(inputTensors);
-
-            if (m_Params.m_ImportInputsIfAligned)
-            {
-                m_ImportedInputIds.push_back(
-                    m_Runtime->ImportInputs(m_NetworkId, m_InputTensorsVec.back(), armnn::MemorySource::Malloc));
-            }
-        }
-    }
-
-    for(unsigned int outputSet = 0; outputSet  < noOutputSets; outputSet++)
-    {
-        armnn::OutputTensors outputTensors;
-        for (const auto &output: m_IOInfo.m_OutputInfoMap)
-        {
-            const armnn::BindingPointInfo &bindingPointInfo = output.second;
-            const armnn::TensorInfo &tensorInfo = bindingPointInfo.second;
-
-            m_OutputStorage.emplace_back(tensorInfo.GetNumBytes());
-            outputTensors.emplace_back(bindingPointInfo.first, armnn::Tensor{tensorInfo, m_OutputStorage.back().m_Mem});
-        }
-        m_OutputTensorsVec.emplace_back(outputTensors);
-        if (m_Params.m_ImportInputsIfAligned)
-        {
-            m_ImportedOutputIds.push_back(
-                    m_Runtime->ImportOutputs(m_NetworkId, m_OutputTensorsVec.back(), armnn::MemorySource::Malloc));
-        }
-    }
-
-    // Fill the remaining iterations with copies
-    const unsigned int remainingInputSets = m_Params.m_Iterations - noInputSets ;
-    for (unsigned int i = 1; i <= remainingInputSets; i++)
-    {
-        m_InputTensorsVec.push_back(m_InputTensorsVec[noInputSets % i]);
-        if (m_Params.m_ImportInputsIfAligned)
-        {
-            m_ImportedInputIds.push_back(m_ImportedInputIds[noInputSets % i]);
-        }
-    }
-
-    const unsigned int remainingOutputSets = m_Params.m_Iterations - noOutputSets;
-    for (unsigned int i = 1; i <= remainingOutputSets; i++)
-    {
-        m_OutputTensorsVec.push_back(m_OutputTensorsVec[noOutputSets % i]);
-        if (m_Params.m_ImportInputsIfAligned)
-        {
-            m_ImportedOutputIds.push_back(m_ImportedOutputIds[noOutputSets % i]);
-        }
-    }
-}
-
-ArmNNExecutor::IOInfo ArmNNExecutor::GetIOInfo(armnn::INetwork* network)
-{
-    struct IOStrategy : armnn::IStrategy
-    {
-        void ExecuteStrategy(const armnn::IConnectableLayer* layer,
-                             const armnn::BaseDescriptor& descriptor,
-                             const std::vector<armnn::ConstTensor>& constants,
-                             const char* name,
-                             const armnn::LayerBindingId id = 0) override
-        {
-            armnn::IgnoreUnused(descriptor, constants, id);
-            switch (layer->GetType())
-            {
-                case armnn::LayerType::Input:
-                {
-                    m_IOInfo.m_InputNames.emplace_back(name);
-                    m_IOInfo.m_InputInfoMap[name] = {id, layer->GetOutputSlot(0).GetTensorInfo()};
-                    break;
-                }
-                case armnn::LayerType::Output:
-                {
-                    m_IOInfo.m_OutputNames.emplace_back(name);
-                    m_IOInfo.m_OutputInfoMap[name] = {id, layer->GetInputSlot(0).GetConnection()->GetTensorInfo()};
-                    break;
-                }
-                default:{}
-            }
-        }
-
-        IOInfo m_IOInfo;
-    };
-
-    IOStrategy ioStrategy;
-    network->ExecuteStrategy(ioStrategy);
-
-    return ioStrategy.m_IOInfo;
-}
-
-armnn::IOptimizedNetworkPtr ArmNNExecutor::OptimizeNetwork(armnn::INetwork* network)
-{
-    armnn::IOptimizedNetworkPtr optNet{nullptr, [](armnn::IOptimizedNetwork*){}};
-
-    armnn::OptimizerOptions options;
-    options.m_ReduceFp32ToFp16 = m_Params.m_EnableFp16TurboMode;
-    options.m_ReduceFp32ToBf16 = m_Params.m_EnableBf16TurboMode;
-    options.m_Debug = m_Params.m_PrintIntermediate;
-    options.m_shapeInferenceMethod = m_Params.m_InferOutputShape ?
-                                     armnn::ShapeInferenceMethod::InferAndValidate :
-                                     armnn::ShapeInferenceMethod::ValidateOnly;
-    options.m_ProfilingEnabled = m_Params.m_EnableProfiling;
-
-    armnn::BackendOptions gpuAcc("GpuAcc",
-                                 {
-                                         { "FastMathEnabled", m_Params.m_EnableFastMath },
-                                         { "SaveCachedNetwork", m_Params.m_SaveCachedNetwork },
-                                         { "CachedNetworkFilePath", m_Params.m_CachedNetworkFilePath },
-                                         { "MLGOTuningFilePath", m_Params.m_MLGOTuningFilePath }
-                                 });
-
-    armnn::BackendOptions cpuAcc("CpuAcc",
-                                 {
-                                         { "FastMathEnabled", m_Params.m_EnableFastMath },
-                                         { "NumberOfThreads", m_Params.m_NumberOfThreads }
-                                 });
-    options.m_ModelOptions.push_back(gpuAcc);
-    options.m_ModelOptions.push_back(cpuAcc);
-
-    const auto optimization_start_time = armnn::GetTimeNow();
-    optNet = armnn::Optimize(*network, m_Params.m_ComputeDevices, m_Runtime->GetDeviceSpec(), options);
-
-    ARMNN_LOG(info) << "Optimization time: " << std::setprecision(2)
-                    << std::fixed << armnn::GetTimeDuration(optimization_start_time).count() << " ms\n";
-
-    if (!optNet)
-    {
-        LogAndThrow("Optimize returned nullptr");
-    }
-
-    return optNet;
-}
-
-std::unique_ptr<ArmNNExecutor::IParser> ArmNNExecutor::CreateParser()
-{
-    // If no model format is given check the file name
-    const std::string& modelFormat = m_Params.m_ModelPath;
-
-    m_Params.m_IsModelBinary = modelFormat.find("json") == std::string::npos ? true : false;
-    std::unique_ptr<IParser> parser = nullptr;
-    // Forward to implementation based on the parser type
-    if (modelFormat.find("armnn") != std::string::npos)
-    {
-    #if defined(ARMNN_SERIALIZER)
-        parser = std::make_unique<ArmNNDeserializer>();
-    #else
-             LogAndThrow("Not built with serialization support.");
-#endif
-        }
-        else if(modelFormat.find("tflite") != std::string::npos)
-        {
-    #if defined(ARMNN_TF_LITE_PARSER)
-            parser = std::make_unique<TfliteParser>(m_Params);
-    #else
-           LogAndThrow("Not built with Tensorflow-Lite parser support.");
-#endif
-        }
-        else if (modelFormat.find("onnx") != std::string::npos)
-        {
-    #if defined(ARMNN_ONNX_PARSER)
-            parser = std::make_unique<OnnxParser>();
-    #else
-        LogAndThrow("Not built with Onnx parser support.");
-    #endif
-    }
-
-    return parser;
-}
-
-void ArmNNExecutor::PrintOutputTensors(const armnn::OutputTensors* outputTensors,
-                                       unsigned int iteration)
-{
-    auto findOutputName = [&](const armnn::LayerBindingId id)
-    {
-        for (auto it = m_IOInfo.m_OutputInfoMap.begin(); it != m_IOInfo.m_OutputInfoMap.end(); ++it)
-        {
-            if (id == it->second.first)
-            {
-                return it->first;
-            }
-        }
-        return std::string{};
-    };
-
-    unsigned int outputIndex = 0;
-    unsigned int numOutputs = outputTensors->size();
-    for (const auto& output: *outputTensors)
-    {
-        const auto bindingName = findOutputName(output.first);
-        // 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 + outputIndex;
-        if (!m_Params.m_OutputTensorFiles.empty())
-        {
-            outputFileIndex = outputFileIndex % m_Params.m_OutputTensorFiles.size();
-            ARMNN_LOG(info) << "Writing output: " << bindingName << " bindingId: '"
-                            << output.first
-                            << "' of iteration: " << iteration+1 << " to file: '"
-                            << m_Params.m_OutputTensorFiles[outputFileIndex] << "'";
-        }
-
-        const armnn::Optional<std::string> outputTensorFile = m_Params.m_OutputTensorFiles.empty() ?
-                                                              armnn::EmptyOptional() :
-                                                              armnn::MakeOptional<std::string>(
-                                                                      m_Params.m_OutputTensorFiles[outputFileIndex]);
-
-        OutputWriteInfo outputWriteInfo
-        {
-            outputTensorFile,
-            bindingName,
-            output.second,
-            !m_Params.m_DontPrintOutputs
-        };
-
-        std::cout << bindingName << ": ";
-        std::vector<float> values;
-        switch (output.second.GetDataType())
-        {
-            case armnn::DataType::Float32:
-            {
-                PrintTensor<float>(outputWriteInfo, "%f ");
-                break;
-            }
-
-            case armnn::DataType::Signed32:
-            {
-                PrintTensor<int>(outputWriteInfo, "%d ");
-                break;
-            }
-            case armnn::DataType::QSymmS8:
-            case armnn::DataType::QAsymmS8:
-            {
-                PrintQuantizedTensor<int8_t>(outputWriteInfo);
-                break;
-            }
-            case armnn::DataType::QAsymmU8:
-            {
-                PrintQuantizedTensor<uint8_t>(outputWriteInfo);
-                break;
-            }
-            case armnn::DataType::Float16:
-            case armnn::DataType::QSymmS16:
-            case armnn::DataType::BFloat16:
-            case armnn::DataType::Boolean:
-            case armnn::DataType::Signed64:
-                break;
-        }
-        std::cout << "\n";
-    }
-}
-
-void ArmNNExecutor::CompareAndPrintResult(std::vector<const void*> otherOutput)
-{
-    unsigned int index = 0;
-
-    for (const auto& outputTensors: m_OutputTensorsVec)
-    {
-        for (const auto& outputTensor: outputTensors)
-        {
-            float result = 0;
-            size_t size = outputTensor.second.GetNumBytes();
-
-            switch (outputTensor.second.GetDataType())
-            {
-                case armnn::DataType::Float32:
-                {
-                    result = ComputeRMSE<float>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);
-                    break;
-                }
-                case armnn::DataType::QSymmS16:
-                {
-                    result = ComputeRMSE<int16_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);
-                    break;
-                }
-                case armnn::DataType::QSymmS8:
-                {
-                    result = ComputeRMSE<int8_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);
-                    break;
-                }
-                case armnn::DataType::QAsymmU8:
-                case armnn::DataType::QAsymmS8:
-                {
-                    result = ComputeRMSE<uint8_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);
-                    break;
-                }
-                default: {}
-            }
-
-            std::cout << "RMSE: of " << result << "\n";
-        }
-    }
-}
-#if defined(ARMNN_SERIALIZER)
-ArmNNExecutor::ArmNNDeserializer::ArmNNDeserializer() : m_Parser(armnnDeserializer::IDeserializer::Create()){}
-
-armnn::INetworkPtr ArmNNExecutor::ArmNNDeserializer::CreateNetwork(const ExecuteNetworkParams &params)
-{
-    const std::string &modelPath = params.m_ModelPath;
-
-    std::ifstream file(modelPath, std::ios::binary);
-    return m_Parser->CreateNetworkFromBinary(file);
-}
-
-armnn::BindingPointInfo
-ArmNNExecutor::ArmNNDeserializer::GetInputBindingPointInfo(size_t, const std::string &inputName)
-{
-    armnnDeserializer::BindingPointInfo DeserializerBPI = m_Parser->GetNetworkInputBindingInfo(0, inputName);
-    return {DeserializerBPI.m_BindingId, DeserializerBPI.m_TensorInfo};
-}
-
-armnn::BindingPointInfo
-ArmNNExecutor::ArmNNDeserializer::GetOutputBindingPointInfo(size_t, const std::string &outputName)
-{
-    armnnDeserializer::BindingPointInfo DeserializerBPI = m_Parser->GetNetworkOutputBindingInfo(0, outputName);
-    return {DeserializerBPI.m_BindingId, DeserializerBPI.m_TensorInfo};
-}
-#endif
-
-#if defined(ARMNN_TF_LITE_PARSER)
-ArmNNExecutor::TfliteParser::TfliteParser(const ExecuteNetworkParams& params)
-{
-    armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions options;
-    options.m_StandInLayerForUnsupported = params.m_ParseUnsupported;
-    options.m_InferAndValidate = params.m_InferOutputShape;
-
-    m_Parser = armnnTfLiteParser::ITfLiteParser::Create(options);
-}
-
-armnn::INetworkPtr ArmNNExecutor::TfliteParser::CreateNetwork(const ExecuteNetworkParams &params)
-{
-    const std::string &modelPath = params.m_ModelPath;
-    return m_Parser->CreateNetworkFromBinaryFile(modelPath.c_str());
-}
-
-armnn::BindingPointInfo ArmNNExecutor::TfliteParser::GetInputBindingPointInfo(size_t subgraphId,
-                                                                              const std::string &inputName)
-{
-    return m_Parser->GetNetworkInputBindingInfo(subgraphId, inputName);
-}
-
-armnn::BindingPointInfo ArmNNExecutor::TfliteParser::GetOutputBindingPointInfo(size_t subgraphId,
-                                                                               const std::string &outputName)
-{
-    return m_Parser->GetNetworkOutputBindingInfo(subgraphId, outputName);
-}
-#endif
-
-
-#if defined(ARMNN_ONNX_PARSER)
-ArmNNExecutor::OnnxParser::OnnxParser() : m_Parser(armnnOnnxParser::IOnnxParser::Create()){}
-armnn::INetworkPtr ArmNNExecutor::OnnxParser::CreateNetwork(const ExecuteNetworkParams &params)
-{
-    const std::string &modelPath = params.m_ModelPath;
-    m_Parser = armnnOnnxParser::IOnnxParser::Create();
-    std::map<std::string, armnn::TensorShape> inputShapes;
-    if(!params.m_InputTensorShapes.empty())
-    {
-        const size_t numInputShapes = params.m_InputTensorShapes.size();
-        const size_t numInputBindings = params.m_InputNames.size();
-        if(numInputShapes < numInputBindings)
-        {
-            throw armnn::Exception(
-                    fmt::format("Not every input has its tensor shape specified: expected={0}, got={1}",
-                                numInputBindings, numInputShapes));
-        }
-
-        for (size_t i = 0; i < numInputShapes; i++)
-        {
-            inputShapes[params.m_InputNames[i]] = params.m_InputTensorShapes[i];
-        }
-
-        return params.m_IsModelBinary ?
-               m_Parser->CreateNetworkFromBinaryFile(modelPath.c_str(), inputShapes) :
-               m_Parser->CreateNetworkFromTextFile(modelPath.c_str(), inputShapes);
-    }
-
-    // Handle text and binary input differently by calling the corresponding parser function
-    return params.m_IsModelBinary ?
-           m_Parser->CreateNetworkFromBinaryFile(params.m_ModelPath.c_str()) :
-           m_Parser->CreateNetworkFromTextFile(params.m_ModelPath.c_str());
-}
-
-armnn::BindingPointInfo ArmNNExecutor::OnnxParser::GetInputBindingPointInfo(size_t, const std::string &inputName)
-{
-    return m_Parser->GetNetworkInputBindingInfo(inputName);
-}
-
-armnn::BindingPointInfo ArmNNExecutor::OnnxParser::GetOutputBindingPointInfo(size_t, const std::string &outputName)
-{
-    return m_Parser->GetNetworkOutputBindingInfo(outputName);
-}
-#endif
diff --git a/tests/ExecuteNetwork/ArmNNExecutor.hpp b/tests/ExecuteNetwork/ArmNNExecutor.hpp
deleted file mode 100644
index aec7a20a06..0000000000
--- a/tests/ExecuteNetwork/ArmNNExecutor.hpp
+++ /dev/null
@@ -1,161 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "IExecutor.hpp"
-#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
-#include "ExecuteNetworkProgramOptions.hpp"
-#include "armnn/utility/NumericCast.hpp"
-#include "armnn/utility/Timer.hpp"
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Threadpool.hpp>
-#include <armnn/Logging.hpp>
-#include <armnn/utility/Timer.hpp>
-#include <armnn/BackendRegistry.hpp>
-#include <armnn/utility/Assert.hpp>
-#include <armnn/utility/NumericCast.hpp>
-
-#include <armnnUtils/Filesystem.hpp>
-#include <HeapProfiling.hpp>
-
-#include <fmt/format.h>
-
-#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
-
-class ArmNNExecutor : public IExecutor
-{
-public:
-    ArmNNExecutor(const ExecuteNetworkParams& params, armnn::IRuntime::CreationOptions runtimeOptions);
-
-    std::vector<const void *> Execute() override;
-    void PrintNetworkInfo() override;
-    void CompareAndPrintResult(std::vector<const void*> otherOutput) override;
-
-private:
-
-    struct IParser;
-    struct IOInfo;
-    struct IOStorage;
-
-    using BindingPointInfo = armnn::BindingPointInfo;
-
-    std::unique_ptr<IParser> CreateParser();
-
-    void ExecuteAsync();
-    void ExecuteSync();
-    void SetupInputsAndOutputs();
-
-    IOInfo GetIOInfo(armnn::INetwork* network);
-
-    void PrintOutputTensors(const armnn::OutputTensors* outputTensors, unsigned int iteration);
-
-    armnn::IOptimizedNetworkPtr OptimizeNetwork(armnn::INetwork* network);
-
-    struct IOStorage
-    {
-        IOStorage(size_t size)
-        {
-            m_Mem = operator new(size);
-        }
-        ~IOStorage()
-        {
-            operator delete(m_Mem);
-        }
-        IOStorage(IOStorage &&rhs)
-        {
-            this->m_Mem = rhs.m_Mem;
-            rhs.m_Mem = nullptr;
-        }
-
-        IOStorage(const IOStorage &rhs) = delete;
-        IOStorage &operator=(IOStorage &rhs) = delete;
-        IOStorage &operator=(IOStorage &&rhs) = delete;
-
-        void *m_Mem;
-    };
-
-    struct IOInfo
-    {
-        std::vector<std::string> m_InputNames;
-        std::vector<std::string> m_OutputNames;
-        std::map<std::string, armnn::BindingPointInfo> m_InputInfoMap;
-        std::map<std::string, armnn::BindingPointInfo> m_OutputInfoMap;
-    };
-
-    IOInfo m_IOInfo;
-    std::vector<IOStorage> m_InputStorage;
-    std::vector<IOStorage> m_OutputStorage;
-    std::vector<armnn::InputTensors> m_InputTensorsVec;
-    std::vector<armnn::OutputTensors> m_OutputTensorsVec;
-    std::vector<std::vector<unsigned int>> m_ImportedInputIds;
-    std::vector<std::vector<unsigned int>> m_ImportedOutputIds;
-    std::shared_ptr<armnn::IRuntime> m_Runtime;
-    armnn::NetworkId m_NetworkId;
-    ExecuteNetworkParams m_Params;
-
-    struct IParser
-    {
-        virtual armnn::INetworkPtr CreateNetwork(const ExecuteNetworkParams& params) = 0;
-        virtual armnn::BindingPointInfo GetInputBindingPointInfo(size_t id, const std::string &inputName) = 0;
-        virtual armnn::BindingPointInfo GetOutputBindingPointInfo(size_t id, const std::string &outputName) = 0;
-
-        virtual ~IParser(){};
-    };
-
-#if defined(ARMNN_SERIALIZER)
-    class ArmNNDeserializer : public IParser
-    {
-        public:
-        ArmNNDeserializer();
-
-        armnn::INetworkPtr CreateNetwork(const ExecuteNetworkParams &params) override;
-        armnn::BindingPointInfo GetInputBindingPointInfo(size_t, const std::string &inputName) override;
-        armnn::BindingPointInfo GetOutputBindingPointInfo(size_t, const std::string &outputName) override;
-
-        private:
-        armnnDeserializer::IDeserializerPtr m_Parser;
-    };
-#endif
-
-#if defined(ARMNN_TF_LITE_PARSER)
-    class TfliteParser : public IParser
-    {
-    public:
-        TfliteParser(const ExecuteNetworkParams& params);
-
-        armnn::INetworkPtr CreateNetwork(const ExecuteNetworkParams &params) override;
-        armnn::BindingPointInfo GetInputBindingPointInfo(size_t subgraphId, const std::string &inputName) override;
-        armnn::BindingPointInfo GetOutputBindingPointInfo(size_t subgraphId, const std::string &outputName) override;
-
-    private:
-        armnnTfLiteParser::ITfLiteParserPtr m_Parser{nullptr, [](armnnTfLiteParser::ITfLiteParser*){}};
-    };
-#endif
-
-#if defined(ARMNN_ONNX_PARSER)
-    class OnnxParser : public IParser
-    {
-        public:
-        OnnxParser();
-
-        armnn::INetworkPtr CreateNetwork(const ExecuteNetworkParams &params) override;
-        armnn::BindingPointInfo GetInputBindingPointInfo(size_t subgraphId, const std::string &inputName) override;
-        armnn::BindingPointInfo GetOutputBindingPointInfo(size_t subgraphId, const std::string &outputName) override;
-
-        private:
-        armnnOnnxParser::IOnnxParserPtr m_Parser;
-    };
-#endif
-};
\ No newline at end of file
diff --git a/tests/ExecuteNetwork/ExecuteNetwork.cpp b/tests/ExecuteNetwork/ExecuteNetwork.cpp
index 73cbbb8162..153fe5bcc7 100644
--- a/tests/ExecuteNetwork/ExecuteNetwork.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetwork.cpp
@@ -1,32 +1,993 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
+#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
 #include "ExecuteNetworkProgramOptions.hpp"
-#include "ArmNNExecutor.hpp"
-#if defined(ARMNN_TF_LITE_DELEGATE)
-#include "TfliteExecutor.hpp"
-#endif
+#include <armnn/IAsyncExecutionCallback.hpp>
+#include <AsyncExecutionCallback.hpp>
+
 #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>
 
-std::unique_ptr<IExecutor> BuildExecutor(ProgramOptions& programOptions)
+/**
+ * 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)
 {
-    if (programOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate ||
-        programOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter)
+    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 defined(ARMNN_TF_LITE_DELEGATE)
-        return std::make_unique<TfLiteExecutor>(programOptions.m_ExNetParams);
-#else
-        ARMNN_LOG(fatal) << "Not built with Arm NN Tensorflow-Lite delegate support.";
-            return nullptr;
-#endif
+        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;
+        }
     }
     else
     {
-        return std::make_unique<ArmNNExecutor>(programOptions.m_ExNetParams, programOptions.m_RuntimeOptions);
+        std::cout << "Running on TfLite without ArmNN delegate\n";
     }
+
+    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
@@ -43,49 +1004,74 @@ int main(int argc, const char* argv[])
 
     // 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;
     }
 
-    std::vector<const void *> outputResults;
-
-    auto executor = BuildExecutor(programOptions);
-    if (!executor)
+    if ((ProgramOptions.m_ExNetParams.m_OutputDetailsToStdOut ||
+         ProgramOptions.m_ExNetParams.m_OutputDetailsOnlyToStdOut)
+         && !ProgramOptions.m_ExNetParams.m_EnableProfiling)
     {
+        ARMNN_LOG(fatal) << "You must enable profiling if you would like to output layer details";
         return EXIT_FAILURE;
     }
 
-    executor->PrintNetworkInfo();
-    outputResults = executor->Execute();
+    std::string modelFormat = ProgramOptions.m_ExNetParams.m_ModelFormat;
 
-    if (!programOptions.m_ExNetParams.m_ComparisonComputeDevices.empty() ||
-            programOptions.m_ExNetParams.m_CompareWithTflite)
+    // Forward to implementation based on the parser type
+    if (modelFormat.find("armnn") != std::string::npos)
     {
-        ExecuteNetworkParams comparisonParams = programOptions.m_ExNetParams;
-        comparisonParams.m_ComputeDevices = programOptions.m_ExNetParams.m_ComparisonComputeDevices;
-
-        if (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)
         {
-            comparisonParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter;
+            #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
         }
-
-        auto comparisonExecutor = BuildExecutor(programOptions);
-
-        if (!comparisonExecutor)
+        else if (ProgramOptions.m_ExNetParams.m_TfLiteExecutor ==
+                    ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate ||
+                ProgramOptions.m_ExNetParams.m_TfLiteExecutor ==
+                    ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter)
         {
+        #if defined(ARMNN_TF_LITE_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
         }
-
-        comparisonExecutor->PrintNetworkInfo();
-        comparisonExecutor->Execute();
-
-        comparisonExecutor->CompareAndPrintResult(outputResults);
+    }
+    else
+    {
+        ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
+                         << "'. Please include 'tflite' or 'onnx'";
+        return EXIT_FAILURE;
     }
 }
diff --git a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
index f341c30738..cc75bb4323 100644
--- a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
@@ -1,15 +1,76 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include "ExecuteNetworkParams.hpp"
 
 #include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
+#include <InferenceModel.hpp>
 #include <armnn/Logging.hpp>
 
 #include <fmt/format.h>
-#include <armnnUtils/Filesystem.hpp>
+
+bool IsModelBinary(const std::string& modelFormat)
+{
+    // Parse model binary flag from the model-format string we got from the command-line
+    if (modelFormat.find("binary") != std::string::npos)
+    {
+        return true;
+    }
+    else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos)
+    {
+        return false;
+    }
+    else
+    {
+        throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. "
+                                                          "Please include 'binary' or 'text'",
+                                                          modelFormat));
+    }
+}
+
+void CheckModelFormat(const std::string& modelFormat)
+{
+    // Forward to implementation based on the parser type
+    if (modelFormat.find("armnn") != std::string::npos)
+    {
+#if defined(ARMNN_SERIALIZER)
+#else
+        throw armnn::InvalidArgumentException("Can't run model in armnn format without a "
+                                              "built with serialization support.");
+#endif
+    }
+    else if (modelFormat.find("onnx") != std::string::npos)
+    {
+#if defined(ARMNN_ONNX_PARSER)
+#else
+        throw armnn::InvalidArgumentException("Can't run model in onnx format without a "
+                                              "built with Onnx parser support.");
+#endif
+    }
+    else if (modelFormat.find("tflite") != std::string::npos)
+    {
+#if defined(ARMNN_TF_LITE_PARSER)
+        if (!IsModelBinary(modelFormat))
+        {
+            throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. Only 'binary' "
+                                                              "format supported for tflite files",
+                                                              modelFormat));
+        }
+#elif defined(ARMNN_TFLITE_DELEGATE)
+#else
+        throw armnn::InvalidArgumentException("Can't run model in tflite format without a "
+                                              "built with Tensorflow Lite parser support.");
+#endif
+    }
+    else
+    {
+        throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. "
+                                                          "Please include 'tflite' or 'onnx'",
+                                                          modelFormat));
+    }
+}
 
 void CheckClTuningParameter(const int& tuningLevel,
                             const std::string& tuningPath,
@@ -44,6 +105,7 @@ void CheckClTuningParameter(const int& tuningLevel,
             ARMNN_LOG(warning) << "To use Cl Tuning the compute device GpuAcc needs to be active.";
         }
     }
+
 }
 
 void ExecuteNetworkParams::ValidateParams()
@@ -58,6 +120,7 @@ void ExecuteNetworkParams::ValidateParams()
                              << invalidBackends;
         }
     }
+
     CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices);
 
     if (m_EnableBf16TurboMode && m_EnableFp16TurboMode)
@@ -66,6 +129,10 @@ void ExecuteNetworkParams::ValidateParams()
                                               "enabled at the same time.");
     }
 
+    m_IsModelBinary = IsModelBinary(m_ModelFormat);
+
+    CheckModelFormat(m_ModelFormat);
+
     // Check input tensor shapes
     if ((m_InputTensorShapes.size() != 0) &&
         (m_InputTensorShapes.size() != m_InputNames.size()))
@@ -90,6 +157,68 @@ void ExecuteNetworkParams::ValidateParams()
                                 m_InputNames.size(),
                                 m_InputTensorDataFilePaths.size()));
         }
+        else if (m_InputTensorDataFilePaths.size() % m_InputNames.size() != 0)
+        {
+            throw armnn::InvalidArgumentException(
+                    fmt::format("According to the number of input names the user provided the network has {} "
+                                "inputs. The user specified {} input-tensor-data file paths which is not "
+                                "divisible by the number of inputs.",
+                                m_InputNames.size(),
+                                m_InputTensorDataFilePaths.size()));
+        }
+    }
+
+    if (m_InputTypes.size() == 0)
+    {
+        //Defaults the value of all inputs to "float"
+        m_InputTypes.assign(m_InputNames.size(), "float");
+    }
+    else if ((m_InputTypes.size() != 0) &&
+             (m_InputTypes.size() != m_InputNames.size()))
+    {
+        throw armnn::InvalidArgumentException("input-name and input-type must have the same amount of elements.");
+    }
+
+    // Make sure that the number of input files given is divisible by the number of inputs of the model
+    if (!(m_InputTensorDataFilePaths.size() % m_InputNames.size() == 0))
+    {
+        throw armnn::InvalidArgumentException(
+                fmt::format("The number of input-tensor-data files ({0}) is not divisible by the "
+                            "number of inputs ({1} according to the number of input names).",
+                            m_InputTensorDataFilePaths.size(),
+                            m_InputNames.size()));
+    }
+
+    if (m_OutputTypes.size() == 0)
+    {
+        //Defaults the value of all outputs to "float"
+        m_OutputTypes.assign(m_OutputNames.size(), "float");
+    }
+    else if ((m_OutputTypes.size() != 0) &&
+             (m_OutputTypes.size() != m_OutputNames.size()))
+    {
+        throw armnn::InvalidArgumentException("output-name and output-type must have the same amount of elements.");
+    }
+
+    // Make sure that the number of output files given is equal to the number of outputs of the model
+    // or equal to the number of outputs of the model multiplied with the number of iterations
+    if (!m_OutputTensorFiles.empty())
+    {
+        if ((m_OutputTensorFiles.size() != m_OutputNames.size()) &&
+            (m_OutputTensorFiles.size() != m_OutputNames.size() * m_Iterations))
+        {
+            std::stringstream errmsg;
+            auto numOutputs = m_OutputNames.size();
+            throw armnn::InvalidArgumentException(
+                    fmt::format("The user provided {0} output-tensor files. The only allowed number of output-tensor "
+                                "files is the number of outputs of the network ({1} according to the number of "
+                                "output names) or the number of outputs multiplied with the number of times the "
+                                "network should be executed (NumOutputs * NumIterations = {1} * {2} = {3}).",
+                                m_OutputTensorFiles.size(),
+                                numOutputs,
+                                m_Iterations,
+                                numOutputs*m_Iterations));
+        }
     }
 
     // Check that threshold time is not less than zero
@@ -181,5 +310,4 @@ armnnDelegate::DelegateOptions ExecuteNetworkParams::ToDelegateOptions() const
 
     return delegateOptions;
 }
-
 #endif
diff --git a/tests/ExecuteNetwork/ExecuteNetworkParams.hpp b/tests/ExecuteNetwork/ExecuteNetworkParams.hpp
index 104c1c50c2..5ef2b6ea7c 100644
--- a/tests/ExecuteNetwork/ExecuteNetworkParams.hpp
+++ b/tests/ExecuteNetwork/ExecuteNetworkParams.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -16,6 +16,8 @@
 /// Check ExecuteNetworkProgramOptions.cpp for a description of each parameter
 struct ExecuteNetworkParams
 {
+    using TensorShapePtr = std::unique_ptr<armnn::TensorShape>;
+
     enum class TfLiteExecutor
     {
         ArmNNTfLiteParser,
@@ -23,49 +25,50 @@ struct ExecuteNetworkParams
         TfliteInterpreter
     };
 
-    bool                              m_AllowExpandedDims;
-    std::string                       m_CachedNetworkFilePath;
-    std::vector<armnn::BackendId>     m_ComputeDevices;
-    bool                              m_Concurrent;
-    bool                              m_DequantizeOutput;
-    std::string                       m_DynamicBackendsPath;
-    bool                              m_EnableBf16TurboMode;
-    bool                              m_EnableFastMath = false;
-    bool                              m_EnableFp16TurboMode;
-    bool                              m_EnableLayerDetails = false;
-    bool                              m_EnableProfiling;
-    bool                              m_GenerateTensorData;
-    bool                              m_InferOutputShape = false;
-    bool                              m_EnableDelegate = false;
-    bool                              m_IsModelBinary;
-    std::vector<std::string>          m_InputNames;
-    std::vector<std::string>          m_InputTensorDataFilePaths;
-    std::vector<armnn::TensorShape>   m_InputTensorShapes;
-    size_t                            m_Iterations;
-    std::string                       m_ModelPath;
-    unsigned int                      m_NumberOfThreads;
-    bool                              m_OutputDetailsToStdOut;
-    bool                              m_OutputDetailsOnlyToStdOut;
-    std::vector<std::string>          m_OutputNames;
-    std::vector<std::string>          m_OutputTensorFiles;
-    bool                              m_ParseUnsupported = false;
-    bool                              m_PrintIntermediate;
-    bool                              m_DontPrintOutputs;
-    bool                              m_QuantizeInput;
-    bool                              m_SaveCachedNetwork;
-    size_t                            m_SubgraphId;
-    double                            m_ThresholdTime;
-    int                               m_TuningLevel;
-    std::string                       m_TuningPath;
-    std::string                       m_MLGOTuningFilePath;
-    TfLiteExecutor                    m_TfLiteExecutor;
-    size_t                            m_ThreadPoolSize;
-    bool                              m_ImportInputsIfAligned;
-    bool                              m_ReuseBuffers;
+    bool                          m_AllowExpandedDims;
+    std::string                   m_CachedNetworkFilePath;
+    std::vector<armnn::BackendId> m_ComputeDevices;
+    bool                          m_Concurrent;
+    bool                          m_DequantizeOutput;
+    std::string                   m_DynamicBackendsPath;
+    bool                          m_EnableBf16TurboMode;
+    bool                          m_EnableFastMath = false;
+    bool                          m_EnableFp16TurboMode;
+    bool                          m_EnableLayerDetails = false;
+    bool                          m_EnableProfiling;
+    bool                          m_GenerateTensorData;
+    bool                          m_InferOutputShape = false;
+    bool                          m_EnableDelegate = false;
+    std::vector<std::string>      m_InputNames;
+    std::vector<std::string>      m_InputTensorDataFilePaths;
+    std::vector<TensorShapePtr>   m_InputTensorShapes;
+    std::vector<std::string>      m_InputTypes;
+    bool                          m_IsModelBinary;
+    size_t                        m_Iterations;
+    std::string                   m_ModelFormat;
+    std::string                   m_ModelPath;
+    unsigned int                  m_NumberOfThreads;
+    bool                          m_OutputDetailsToStdOut;
+    bool                          m_OutputDetailsOnlyToStdOut;
+    std::vector<std::string>      m_OutputNames;
+    std::vector<std::string>      m_OutputTensorFiles;
+    std::vector<std::string>      m_OutputTypes;
+    bool                          m_ParseUnsupported = false;
+    bool                          m_PrintIntermediate;
+    bool                          m_DontPrintOutputs;
+    bool                          m_QuantizeInput;
+    bool                          m_SaveCachedNetwork;
+    size_t                        m_SimultaneousIterations;
+    size_t                        m_SubgraphId;
+    double                        m_ThresholdTime;
+    int                           m_TuningLevel;
+    std::string                   m_TuningPath;
+    std::string                   m_MLGOTuningFilePath;
+    TfLiteExecutor                m_TfLiteExecutor;
+    size_t                        m_ThreadPoolSize;
+    bool                          m_ImportInputsIfAligned;
+    bool                          m_ReuseBuffers;
 
-    std::string                       m_ComparisonFile;
-    std::vector<armnn::BackendId>     m_ComparisonComputeDevices;
-    bool                              m_CompareWithTflite;
     // Ensures that the parameters for ExecuteNetwork fit together
     void ValidateParams();
 
diff --git a/tests/ExecuteNetwork/ExecuteNetworkProgramOptions.cpp b/tests/ExecuteNetwork/ExecuteNetworkProgramOptions.cpp
index da6200b8a9..ad35092c1d 100644
--- a/tests/ExecuteNetwork/ExecuteNetworkProgramOptions.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetworkProgramOptions.cpp
@@ -1,10 +1,11 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include "ExecuteNetworkProgramOptions.hpp"
 #include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
+#include "InferenceTest.hpp"
 
 #include <armnn/BackendRegistry.hpp>
 #include <armnn/Exceptions.hpp>
@@ -50,6 +51,8 @@ void CheckOptionDependency(const cxxopts::ParseResult& result,
 
 void CheckOptionDependencies(const cxxopts::ParseResult& result)
 {
+    CheckOptionDependency(result, "model-path", "model-format");
+    CheckOptionDependency(result, "input-tensor-shape", "model-path");
     CheckOptionDependency(result, "tuning-level", "tuning-path");
 }
 
@@ -116,8 +119,10 @@ void CheckRequiredOptions(const cxxopts::ParseResult& result)
 
     // For each option in option-group "a) Required
     std::vector<std::string> requiredOptions{"compute",
-                                             "model-path"
-                                             };
+                                             "model-format",
+                                             "model-path",
+                                             "input-name",
+                                             "output-name"};
 
     bool requiredMissing = false;
     for(auto const&  str : requiredOptions)
@@ -139,39 +144,13 @@ void CheckForDeprecatedOptions(const cxxopts::ParseResult& result)
     if(result.count("simultaneous-iterations") > 0)
     {
         ARMNN_LOG(warning) << "DEPRECATED: The program option 'simultaneous-iterations' is deprecated and will be "
-                              "removed soon. Please use the option '\"P, enable-thread-pool\"' instead.";
+                              "removed soon. Please use the option 'iterations' combined with 'concurrent' instead.";
     }
     if(result.count("armnn-tflite-delegate") > 0)
     {
         ARMNN_LOG(warning) << "DEPRECATED: The program option 'armnn-tflite-delegate' is deprecated and will be "
                               "removed soon. Please use the option 'tflite-executor' instead.";
     }
-    if(result.count("concurrent") > 0)
-    {
-        ARMNN_LOG(warning) << "DEPRECATED: The program option 'concurrent' is deprecated and will be "
-                              "removed soon. Please use the option '\"P, enable-thread-pool\"' instead.";
-    }
-    if(result.count("input-type") > 0)
-    {
-        ARMNN_LOG(warning) << "DEPRECATED: The program option 'input-type' is deprecated and will be "
-                              "removed soon. The input-types are now automatically set.";
-    }
-    if(result.count("output-type") > 0)
-    {
-        ARMNN_LOG(warning) << "DEPRECATED: The program option 'output-type' is deprecated and will be "
-                              "removed soon. The output-types are now automatically set.";
-    }
-    if(result.count("output-name") > 0)
-    {
-        ARMNN_LOG(warning) << "DEPRECATED: The program option 'output-name' is deprecated and will be "
-                              "removed soon. The output-names are now automatically set.";
-    }
-    if(result.count("model-format") > 0)
-    {
-        ARMNN_LOG(warning) << "DEPRECATED: The program option 'input-name' is deprecated and will be "
-                              "removed soon. The model-format are now automatically set.";
-    }
-
 }
 
 void ProgramOptions::ValidateExecuteNetworkParams()
@@ -208,9 +187,7 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  cxxopts::value<std::vector<std::string>>())
 
                 ("f,model-format",
-                 "armnn-binary, onnx-binary, onnx-text, tflite-binary"
-                 "DEPRECATED: The program option 'input-name' is deprecated and will be "
-                 "removed soon. The model-format are now automatically set.",
+                 "armnn-binary, onnx-binary, onnx-text, tflite-binary",
                  cxxopts::value<std::string>())
 
                 ("m,model-path",
@@ -218,13 +195,11 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  cxxopts::value<std::string>(m_ExNetParams.m_ModelPath))
 
                 ("i,input-name",
-                 "Identifier of the input tensors in the network separated by comma."
-                 "This option is not required, but can be used to set the order of inputs",
+                 "Identifier of the input tensors in the network separated by comma.",
                  cxxopts::value<std::string>())
 
                 ("o,output-name",
-                 "Identifier of the output tensors in the network separated by comma."
-                 "This option is not required, but can be used to set the order of outputs",
+                 "Identifier of the output tensors in the network separated by comma.",
                  cxxopts::value<std::string>());
 
         m_CxxOptions.add_options("b) General")
@@ -233,16 +208,10 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  "If left empty (the default), dynamic backends will not be used.",
                  cxxopts::value<std::string>(m_RuntimeOptions.m_DynamicBackendsPath))
 
-                ("P, thread-pool-size",
-                 "Run the network using the Arm NN thread pool with the number of threads provided. ",
-                 cxxopts::value<size_t>(m_ExNetParams.m_ThreadPoolSize)->default_value("0"))
-
                 ("n,concurrent",
                  "This option is for Arm NN internal asynchronous testing purposes. "
                  "False by default. If set to true will use std::launch::async or the Arm NN thread pool, "
-                 "if 'thread-pool-size' is greater than 0, for asynchronous execution."
-                 "DEPRECATED: The program option 'concurrent' is deprecated and will be "
-                 "removed soon. Please use the option '\"P, enable-thread-pool\"' instead.",
+                 "if 'thread-pool-size' is greater than 0, for asynchronous execution.",
                  cxxopts::value<bool>(m_ExNetParams.m_Concurrent)->default_value("false")->implicit_value("true"))
 
                 ("d,input-tensor-data",
@@ -266,7 +235,7 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  cxxopts::value<bool>(m_ExNetParams.m_AllowExpandedDims)->default_value("false")
                  ->implicit_value("true"))
 
-                ("I,iterations",
+                ("iterations",
                  "Number of iterations to run the network for, default is set to 1. "
                  "If you wish to run the model with different input data for every execution you can do so by "
                  "supplying more input file paths to the 'input-tensor-data' option. "
@@ -303,7 +272,6 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  "If unset, default to not quantized. Accepted values (true or false)"
                  " (Not available when executing ArmNNTfLiteDelegate or TfliteInterpreter)",
                  cxxopts::value<bool>(m_ExNetParams.m_QuantizeInput)->default_value("false")->implicit_value("true"))
-
                 ("r,threshold-time",
                  "Threshold time is the maximum allowed time for inference measured in milliseconds. If the actual "
                  "inference time is greater than the threshold time, the test will fail. By default, no threshold "
@@ -333,17 +301,13 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                 ("y,input-type",
                  "The type of the input tensors in the network separated by comma. "
                  "If unset, defaults to \"float\" for all defined inputs. "
-                 "Accepted values (float, int, qasymms8 or qasymmu8)."
-                 "DEPRECATED: The program option 'input-type' is deprecated and will be "
-                 "removed soon. The input-types are now automatically set.",
+                 "Accepted values (float, int, qasymms8 or qasymmu8).",
                  cxxopts::value<std::string>())
 
                 ("z,output-type",
                  "The type of the output tensors in the network separated by comma. "
                  "If unset, defaults to \"float\" for all defined outputs. "
-                 "Accepted values (float, int,  qasymms8 or qasymmu8)."
-                 "DEPRECATED: The program option 'output-type' is deprecated and will be "
-                 "removed soon. The input-types are now automatically set.",
+                 "Accepted values (float, int,  qasymms8 or qasymmu8).",
                  cxxopts::value<std::string>())
 
                 ("T,tflite-executor",
@@ -353,21 +317,23 @@ ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                  "tflite is the TfliteInterpreter",
                  cxxopts::value<std::string>()->default_value("parser"))
 
-                ("C, compare-output",
-                "Number of Arm NN threads to use when running the network asynchronously via the Arm NN thread pool. "
-                "The default is set to 0 which equals disabled. If 'thread-pool-size' is greater than 0 the "
-                "'concurrent' option is automatically set to true.",
-                cxxopts::value<std::string>(m_ExNetParams.m_ComparisonFile))
+                ("D,armnn-tflite-delegate",
+                 "Enable Arm NN TfLite delegate. "
+                 "DEPRECATED: This option is deprecated please use tflite-executor instead",
+                 cxxopts::value<bool>(m_ExNetParams.m_EnableDelegate)->default_value("false")->implicit_value("true"))
 
-                ("B, compare-output-with-backend",
-                 "Compare the output of the network with a different backend.",
-                 cxxopts::value<std::vector<std::string>>())
-
-                ("A, compare-with-tflite",
-                 "Compare the outout of the network with the tflite ref model.",
-                 cxxopts::value<bool>(m_ExNetParams.m_CompareWithTflite)->default_value("false")
-                                                                        ->implicit_value("true"));
+                ("simultaneous-iterations",
+                 "Number of simultaneous iterations to async-run the network for, default is set to 1 (disabled). "
+                 "When thread-pool-size is set the Arm NN thread pool is used. Otherwise std::launch::async is used."
+                 "DEPRECATED: This option is deprecated and will be removed soon. "
+                 "Please use the option 'iterations' combined with 'concurrent' instead.",
+                 cxxopts::value<size_t>(m_ExNetParams.m_SimultaneousIterations)->default_value("1"))
 
+                ("thread-pool-size",
+                 "Number of Arm NN threads to use when running the network asynchronously via the Arm NN thread pool. "
+                 "The default is set to 0 which equals disabled. If 'thread-pool-size' is greater than 0 the "
+                 "'concurrent' option is automatically set to true.",
+                 cxxopts::value<size_t>(m_ExNetParams.m_ThreadPoolSize)->default_value("0"));
 
         m_CxxOptions.add_options("c) Optimization")
                 ("bf16-turbo-mode",
@@ -503,22 +469,21 @@ void ProgramOptions::ParseOptions(int ac, const char* av[])
     CheckOptionDependencies(m_CxxResult);
     CheckForDeprecatedOptions(m_CxxResult);
 
-    if ((m_ExNetParams.m_OutputDetailsToStdOut ||
-         m_ExNetParams.m_OutputDetailsOnlyToStdOut) &&
-        !m_ExNetParams.m_EnableProfiling)
-    {
-        throw cxxopts::OptionParseException("You must enable profiling if you would like to output layer details");
-    }
-
     // Some options can't be assigned directly because they need some post-processing:
     auto computeDevices = GetOptionValue<std::vector<std::string>>("compute", m_CxxResult);
     m_ExNetParams.m_ComputeDevices = GetBackendIDs(computeDevices);
+    m_ExNetParams.m_ModelFormat =
+            armnn::stringUtils::StringTrimCopy(GetOptionValue<std::string>("model-format", m_CxxResult));
     m_ExNetParams.m_InputNames =
             ParseStringList(GetOptionValue<std::string>("input-name", m_CxxResult), ",");
     m_ExNetParams.m_InputTensorDataFilePaths =
             ParseStringList(GetOptionValue<std::string>("input-tensor-data", m_CxxResult), ",");
     m_ExNetParams.m_OutputNames =
             ParseStringList(GetOptionValue<std::string>("output-name", m_CxxResult), ",");
+    m_ExNetParams.m_InputTypes =
+            ParseStringList(GetOptionValue<std::string>("input-type", m_CxxResult), ",");
+    m_ExNetParams.m_OutputTypes =
+            ParseStringList(GetOptionValue<std::string>("output-type", m_CxxResult), ",");
     m_ExNetParams.m_OutputTensorFiles =
             ParseStringList(GetOptionValue<std::string>("write-outputs-to-file", m_CxxResult), ",");
     m_ExNetParams.m_GenerateTensorData =
@@ -552,13 +517,13 @@ void ProgramOptions::ParseOptions(int ac, const char* av[])
     {
         m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate;
     }
-
-    // Set concurrent to true if the user expects to run inferences asynchronously
-    if (m_ExNetParams.m_Concurrent)
+    if (m_ExNetParams.m_SimultaneousIterations > 1)
     {
-       m_ExNetParams.m_ThreadPoolSize = 1;
+        m_ExNetParams.m_Iterations = m_ExNetParams.m_SimultaneousIterations;
+        m_ExNetParams.m_Concurrent = true;
     }
 
+    // Set concurrent to true if the user expects to run inferences asynchronously
     if (m_ExNetParams.m_ThreadPoolSize > 0)
     {
         m_ExNetParams.m_Concurrent = true;
@@ -578,7 +543,7 @@ void ProgramOptions::ParseOptions(int ac, const char* av[])
             std::vector<unsigned int> dims = ParseArray(ss);
 
             m_ExNetParams.m_InputTensorShapes.push_back(
-                    armnn::TensorShape{static_cast<unsigned int>(dims.size()), dims.data()});
+                    std::make_unique<armnn::TensorShape>(static_cast<unsigned int>(dims.size()), dims.data()));
         }
     }
 
@@ -603,12 +568,5 @@ void ProgramOptions::ParseOptions(int ac, const char* av[])
     }
 
     ValidateRuntimeOptions();
-
-    auto comparisonComputDevices = GetOptionValue<std::vector<std::string>>("compare-output-with-backend", m_CxxResult);
-
-    if (!comparisonComputDevices.empty())
-    {
-        m_ExNetParams.m_ComparisonComputeDevices = GetBackendIDs(comparisonComputDevices);
-    }
 }
 
diff --git a/tests/ExecuteNetwork/IExecutor.hpp b/tests/ExecuteNetwork/IExecutor.hpp
deleted file mode 100644
index 4ed6cbde84..0000000000
--- a/tests/ExecuteNetwork/IExecutor.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-#include <vector>
-
-/// IExecutor executes a network
-class IExecutor
-{
-public:
-    /// Execute the given network
-    /// @return std::vector<const void*> A type erased vector of the outputs,
-    /// that can be compared with the output of another IExecutor
-    virtual std::vector<const void*> Execute()  = 0;
-    /// Print available information about the network
-    virtual void PrintNetworkInfo() = 0;
-    /// Compare the output with the result of another IExecutor
-    virtual void CompareAndPrintResult(std::vector<const void*> otherOutput) = 0;
-    virtual ~IExecutor(){};
-};
diff --git a/tests/ExecuteNetwork/TfliteExecutor.cpp b/tests/ExecuteNetwork/TfliteExecutor.cpp
deleted file mode 100644
index f7a3068d7b..0000000000
--- a/tests/ExecuteNetwork/TfliteExecutor.cpp
+++ /dev/null
@@ -1,251 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "TfliteExecutor.hpp"
-
-TfLiteExecutor::TfLiteExecutor(const ExecuteNetworkParams& params) : m_Params(params)
-{
-    std::unique_ptr<tflite::FlatBufferModel> model =
-            tflite::FlatBufferModel::BuildFromFile(m_Params.m_ModelPath.c_str());
-
-    m_TfLiteInterpreter =  std::make_unique<Interpreter>();
-    tflite::ops::builtin::BuiltinOpResolver resolver;
-
-    tflite::InterpreterBuilder builder(*model, resolver);
-    builder(&m_TfLiteInterpreter);
-    m_TfLiteInterpreter->AllocateTensors();
-
-    int status;
-    if (m_Params.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate)
-    {
-        // Create the Armnn Delegate
-        // Populate a DelegateOptions from the ExecuteNetworkParams.
-        armnnDelegate::DelegateOptions delegateOptions = m_Params.ToDelegateOptions();
-        delegateOptions.SetExternalProfilingParams(delegateOptions.GetExternalProfilingParams());
-
-        std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
-                theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
-                                 armnnDelegate::TfLiteArmnnDelegateDelete);
-        // Register armnn_delegate to TfLiteInterpreter
-        status = m_TfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate));
-        if (status == kTfLiteError)
-        {
-            LogAndThrow("Could not register ArmNN TfLite Delegate to TfLiteInterpreter");
-        }
-    }
-    else
-    {
-        std::cout << "Running on TfLite without ArmNN delegate\n";
-    }
-
-    armnn::Optional<std::string> dataFile = m_Params.m_GenerateTensorData
-                                            ? armnn::EmptyOptional()
-                                            : armnn::MakeOptional<std::string>(m_Params.m_InputTensorDataFilePaths[0]);
-
-    const size_t numInputs = m_Params.m_InputNames.size();
-
-    for(unsigned int inputIndex = 0; inputIndex < numInputs; ++inputIndex)
-    {
-        int input = m_TfLiteInterpreter->inputs()[inputIndex];
-
-        TfLiteIntArray* inputDims = m_TfLiteInterpreter->tensor(input)->dims;
-
-        unsigned int inputSize = 1;
-        for (unsigned int dim = 0; dim < static_cast<unsigned int>(inputDims->size); ++dim)
-        {
-            inputSize *= inputDims->data[dim];
-        }
-
-        const auto& inputName = m_TfLiteInterpreter->input_tensor(input)->name;
-        const auto& dataType = m_TfLiteInterpreter->input_tensor(input)->type;
-
-        switch (dataType)
-        {
-            case kTfLiteFloat32:
-            {
-                auto inputData = m_TfLiteInterpreter->typed_tensor<float>(input);
-                PopulateTensorWithData(inputData, inputSize, dataFile, inputName);
-                break;
-            }
-            case kTfLiteInt32:
-            {
-                auto inputData = m_TfLiteInterpreter->typed_tensor<int>(input);
-                PopulateTensorWithData(inputData, inputSize, dataFile, inputName);
-                break;
-            }
-            case kTfLiteUInt8:
-            {
-                auto inputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(input);
-                PopulateTensorWithData(inputData, inputSize, dataFile, inputName);
-                break;
-            }
-            case kTfLiteInt16:
-            {
-                auto inputData = m_TfLiteInterpreter->typed_tensor<int16_t>(input);
-                PopulateTensorWithData(inputData, inputSize, dataFile, inputName);
-                break;
-            }
-            case kTfLiteInt8:
-            {
-                auto inputData = m_TfLiteInterpreter->typed_tensor<int8_t>(input);
-                PopulateTensorWithData(inputData, inputSize, dataFile, inputName);
-                break;
-            }
-            default:
-            {
-                LogAndThrow("Unsupported input tensor data type");
-            }
-        }
-    }
-}
-
-std::vector<const void *> TfLiteExecutor::Execute()
-{
-    int status = 0;
-    std::vector<const void*> results;
-    for (size_t x = 0; x < m_Params.m_Iterations; x++)
-    {
-        // Start timer to record inference time in milliseconds.
-        const auto start_time = armnn::GetTimeNow();
-        // Run the inference
-        status = m_TfLiteInterpreter->Invoke();
-        const auto duration = armnn::GetTimeDuration(start_time);
-
-        if (m_Params.m_DontPrintOutputs || m_Params.m_ReuseBuffers)
-        {
-            break;
-        }
-        // Print out the output
-        for (unsigned int outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex)
-        {
-            auto tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex];
-            TfLiteIntArray* outputDims = m_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 (!m_Params.m_OutputTensorFiles.empty())
-            {
-                outputTensorFile = fopen(m_Params.m_OutputTensorFiles[outputIndex].c_str(), "w");
-                if (outputTensorFile == NULL)
-                {
-                    LogAndThrow("Specified output tensor file, \"" + m_Params.m_OutputTensorFiles[outputIndex] +
-                                "\", cannot be created. Defaulting to stdout. Error was: " + std::strerror(errno));
-                }
-                else
-                {
-                    ARMNN_LOG(info) << "Writing output " << outputIndex << "' of iteration: " << x+1 << " to file: '"
-                                    << m_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 << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name << ": ";
-            results.push_back(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation);
-
-            switch (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type)
-            {
-
-                case kTfLiteFloat32:
-                {
-                    auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
-
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%f ", tfLiteDelageOutputData[i]);
-                    }
-                    break;
-                }
-                case kTfLiteInt32:
-                {
-                    auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<int32_t>(tfLiteDelegateOutputId);
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]);
-                    }
-                    break;
-                }
-                case kTfLiteUInt8:
-                {
-                    auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(tfLiteDelegateOutputId);
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%u ", tfLiteDelageOutputData[i]);
-                    }
-                    break;
-                }
-                case kTfLiteInt8:
-                {
-                    auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<int8_t>(tfLiteDelegateOutputId);
-                    for (int i = 0; i < outputSize; ++i)
-                    {
-                        fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]);
-                    }
-                    break;
-                }
-                default:
-                {
-                    LogAndThrow("Unsupported output type");
-                }
-            }
-
-            std::cout << std::endl;
-        }
-        CheckInferenceTimeThreshold(duration, m_Params.m_ThresholdTime);
-    }
-
-    std::cout << status;
-    return results;
-}
-
-void TfLiteExecutor::CompareAndPrintResult(std::vector<const void*> otherOutput)
-{
-    for (unsigned int outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex)
-    {
-        auto tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex];
-        float result = 0;
-        switch (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type)
-        {
-            case kTfLiteFloat32:
-            {
-                result =  ComputeRMSE<float>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,
-                                                  otherOutput[outputIndex],
-                                                  m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);
-
-                break;
-            }
-            case kTfLiteInt32:
-            {
-                result =  ComputeRMSE<int32_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,
-                                                    otherOutput[outputIndex],
-                                                    m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);
-                break;
-            }
-            case kTfLiteUInt8:
-            {
-                result =  ComputeRMSE<uint8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,
-                                                    otherOutput[outputIndex],
-                                                    m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);
-                break;
-            }
-            case kTfLiteInt8:
-            {
-                result =  ComputeRMSE<int8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,
-                                                    otherOutput[outputIndex],
-                                                    m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);
-                break;
-            }
-            default:
-            {
-            }
-        }
-
-        std::cout << "RMSE of "
-                  << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name
-                  << ": " << result << std::endl;
-    }
-};
diff --git a/tests/ExecuteNetwork/TfliteExecutor.hpp b/tests/ExecuteNetwork/TfliteExecutor.hpp
deleted file mode 100644
index 009c79488e..0000000000
--- a/tests/ExecuteNetwork/TfliteExecutor.hpp
+++ /dev/null
@@ -1,35 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "IExecutor.hpp"
-#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
-#include "ExecuteNetworkProgramOptions.hpp"
-#include "armnn/utility/NumericCast.hpp"
-#include "armnn/utility/Timer.hpp"
-
-#include <armnn_delegate.hpp>
-#include <DelegateOptions.hpp>
-
-#include <tensorflow/lite/c/common.h>
-#include <tensorflow/lite/interpreter.h>
-#include <tensorflow/lite/kernels/register.h>
-
-using namespace tflite;
-class  TfLiteExecutor : public IExecutor
-{
-public:
-    TfLiteExecutor(const ExecuteNetworkParams& m_Params);
-
-    std::vector<const void *> Execute() override;
-    void PrintNetworkInfo() override{};
-    void CompareAndPrintResult(std::vector<const void*> otherOutput) override;
-
-private:
-    std::unique_ptr<tflite::FlatBufferModel> m_Model;
-    const ExecuteNetworkParams& m_Params;
-    std::unique_ptr<Interpreter> m_TfLiteInterpreter;
-};
-
diff --git a/tests/InferenceModel.hpp b/tests/InferenceModel.hpp
index 268f60301c..93716e1a6f 100644
--- a/tests/InferenceModel.hpp
+++ b/tests/InferenceModel.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -15,7 +15,6 @@
 #include <armnn/utility/NumericCast.hpp>
 
 #include <armnnUtils/TContainer.hpp>
-#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"
 
 #include <common/include/ProfilingGuid.hpp>
 
@@ -47,6 +46,40 @@
 #include <vector>
 #include <type_traits>
 
+namespace
+{
+
+inline bool CheckRequestedBackendsAreValid(const std::vector<armnn::BackendId>& backendIds,
+                                           armnn::Optional<std::string&> invalidBackendIds = armnn::EmptyOptional())
+{
+    if (backendIds.empty())
+    {
+        return false;
+    }
+
+    armnn::BackendIdSet validBackendIds = armnn::BackendRegistryInstance().GetBackendIds();
+
+    bool allValid = true;
+    for (const auto& backendId : backendIds)
+    {
+        if (std::find(validBackendIds.begin(), validBackendIds.end(), backendId) == validBackendIds.end())
+        {
+            allValid = false;
+            if (invalidBackendIds)
+            {
+                if (!invalidBackendIds.value().empty())
+                {
+                    invalidBackendIds.value() += ", ";
+                }
+                invalidBackendIds.value() += backendId;
+            }
+        }
+    }
+    return allValid;
+}
+
+} // anonymous namespace
+
 namespace InferenceModelInternal
 {
 using BindingPointInfo = armnn::BindingPointInfo;
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
index 2d3567bd24..6c74aaa6ed 100644
--- a/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
@@ -1,12 +1,110 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include "NetworkExecutionUtils.hpp"
 
 #include <armnnUtils/Filesystem.hpp>
-#include <iterator>
+#include <InferenceTest.hpp>
+#include <ResolveType.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
+
+template<armnn::DataType NonQuantizedType>
+auto ParseDataArray(std::istream& stream);
+
+template<armnn::DataType QuantizedType>
+auto ParseDataArray(std::istream& stream,
+                    const float& quantizationScale,
+                    const int32_t& quantizationOffset);
+
+template<>
+auto ParseDataArray<armnn::DataType::Float32>(std::istream& stream)
+{
+    return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::Signed32>(std::istream& stream)
+{
+    return ParseArrayImpl<int>(stream, [](const std::string& s) { return std::stoi(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmS8>(std::istream& stream)
+{
+    return ParseArrayImpl<int8_t>(stream,
+                                  [](const std::string& s) { return armnn::numeric_cast<int8_t>(std::stoi(s)); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)
+{
+    return ParseArrayImpl<uint8_t>(stream,
+                                   [](const std::string& s) { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
+}
+
+
+template<>
+auto ParseDataArray<armnn::DataType::QSymmS8>(std::istream& stream)
+{
+    return ParseArrayImpl<int8_t>(stream,
+                                   [](const std::string& s) { return armnn::numeric_cast<int8_t>(std::stoi(s)); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmS8>(std::istream& stream,
+                                               const float& quantizationScale,
+                                               const int32_t& quantizationOffset)
+{
+    return ParseArrayImpl<int8_t>(stream,
+                                  [&quantizationScale, &quantizationOffset](const std::string& s)
+                                  {
+                                      return armnn::numeric_cast<int8_t>(
+                                              armnn::Quantize<int8_t>(std::stof(s),
+                                                                      quantizationScale,
+                                                                      quantizationOffset));
+                                  });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,
+                                               const float& quantizationScale,
+                                               const int32_t& quantizationOffset)
+{
+    return ParseArrayImpl<uint8_t>(stream,
+                                   [&quantizationScale, &quantizationOffset](const std::string& s)
+                                   {
+                                       return armnn::numeric_cast<uint8_t>(
+                                               armnn::Quantize<uint8_t>(std::stof(s),
+                                                                        quantizationScale,
+                                                                        quantizationOffset));
+                                   });
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+std::vector<T> GenerateDummyTensorData(unsigned int numElements)
+{
+    return std::vector<T>(numElements, static_cast<T>(0));
+}
+
+
+std::vector<unsigned int> ParseArray(std::istream& stream)
+{
+    return ParseArrayImpl<unsigned int>(
+            stream,
+            [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
+}
+
 std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter)
 {
     std::stringstream stream(inputString);
@@ -14,27 +112,189 @@ std::vector<std::string> ParseStringList(const std::string& inputString, const c
         return armnn::stringUtils::StringTrimCopy(s); }, delimiter);
 }
 
-bool CheckInferenceTimeThreshold(const std::chrono::duration<double, std::milli>& duration,
-                                 const double& thresholdTime)
+
+TensorPrinter::TensorPrinter(const std::string& binding,
+                             const armnn::TensorInfo& info,
+                             const std::string& outputTensorFile,
+                             bool dequantizeOutput,
+                             const bool printToConsole)
+                             : m_OutputBinding(binding)
+                             , m_Scale(info.GetQuantizationScale())
+                             , m_Offset(info.GetQuantizationOffset())
+                             , m_OutputTensorFile(outputTensorFile)
+                             , m_DequantizeOutput(dequantizeOutput)
+                             , m_PrintToConsole(printToConsole) {}
+
+void TensorPrinter::operator()(const std::vector<float>& values)
+{
+    if (m_PrintToConsole)
+    {
+        std::cout << m_OutputBinding << ": ";
+        ForEachValue(values, [](float value)
+        {
+            printf("%f ", value);
+        });
+        printf("\n");
+    }
+    WriteToFile(values);
+}
+
+void TensorPrinter::operator()(const std::vector<uint8_t>& values)
 {
-    ARMNN_LOG(info) << "\nInference 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(m_DequantizeOutput)
     {
-        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)
+        auto& scale = m_Scale;
+        auto& offset = m_Offset;
+        std::vector<float> dequantizedValues;
+        ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)
+        {
+            auto dequantizedValue = armnn::Dequantize(value, scale, offset);
+            dequantizedValues.push_back(dequantizedValue);
+        });
+
+        if (m_PrintToConsole)
         {
-            std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
-            ARMNN_LOG(fatal) << errorMessage;
-            return false;
+            std::cout << m_OutputBinding << ": ";
+            ForEachValue(dequantizedValues, [](float value)
+            {
+                printf("%f ", value);
+            });
+            printf("\n");
         }
+
+        WriteToFile(dequantizedValues);
     }
-    return true;
+    else
+    {
+        const std::vector<int> intValues(values.begin(), values.end());
+        operator()(intValues);
+    }
+}
+
+void TensorPrinter::operator()(const std::vector<int8_t>& values)
+{
+    if (m_PrintToConsole)
+    {
+        std::cout << m_OutputBinding << ": ";
+        ForEachValue(values, [](int8_t value)
+        {
+            printf("%d ", value);
+        });
+        printf("\n");
+    }
+    WriteToFile(values);
+}
+
+void TensorPrinter::operator()(const std::vector<int>& values)
+{
+    if (m_PrintToConsole)
+    {
+        std::cout << m_OutputBinding << ": ";
+        ForEachValue(values, [](int value)
+        {
+            printf("%d ", value);
+        });
+        printf("\n");
+    }
+    WriteToFile(values);
+}
+
+template<typename Container, typename Delegate>
+void TensorPrinter::ForEachValue(const Container& c, Delegate delegate)
+{
+    for (const auto& value : c)
+    {
+        delegate(value);
+    }
+}
+
+template<typename T>
+void TensorPrinter::WriteToFile(const std::vector<T>& values)
+{
+    if (!m_OutputTensorFile.empty())
+    {
+        std::ofstream outputTensorFile;
+        outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);
+        if (outputTensorFile.is_open())
+        {
+            outputTensorFile << m_OutputBinding << ": ";
+            std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, " "));
+        }
+        else
+        {
+            ARMNN_LOG(info) << "Output Tensor File: " << m_OutputTensorFile << " could not be opened!";
+        }
+        outputTensorFile.close();
+    }
+}
+
+void PopulateTensorWithData(armnnUtils::TContainer& tensorData,
+                            unsigned int numElements,
+                            const std::string& dataTypeStr,
+                            const armnn::Optional<QuantizationParams>& qParams,
+                            const armnn::Optional<std::string>& dataFile)
+{
+    const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
+    const bool quantizeData = qParams.has_value();
+
+    std::ifstream inputTensorFile;
+    if (readFromFile)
+    {
+        inputTensorFile = std::ifstream(dataFile.value());
+    }
+
+    if (dataTypeStr.compare("float") == 0)
+    {
+        if (quantizeData)
+        {
+            const float qScale  = qParams.value().first;
+            const int   qOffset = qParams.value().second;
+
+            tensorData = readFromFile ?
+                         ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :
+                         GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+        }
+        else
+        {
+            tensorData = readFromFile ?
+                         ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :
+                         GenerateDummyTensorData<armnn::DataType::Float32>(numElements);
+        }
+    }
+    else if (dataTypeStr.compare("int") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);
+    }
+    else if (dataTypeStr.compare("qsymms8") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::QSymmS8>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::QSymmS8>(numElements);
+    }
+    else if (dataTypeStr.compare("qasymm8") == 0 || dataTypeStr.compare("qasymmu8") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+    }
+    else if (dataTypeStr.compare("qasymms8") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::QAsymmS8>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::QAsymmS8>(numElements);
+    }
+    else
+    {
+        std::string errorMessage = "Unsupported tensor data type " + dataTypeStr;
+        ARMNN_LOG(fatal) << errorMessage;
+
+        inputTensorFile.close();
+        throw armnn::Exception(errorMessage);
+    }
+
+    inputTensorFile.close();
 }
 
 bool ValidatePath(const std::string& file, const bool expectFile)
@@ -52,13 +312,6 @@ bool ValidatePath(const std::string& file, const bool expectFile)
     return true;
 }
 
-std::vector<unsigned int> ParseArray(std::istream& stream)
-{
-    return ParseArrayImpl<unsigned int>(
-            stream,
-            [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
-}
-
 bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile)
 {
     bool allPathsValid = true;
@@ -72,9 +325,5 @@ bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFil
     return allPathsValid;
 }
 
-void LogAndThrow(std::string eMsg)
-{
-    ARMNN_LOG(error) << eMsg;
-    throw armnn::Exception(eMsg);
-}
+
 
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
index 14d7fe5551..bc2868ab35 100644
--- a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
@@ -1,83 +1,63 @@
 //
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #pragma once
 
+#include <armnn/IRuntime.hpp>
+#include <armnn/Types.hpp>
 #include <armnn/Logging.hpp>
 #include <armnn/utility/StringUtils.hpp>
-#include <armnn/utility/NumericCast.hpp>
-#include <armnn/BackendRegistry.hpp>
+#include <armnnUtils/TContainer.hpp>
 
 #include <iostream>
 #include <fstream>
-#include <iomanip>
-#include <iterator>
 
-/**
- * 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);
-
-inline bool CheckRequestedBackendsAreValid(const std::vector<armnn::BackendId>& backendIds,
-                                           armnn::Optional<std::string&> invalidBackendIds = armnn::EmptyOptional())
-{
-    if (backendIds.empty())
-    {
-        return false;
-    }
-
-    armnn::BackendIdSet validBackendIds = armnn::BackendRegistryInstance().GetBackendIds();
-
-    bool allValid = true;
-    for (const auto& backendId : backendIds)
-    {
-        if (std::find(validBackendIds.begin(), validBackendIds.end(), backendId) == validBackendIds.end())
-        {
-            allValid = false;
-            if (invalidBackendIds)
-            {
-                if (!invalidBackendIds.value().empty())
-                {
-                    invalidBackendIds.value() += ", ";
-                }
-                invalidBackendIds.value() += backendId;
-            }
-        }
-    }
-    return allValid;
-}
 
 std::vector<unsigned int> ParseArray(std::istream& stream);
 
 /// Splits a given string at every accurance of delimiter into a vector of string
 std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter);
 
-/// Dequantize an array of a given type
-/// @param array Type erased array to dequantize
-/// @param numElements Elements in the array
-/// @param array Type erased array to dequantize
-template <typename T>
-std::vector<float> DequantizeArray(const void* array, unsigned int numElements, float scale, int32_t offset)
+struct TensorPrinter
 {
-    const T* quantizedArray = reinterpret_cast<const T*>(array);
-    std::vector<float> dequantizedVector;
-    dequantizedVector.reserve(numElements);
-    for (unsigned int i = 0; i < numElements; ++i)
-    {
-        float f = armnn::Dequantize(*(quantizedArray + i), scale, offset);
-        dequantizedVector.push_back(f);
-    }
-    return dequantizedVector;
-}
+    TensorPrinter(const std::string& binding,
+                  const armnn::TensorInfo& info,
+                  const std::string& outputTensorFile,
+                  bool dequantizeOutput,
+                  bool printToConsole = true);
+
+    void operator()(const std::vector<float>& values);
+
+    void operator()(const std::vector<uint8_t>& values);
+
+    void operator()(const std::vector<int>& values);
+
+    void operator()(const std::vector<int8_t>& values);
+
+private:
+    template<typename Container, typename Delegate>
+    void ForEachValue(const Container& c, Delegate delegate);
+
+    template<typename T>
+    void WriteToFile(const std::vector<T>& values);
+
+    std::string m_OutputBinding;
+    float m_Scale;
+    int m_Offset;
+    std::string m_OutputTensorFile;
+    bool m_DequantizeOutput;
+    bool m_PrintToConsole;
+};
+
+using QuantizationParams = std::pair<float, int32_t>;
 
-void LogAndThrow(std::string eMsg);
+void PopulateTensorWithData(armnnUtils::TContainer& tensorData,
+                            unsigned int numElements,
+                            const std::string& dataTypeStr,
+                            const armnn::Optional<QuantizationParams>& qParams,
+                            const armnn::Optional<std::string>& dataFile);
 
 /**
  * Verifies if the given string is a valid path. Reports invalid paths to std::err.
@@ -95,152 +75,6 @@ bool ValidatePath(const std::string& file, const bool expectFile);
  * */
 bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile);
 
-/// Returns a function of read the given type as a string
-template <typename Integer, typename std::enable_if_t<std::is_integral<Integer>::value>* = nullptr>
-std::function<Integer(const std::string&)> GetParseElementFunc()
-{
-    return [](const std::string& s) { return armnn::numeric_cast<Integer>(std::stoi(s)); };
-}
-
-template <typename Float, std::enable_if_t<std::is_floating_point<Float>::value>* = nullptr>
-std::function<Float(const std::string&)> GetParseElementFunc()
-{
-    return [](const std::string& s) { return std::stof(s); };
-}
-
-template <typename T>
-void PopulateTensorWithData(T* tensor,
-                            const unsigned int numElements,
-                            const armnn::Optional<std::string>& dataFile,
-                            const std::string& inputName)
-{
-    const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
-
-    std::ifstream inputTensorFile;
-    if (!readFromFile)
-    {
-        std::fill(tensor, tensor + numElements, 0);
-        return;
-    }
-    else
-    {
-        inputTensorFile = std::ifstream(dataFile.value());
-    }
-
-    auto parseElementFunc = GetParseElementFunc<T>();
-    std::string line;
-    unsigned int index = 0;
-    while (std::getline(inputTensorFile, line))
-    {
-        std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, "\t ,:");
-        for (const std::string& token : tokens)
-        {
-            if (!token.empty()) // See https://stackoverflow.com/questions/10437406/
-            {
-                try
-                {
-                    if (index == numElements)
-                    {
-                        ARMNN_LOG(error) << "Number of elements: " << (index +1) << " in file \"" << dataFile.value()
-                                         << "\" does not match number of elements: " << numElements
-                                         << " for input \"" << inputName << "\".";
-                    }
-                    *(tensor + index) = parseElementFunc(token);
-                    index++;
-                }
-                catch (const std::exception&)
-                {
-                    ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored.";
-                }
-            }
-        }
-    }
-
-    if (index != numElements)
-    {
-        ARMNN_LOG(error) << "Number of elements: " << (index +1) << " in file \"" << inputName
-                         << "\" does not match number of elements: " << numElements
-                         << " for input \"" << inputName << "\".";
-    }
-}
-
-template<typename T>
-void WriteToFile(const std::string& outputTensorFileName,
-                 const std::string& outputName,
-                 const T* const array,
-                 const unsigned int numElements)
-{
-    std::ofstream outputTensorFile;
-    outputTensorFile.open(outputTensorFileName, std::ofstream::out | std::ofstream::trunc);
-    if (outputTensorFile.is_open())
-    {
-        outputTensorFile << outputName << ": ";
-        std::copy(array, array + numElements, std::ostream_iterator<T>(outputTensorFile, " "));
-    }
-    else
-    {
-        ARMNN_LOG(info) << "Output Tensor File: " << outputTensorFileName << " could not be opened!";
-    }
-    outputTensorFile.close();
-}
-
-struct OutputWriteInfo
-{
-    const armnn::Optional<std::string>& m_OutputTensorFile;
-    const std::string& m_OutputName;
-    const armnn::Tensor& m_Tensor;
-    const bool m_PrintTensor;
-};
-
-template <typename T>
-void PrintTensor(OutputWriteInfo& info, const char* formatString)
-{
-    const T* array = reinterpret_cast<const T*>(info.m_Tensor.GetMemoryArea());
-
-    if (info.m_OutputTensorFile.has_value())
-    {
-        WriteToFile(info.m_OutputTensorFile.value(),
-                    info.m_OutputName,
-                    array,
-                    info.m_Tensor.GetNumElements());
-    }
-
-    if (info.m_PrintTensor)
-    {
-        for (unsigned int i = 0; i < info.m_Tensor.GetNumElements(); i++)
-        {
-            printf(formatString, array[i]);
-        }
-    }
-}
-
-template <typename T>
-void PrintQuantizedTensor(OutputWriteInfo& info)
-{
-    std::vector<float> dequantizedValues;
-    auto tensor = info.m_Tensor;
-    dequantizedValues = DequantizeArray<T>(tensor.GetMemoryArea(),
-                                           tensor.GetNumElements(),
-                                           tensor.GetInfo().GetQuantizationScale(),
-                                           tensor.GetInfo().GetQuantizationOffset());
-
-    if (info.m_OutputTensorFile.has_value())
-    {
-        WriteToFile(info.m_OutputTensorFile.value(),
-                    info.m_OutputName,
-                    dequantizedValues.data(),
-                    tensor.GetNumElements());
-    }
-
-    if (info.m_PrintTensor)
-    {
-        std::for_each(dequantizedValues.begin(), dequantizedValues.end(), [&](float value)
-        {
-            printf("%f ", value);
-        });
-    }
-}
-
 template<typename T, typename TParseElementFunc>
 std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char* chars = "\t ,:")
 {
@@ -269,28 +103,21 @@ std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseEleme
     return result;
 }
 
-/// Compute the root-mean-square error (RMSE)
-/// @param expected
-/// @param actual
-/// @param size size of the tensor
-/// @return float the RMSE
-template<typename T>
-float ComputeRMSE(const void* expected, const void* actual, const size_t size)
+template <typename T, typename TParseElementFunc>
+void PopulateTensorWithDataGeneric(std::vector<T>& tensorData,
+                                   unsigned int numElements,
+                                   const armnn::Optional<std::string>& dataFile,
+                                   TParseElementFunc parseFunction)
 {
-    auto typedExpected = reinterpret_cast<const T*>(expected);
-    auto typedActual = reinterpret_cast<const T*>(actual);
-
-    T errorSum = 0;
+    const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
 
-    for (unsigned int i = 0; i < size; i++)
+    std::ifstream inputTensorFile;
+    if (readFromFile)
     {
-        if (std::abs(typedExpected[i] - typedActual[i]) != 0)
-        {
-            std::cout << "";
-        }
-        errorSum += std::pow(std::abs(typedExpected[i] - typedActual[i]), 2);
+        inputTensorFile = std::ifstream(dataFile.value());
     }
 
-    float rmse = std::sqrt(armnn::numeric_cast<float>(errorSum) / armnn::numeric_cast<float>(size / sizeof(T)));
-    return rmse;
-}
\ No newline at end of file
+    tensorData = readFromFile ?
+                 ParseArrayImpl<T>(inputTensorFile, parseFunction) :
+                 std::vector<T>(numElements, static_cast<T>(0));
+}