diff --git a/test/Tests.cpp b/test/Tests.cpp
index 0ab2908..37aece7 100644
--- a/test/Tests.cpp
+++ b/test/Tests.cpp
@@ -2,43 +2,18 @@
 // Copyright © 2017 Arm Ltd. All rights reserved.
 // See LICENSE file in the project root for full license information.
 //
-
 #define LOG_TAG "ArmnnDriverTests"
 #define BOOST_TEST_MODULE armnn_driver_tests
 #include <boost/test/unit_test.hpp>
 #include <log/log.h>
 
-#include "../ArmnnDriver.hpp"
-#include "../SystemPropertiesUtils.hpp"
-
-#include "OperationsUtils.h"
-
-#include <condition_variable>
-
-namespace android
-{
-namespace hardware
-{
-namespace neuralnetworks
-{
-namespace V1_0
-{
-
-std::ostream& operator<<(std::ostream& os, ErrorStatus stat)
-{
-   return os << static_cast<int>(stat);
-}
-
-}
-}
-}
-}
+#include "DriverTestHelpers.hpp"
 
 BOOST_AUTO_TEST_SUITE(DriverTests)
 
-using namespace armnn_driver;
-using namespace android::nn;
-using namespace android;
+using ArmnnDriver = armnn_driver::ArmnnDriver;
+using DriverOptions = armnn_driver::DriverOptions;
+using namespace driverTestHelpers;
 
 BOOST_AUTO_TEST_CASE(Init)
 {
@@ -73,904 +48,4 @@
     BOOST_TEST(cap.quantized8Performance.powerUsage > 0.f);
 }
 
-BOOST_AUTO_TEST_CASE(SystemProperties)
-{
-    // Test default value
-    {
-        auto p = __system_property_find("thisDoesNotExist");
-        BOOST_TEST((p == nullptr));
-
-        int defaultValue = ParseSystemProperty("thisDoesNotExist", -4);
-        BOOST_TEST((defaultValue == -4));
-    }
-
-    //  Test default value from bad data type
-    {
-        __system_property_set("thisIsNotFloat", "notfloat");
-        float defaultValue = ParseSystemProperty("thisIsNotFloat", 0.1f);
-        BOOST_TEST((defaultValue == 0.1f));
-    }
-
-    // Test fetching bool values
-    {
-        __system_property_set("myTestBool", "1");
-        bool b = ParseSystemProperty("myTestBool", false);
-        BOOST_TEST((b == true));
-    }
-    {
-        __system_property_set("myTestBool", "0");
-        bool b = ParseSystemProperty("myTestBool", true);
-        BOOST_TEST((b == false));
-    }
-
-    // Test fetching int
-    {
-        __system_property_set("myTestInt", "567");
-        int i = ParseSystemProperty("myTestInt", 890);
-        BOOST_TEST((i==567));
-    }
-
-    // Test fetching float
-    {
-        __system_property_set("myTestFloat", "1.2f");
-        float f = ParseSystemProperty("myTestFloat", 3.4f);
-        BOOST_TEST((f==1.2f));
-    }
-}
-
-// The following are helpers for writing unit tests for the driver
-namespace
-{
-
-struct ExecutionCallback : public IExecutionCallback
-{
-    ExecutionCallback()
-        : mNotified(false)
-    {
-    }
-
-    Return<void> notify(ErrorStatus status) override
-    {
-        (void)status;
-        ALOGI("ExecutionCallback::notify invoked");
-        std::lock_guard<std::mutex> executionLock(mMutex);
-        mNotified = true;
-        mCondition.notify_one();
-        return Void();
-    }
-
-    /// wait until the callback has notified us that it is done
-    Return<void> wait()
-    {
-        ALOGI("ExecutionCallback::wait invoked");
-        std::unique_lock<std::mutex> executionLock(mMutex);
-        while (!mNotified)
-        {
-            mCondition.wait(executionLock);
-        }
-        mNotified = false;
-        return Void();
-    }
-
-private:
-    // use a mutex and a condition variable to wait for asynchronous callbacks
-    std::mutex mMutex;
-    std::condition_variable mCondition;
-    // and a flag, in case we are notified before the wait call
-    bool mNotified;
-};
-
-class PreparedModelCallback : public IPreparedModelCallback
-{
-public:
-    PreparedModelCallback()
-    {
-    }
-
-    ~PreparedModelCallback() override
-    {
-    }
-
-    Return<void> notify(ErrorStatus status, const sp<IPreparedModel>& preparedModel) override
-    {
-        m_ErrorStatus = status;
-        m_PreparedModel = preparedModel;
-        return Void();
-    }
-
-    ErrorStatus GetErrorStatus()
-    {
-        return m_ErrorStatus;
-    }
-
-    sp<IPreparedModel> GetPreparedModel()
-    {
-        return m_PreparedModel;
-    }
-
-
-private:
-    ErrorStatus        m_ErrorStatus;
-    sp<IPreparedModel> m_PreparedModel;
-};
-
-// lifted from common/Utils.cpp
-hidl_memory allocateSharedMemory(int64_t size)
-{
-    hidl_memory memory;
-
-    const std::string& type      = "ashmem";
-    android::sp<IAllocator>     allocator = IAllocator::getService(type);
-    allocator->allocate(size, [&](bool success, const hidl_memory& mem) {
-        if (!success)
-        {
-            ALOGE("unable to allocate %li bytes of %s", size, type.c_str());
-        }
-        else
-        {
-            memory = mem;
-        }
-    });
-
-    return memory;
-}
-
-
-android::sp<IMemory> AddPoolAndGetData(uint32_t size, Request& request)
-{
-    hidl_memory pool;
-
-    android::sp<IAllocator> allocator = IAllocator::getService("ashmem");
-    allocator->allocate(sizeof(float) * size, [&](bool success, const hidl_memory& mem) {
-        BOOST_TEST(success);
-        pool = mem;
-    });
-
-    request.pools.resize(request.pools.size() + 1);
-    request.pools[request.pools.size() - 1] = pool;
-
-    android::sp<IMemory> mapped = mapMemory(pool);
-    mapped->update();
-    return mapped;
-}
-
-void AddPoolAndSetData(uint32_t size, Request& request, float* data)
-{
-    android::sp<IMemory> memory = AddPoolAndGetData(size, request);
-
-    float* dst = static_cast<float*>(static_cast<void*>(memory->getPointer()));
-
-    memcpy(dst, data, size * sizeof(float));
-}
-
-void AddOperand(Model& model, const Operand& op)
-{
-    model.operands.resize(model.operands.size() + 1);
-    model.operands[model.operands.size() - 1] = op;
-}
-
-void AddIntOperand(Model& model, int32_t value)
-{
-    DataLocation location = {};
-    location.offset = model.operandValues.size();
-    location.length = sizeof(int32_t);
-
-    Operand op    = {};
-    op.type = OperandType::INT32;
-    op.dimensions = hidl_vec<uint32_t>{};
-    op.lifetime   = OperandLifeTime::CONSTANT_COPY;
-    op.location   = location;
-
-    model.operandValues.resize(model.operandValues.size() + location.length);
-    *reinterpret_cast<int32_t*>(&model.operandValues[location.offset]) = value;
-
-    AddOperand(model, op);
-}
-
-template<typename T>
-OperandType TypeToOperandType();
-
-template<>
-OperandType TypeToOperandType<float>()
-{
-    return OperandType::TENSOR_FLOAT32;
-};
-
-template<>
-OperandType TypeToOperandType<int32_t>()
-{
-    return OperandType::TENSOR_INT32;
-};
-
-
-
-template<typename T>
-void AddTensorOperand(Model& model, hidl_vec<uint32_t> dimensions, T* values)
-{
-    uint32_t totalElements = 1;
-    for (uint32_t dim : dimensions)
-    {
-        totalElements *= dim;
-    }
-
-    DataLocation location = {};
-    location.offset = model.operandValues.size();
-    location.length = totalElements * sizeof(T);
-
-    Operand op    = {};
-    op.type       = TypeToOperandType<T>();
-    op.dimensions = dimensions;
-    op.lifetime   = OperandLifeTime::CONSTANT_COPY;
-    op.location   = location;
-
-    model.operandValues.resize(model.operandValues.size() + location.length);
-    for (uint32_t i = 0; i < totalElements; i++)
-    {
-        *(reinterpret_cast<T*>(&model.operandValues[location.offset]) + i) = values[i];
-    }
-
-    AddOperand(model, op);
-}
-
-void AddInputOperand(Model& model, hidl_vec<uint32_t> dimensions)
-{
-    Operand op    = {};
-    op.type       = OperandType::TENSOR_FLOAT32;
-    op.dimensions = dimensions;
-    op.lifetime   = OperandLifeTime::MODEL_INPUT;
-
-    AddOperand(model, op);
-
-    model.inputIndexes.resize(model.inputIndexes.size() + 1);
-    model.inputIndexes[model.inputIndexes.size() - 1] = model.operands.size() - 1;
-}
-
-void AddOutputOperand(Model& model, hidl_vec<uint32_t> dimensions)
-{
-    Operand op = {};
-    op.type       = OperandType::TENSOR_FLOAT32;
-    op.dimensions = dimensions;
-    op.lifetime   = OperandLifeTime::MODEL_OUTPUT;
-
-    AddOperand(model, op);
-
-    model.outputIndexes.resize(model.outputIndexes.size() + 1);
-    model.outputIndexes[model.outputIndexes.size() - 1] = model.operands.size() - 1;
-}
-
-android::sp<IPreparedModel> PrepareModel(const Model& model, ArmnnDriver& driver)
-{
-
-    sp<PreparedModelCallback> cb(new PreparedModelCallback());
-    driver.prepareModel(model, cb);
-
-    BOOST_TEST((cb->GetErrorStatus() == ErrorStatus::NONE));
-    BOOST_TEST((cb->GetPreparedModel() != nullptr));
-
-    return cb->GetPreparedModel();
-}
-
-void Execute(android::sp<IPreparedModel> preparedModel, const Request& request)
-{
-    sp<ExecutionCallback> cb(new ExecutionCallback());
-    BOOST_TEST(preparedModel->execute(request, cb) == ErrorStatus::NONE);
-    ALOGI("Execute: waiting for callback to be invoked");
-    cb->wait();
-}
-
-sp<ExecutionCallback> ExecuteNoWait(android::sp<IPreparedModel> preparedModel, const Request& request)
-{
-    sp<ExecutionCallback> cb(new ExecutionCallback());
-    BOOST_TEST(preparedModel->execute(request, cb) == ErrorStatus::NONE);
-    ALOGI("ExecuteNoWait: returning callback object");
-    return cb;
-}
-}
-
-// Add our own test here since we fail the fc tests which Google supplies (because of non-const weights)
-BOOST_AUTO_TEST_CASE(FullyConnected)
-{
-    // this should ideally replicate fully_connected_float.model.cpp
-    // but that uses slightly weird dimensions which I don't think we need to support for now
-
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-    Model model = {};
-
-    // add operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {2, 4, 1};
-    float   biasValue[]   = {4};
-
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
-    AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
-    AddIntOperand(model, actValue);
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
-    // make the fully connected operation
-    model.operations.resize(1);
-    model.operations[0].type = OperationType::FULLY_CONNECTED;
-    model.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3};
-    model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
-    // make the prepared model
-    android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-    // construct the request
-    DataLocation inloc = {};
-    inloc.poolIndex = 0;
-    inloc.offset    = 0;
-    inloc.length    = 3 * sizeof(float);
-    RequestArgument input = {};
-    input.location = inloc;
-    input.dimensions = hidl_vec<uint32_t>{};
-
-    DataLocation outloc = {};
-    outloc.poolIndex = 1;
-    outloc.offset    = 0;
-    outloc.length    = 1 * sizeof(float);
-    RequestArgument output = {};
-    output.location  = outloc;
-    output.dimensions = hidl_vec<uint32_t>{};
-
-    Request request = {};
-    request.inputs  = hidl_vec<RequestArgument>{input};
-    request.outputs = hidl_vec<RequestArgument>{output};
-
-    // set the input data (matching source test)
-    float indata[] = {2, 32, 16};
-    AddPoolAndSetData(3, request, indata);
-
-    // add memory for the output
-    android::sp<IMemory> outMemory = AddPoolAndGetData(1, request);
-    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
-    // run the execution
-    Execute(preparedModel, request);
-
-    // check the result
-    BOOST_TEST(outdata[0] == 152);
-}
-
-// Add our own test for concurrent execution
-// The main point of this test is to check that multiple requests can be
-// executed without waiting for the callback from previous execution.
-// The operations performed are not significant.
-BOOST_AUTO_TEST_CASE(ConcurrentExecute)
-{
-    ALOGI("ConcurrentExecute: entry");
-
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-    Model model = {};
-
-    // add operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {2, 4, 1};
-    float   biasValue[]   = {4};
-
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
-    AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
-    AddIntOperand(model, actValue);
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
-    // make the fully connected operation
-    model.operations.resize(1);
-    model.operations[0].type = OperationType::FULLY_CONNECTED;
-    model.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3};
-    model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
-    // make the prepared models
-    const size_t maxRequests = 5;
-    android::sp<IPreparedModel> preparedModels[maxRequests];
-    for (size_t i = 0; i < maxRequests; ++i)
-    {
-        preparedModels[i] = PrepareModel(model, *driver);
-    }
-
-    // construct the request data
-    DataLocation inloc = {};
-    inloc.poolIndex = 0;
-    inloc.offset    = 0;
-    inloc.length    = 3 * sizeof(float);
-    RequestArgument input = {};
-    input.location = inloc;
-    input.dimensions = hidl_vec<uint32_t>{};
-
-    DataLocation outloc = {};
-    outloc.poolIndex = 1;
-    outloc.offset    = 0;
-    outloc.length    = 1 * sizeof(float);
-    RequestArgument output = {};
-    output.location  = outloc;
-    output.dimensions = hidl_vec<uint32_t>{};
-
-    // build the requests
-    Request requests[maxRequests];
-    android::sp<IMemory> outMemory[maxRequests];
-    float* outdata[maxRequests];
-    for (size_t i = 0; i < maxRequests; ++i)
-    {
-        requests[i].inputs  = hidl_vec<RequestArgument>{input};
-        requests[i].outputs = hidl_vec<RequestArgument>{output};
-        // set the input data (matching source test)
-        float indata[] = {2, 32, 16};
-        AddPoolAndSetData(3, requests[i], indata);
-        // add memory for the output
-        outMemory[i] = AddPoolAndGetData(1, requests[i]);
-        outdata[i] = static_cast<float*>(static_cast<void*>(outMemory[i]->getPointer()));
-    }
-
-    // invoke the execution of the requests
-    ALOGI("ConcurrentExecute: executing requests");
-    sp<ExecutionCallback> cb[maxRequests];
-    for (size_t i = 0; i < maxRequests; ++i)
-    {
-        cb[i] = ExecuteNoWait(preparedModels[i], requests[i]);
-    }
-
-    // wait for the requests to complete
-    ALOGI("ConcurrentExecute: waiting for callbacks");
-    for (size_t i = 0; i < maxRequests; ++i)
-    {
-        cb[i]->wait();
-    }
-
-    // check the results
-    ALOGI("ConcurrentExecute: validating results");
-    for (size_t i = 0; i < maxRequests; ++i)
-    {
-        BOOST_TEST(outdata[i][0] == 152);
-    }
-    ALOGI("ConcurrentExecute: exit");
-}
-
-BOOST_AUTO_TEST_CASE(GetSupportedOperations)
-{
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
-    ErrorStatus error;
-    std::vector<bool> sup;
-
-    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
-    {
-        error = status;
-        sup = supported;
-    };
-
-    Model model1 = {};
-
-    // add operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {2, 4, 1};
-    float   biasValue[]   = {4};
-
-    AddInputOperand(model1, hidl_vec<uint32_t>{1, 3});
-    AddTensorOperand(model1, hidl_vec<uint32_t>{1, 3}, weightValue);
-    AddTensorOperand(model1, hidl_vec<uint32_t>{1}, biasValue);
-    AddIntOperand(model1, actValue);
-    AddOutputOperand(model1, hidl_vec<uint32_t>{1, 1});
-
-    // make a correct fully connected operation
-    model1.operations.resize(2);
-    model1.operations[0].type = OperationType::FULLY_CONNECTED;
-    model1.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3};
-    model1.operations[0].outputs = hidl_vec<uint32_t>{4};
-
-    // make an incorrect fully connected operation
-    AddIntOperand(model1, actValue);
-    AddOutputOperand(model1, hidl_vec<uint32_t>{1, 1});
-    model1.operations[1].type = OperationType::FULLY_CONNECTED;
-    model1.operations[1].inputs = hidl_vec<uint32_t>{4};
-    model1.operations[1].outputs = hidl_vec<uint32_t>{5};
-
-    driver->getSupportedOperations(model1, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::NONE);
-    BOOST_TEST(sup[0] == true);
-    BOOST_TEST(sup[1] == false);
-
-    // Broadcast add/mul are not supported
-    Model model2 = {};
-
-    AddInputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
-    AddInputOperand(model2, hidl_vec<uint32_t>{4});
-    AddOutputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
-    AddOutputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
-
-    model2.operations.resize(2);
-
-    model2.operations[0].type = OperationType::ADD;
-    model2.operations[0].inputs = hidl_vec<uint32_t>{0,1};
-    model2.operations[0].outputs = hidl_vec<uint32_t>{2};
-
-    model2.operations[1].type = OperationType::MUL;
-    model2.operations[1].inputs = hidl_vec<uint32_t>{0,1};
-    model2.operations[1].outputs = hidl_vec<uint32_t>{3};
-
-    driver->getSupportedOperations(model2, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::NONE);
-    BOOST_TEST(sup[0] == false);
-    BOOST_TEST(sup[1] == false);
-
-    Model model3 = {};
-
-    // Add unsupported operation, should return no error but we don't support it
-    AddInputOperand(model3, hidl_vec<uint32_t>{1, 1, 1, 8});
-    AddIntOperand(model3, 2);
-    AddOutputOperand(model3, hidl_vec<uint32_t>{1, 2, 2, 2});
-    model3.operations.resize(1);
-    model3.operations[0].type = OperationType::DEPTH_TO_SPACE;
-    model1.operations[0].inputs = hidl_vec<uint32_t>{0, 1};
-    model3.operations[0].outputs = hidl_vec<uint32_t>{2};
-
-    driver->getSupportedOperations(model3, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::NONE);
-    BOOST_TEST(sup[0] == false);
-
-    // Add invalid operation
-    Model model4 = {};
-    AddIntOperand(model4, 0);
-    model4.operations.resize(1);
-    model4.operations[0].type = static_cast<OperationType>(100);
-    model4.operations[0].outputs = hidl_vec<uint32_t>{0};
-
-    driver->getSupportedOperations(model4, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::INVALID_ARGUMENT);
-}
-
-// The purpose of this test is to ensure that when encountering an unsupported operation
-//      it is skipped and getSupportedOperations() continues (rather than failing and stopping).
-//      As per IVGCVSW-710.
-BOOST_AUTO_TEST_CASE(UnsupportedLayerContinueOnFailure)
-{
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
-    ErrorStatus error;
-    std::vector<bool> sup;
-
-    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
-    {
-        error = status;
-        sup = supported;
-    };
-
-    Model model = {};
-
-    // operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {2, 4, 1};
-    float   biasValue[]   = {4};
-
-    // broadcast add is unsupported at the time of writing this test, but any unsupported layer will do
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
-    AddInputOperand(model, hidl_vec<uint32_t>{4});
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
-
-    // fully connected
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
-    AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
-    AddIntOperand(model, actValue);
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
-    // broadcast mul is unsupported
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
-
-    model.operations.resize(3);
-
-    // unsupported
-    model.operations[0].type = OperationType::ADD;
-    model.operations[0].inputs = hidl_vec<uint32_t>{0,1};
-    model.operations[0].outputs = hidl_vec<uint32_t>{2};
-
-    // supported
-    model.operations[1].type = OperationType::FULLY_CONNECTED;
-    model.operations[1].inputs  = hidl_vec<uint32_t>{3, 4, 5, 6};
-    model.operations[1].outputs = hidl_vec<uint32_t>{7};
-
-    // unsupported
-    model.operations[2].type = OperationType::MUL;
-    model.operations[2].inputs = hidl_vec<uint32_t>{0,1};
-    model.operations[2].outputs = hidl_vec<uint32_t>{8};
-
-    // we are testing that the unsupported layers return false and the test continues
-    //      rather than failing and stopping.
-    driver->getSupportedOperations(model, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::NONE);
-    BOOST_TEST(sup[0] == false);
-    BOOST_TEST(sup[1] == true);
-    BOOST_TEST(sup[2] == false);
-}
-
-// The purpose of this test is to ensure that when encountering an failure
-//      during mem pool mapping we properly report an error to the framework via a callback
-BOOST_AUTO_TEST_CASE(ModelToINetworkConverterMemPoolFail)
-{
-    auto driver = std::make_unique<ArmnnDriver>(armnn::Compute::CpuRef);
-
-    ErrorStatus error;
-    std::vector<bool> sup;
-
-    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
-    {
-        error = status;
-        sup = supported;
-    };
-
-    Model model = {};
-
-    model.pools = hidl_vec<hidl_memory>{hidl_memory("Unsuported hidl memory type", nullptr, 0)};
-
-    //memory pool mapping should fail, we should report an error
-    driver->getSupportedOperations(model, cb);
-    BOOST_TEST((int)error == (int)ErrorStatus::GENERAL_FAILURE);
-}
-
-namespace
-{
-
-void PaddingTestImpl(android::nn::PaddingScheme paddingScheme)
-{
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-    Model model  = {};
-
-    uint32_t outSize = paddingScheme == kPaddingSame ? 2 : 1;
-
-    // add operands
-    float weightValue[] = {1, -1, 0, 1};
-    float biasValue[]   = {0};
-
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 3, 1});
-    AddTensorOperand(model, hidl_vec<uint32_t>{1, 2, 2, 1}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
-    AddIntOperand(model, (int32_t)paddingScheme); // padding
-    AddIntOperand(model, 2); // stride x
-    AddIntOperand(model, 2); // stride y
-    AddIntOperand(model, 0); // no activation
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, outSize, 1});
-
-    // make the convolution operation
-    model.operations.resize(1);
-    model.operations[0].type = OperationType::CONV_2D;
-    model.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3, 4, 5, 6};
-    model.operations[0].outputs = hidl_vec<uint32_t>{7};
-
-    // make the prepared model
-    android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-    // construct the request
-    DataLocation inloc    = {};
-    inloc.poolIndex       = 0;
-    inloc.offset          = 0;
-    inloc.length          = 6 * sizeof(float);
-    RequestArgument input = {};
-    input.location        = inloc;
-    input.dimensions      = hidl_vec<uint32_t>{};
-
-    DataLocation outloc    = {};
-    outloc.poolIndex       = 1;
-    outloc.offset          = 0;
-    outloc.length          = outSize * sizeof(float);
-    RequestArgument output = {};
-    output.location        = outloc;
-    output.dimensions      = hidl_vec<uint32_t>{};
-
-    Request request = {};
-    request.inputs  = hidl_vec<RequestArgument>{input};
-    request.outputs = hidl_vec<RequestArgument>{output};
-
-
-    // set the input data (matching source test)
-    float indata[] = {4, 1, 0, 3, -1, 2};
-    AddPoolAndSetData(6, request, indata);
-
-    // add memory for the output
-    android::sp<IMemory> outMemory = AddPoolAndGetData(outSize, request);
-    float*               outdata   = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
-    // run the execution
-    Execute(preparedModel, request);
-
-    // check the result
-    if (paddingScheme == kPaddingValid)
-    {
-        BOOST_TEST(outdata[0] == 2);
-    }
-    else if (paddingScheme == kPaddingSame)
-    {
-        BOOST_TEST(outdata[0] == 2);
-        BOOST_TEST(outdata[1] == 0);
-    }
-    else
-    {
-        BOOST_TEST(false);
-    }
-}
-
-}
-
-BOOST_AUTO_TEST_CASE(ConvValidPadding)
-{
-    PaddingTestImpl(kPaddingValid);
-}
-
-BOOST_AUTO_TEST_CASE(ConvSamePadding)
-{
-    PaddingTestImpl(kPaddingSame);
-}
-
-BOOST_AUTO_TEST_CASE(TestFullyConnected4dInput)
-{
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
-    ErrorStatus error;
-    std::vector<bool> sup;
-
-    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
-        {
-            error = status;
-            sup = supported;
-        };
-
-    Model model = {};
-
-    // operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
-                             0, 1, 0, 0, 0, 0, 0, 0,
-                             0, 0, 1, 0, 0, 0, 0, 0,
-                             0, 0, 0, 1, 0, 0, 0, 0,
-                             0, 0, 0, 0, 1, 0, 0, 0,
-                             0, 0, 0, 0, 0, 1, 0, 0,
-                             0, 0, 0, 0, 0, 0, 1, 0,
-                             0, 0, 0, 0, 0, 0, 0, 1}; //identity
-    float   biasValue[]   = {0, 0, 0, 0, 0, 0, 0, 0};
-
-    // fully connected operation
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 1, 1, 8});
-    AddTensorOperand(model, hidl_vec<uint32_t>{8, 8}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{8}, biasValue);
-    AddIntOperand(model, actValue);
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 8});
-
-    model.operations.resize(1);
-
-    model.operations[0].type = OperationType::FULLY_CONNECTED;
-    model.operations[0].inputs  = hidl_vec<uint32_t>{0,1,2,3};
-    model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
-    // make the prepared model
-    android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-
-    // construct the request
-    DataLocation inloc = {};
-    inloc.poolIndex = 0;
-    inloc.offset    = 0;
-    inloc.length    = 8 * sizeof(float);
-    RequestArgument input = {};
-    input.location = inloc;
-    input.dimensions = hidl_vec<uint32_t>{};
-
-    DataLocation outloc = {};
-    outloc.poolIndex = 1;
-    outloc.offset    = 0;
-    outloc.length    = 8 * sizeof(float);
-    RequestArgument output = {};
-    output.location  = outloc;
-    output.dimensions = hidl_vec<uint32_t>{};
-
-    Request request = {};
-    request.inputs  = hidl_vec<RequestArgument>{input};
-    request.outputs = hidl_vec<RequestArgument>{output};
-
-    // set the input data
-    float indata[] = {1,2,3,4,5,6,7,8};
-    AddPoolAndSetData(8, request, indata);
-
-    // add memory for the output
-    android::sp<IMemory> outMemory = AddPoolAndGetData(8, request);
-    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
-    // run the execution
-    Execute(preparedModel, request);
-
-    // check the result
-    BOOST_TEST(outdata[0] == 1);
-    BOOST_TEST(outdata[1] == 2);
-    BOOST_TEST(outdata[2] == 3);
-    BOOST_TEST(outdata[3] == 4);
-    BOOST_TEST(outdata[4] == 5);
-    BOOST_TEST(outdata[5] == 6);
-    BOOST_TEST(outdata[6] == 7);
-    BOOST_TEST(outdata[7] == 8);
-}
-
-BOOST_AUTO_TEST_CASE(TestFullyConnected4dInputReshape)
-{
-    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
-    ErrorStatus error;
-    std::vector<bool> sup;
-
-    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
-        {
-            error = status;
-            sup = supported;
-        };
-
-    Model model = {};
-
-    // operands
-    int32_t actValue      = 0;
-    float   weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
-                             0, 1, 0, 0, 0, 0, 0, 0,
-                             0, 0, 1, 0, 0, 0, 0, 0,
-                             0, 0, 0, 1, 0, 0, 0, 0,
-                             0, 0, 0, 0, 1, 0, 0, 0,
-                             0, 0, 0, 0, 0, 1, 0, 0,
-                             0, 0, 0, 0, 0, 0, 1, 0,
-                             0, 0, 0, 0, 0, 0, 0, 1}; //identity
-    float   biasValue[]   = {0, 0, 0, 0, 0, 0, 0, 0};
-
-    // fully connected operation
-    AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 2, 2});
-    AddTensorOperand(model, hidl_vec<uint32_t>{8, 8}, weightValue);
-    AddTensorOperand(model, hidl_vec<uint32_t>{8}, biasValue);
-    AddIntOperand(model, actValue);
-    AddOutputOperand(model, hidl_vec<uint32_t>{1, 8});
-
-    model.operations.resize(1);
-
-    model.operations[0].type = OperationType::FULLY_CONNECTED;
-    model.operations[0].inputs  = hidl_vec<uint32_t>{0,1,2,3};
-    model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
-    // make the prepared model
-    android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-
-    // construct the request
-    DataLocation inloc = {};
-    inloc.poolIndex = 0;
-    inloc.offset    = 0;
-    inloc.length    = 8 * sizeof(float);
-    RequestArgument input = {};
-    input.location = inloc;
-    input.dimensions = hidl_vec<uint32_t>{};
-
-    DataLocation outloc = {};
-    outloc.poolIndex = 1;
-    outloc.offset    = 0;
-    outloc.length    = 8 * sizeof(float);
-    RequestArgument output = {};
-    output.location  = outloc;
-    output.dimensions = hidl_vec<uint32_t>{};
-
-    Request request = {};
-    request.inputs  = hidl_vec<RequestArgument>{input};
-    request.outputs = hidl_vec<RequestArgument>{output};
-
-    // set the input data
-    float indata[] = {1,2,3,4,5,6,7,8};
-    AddPoolAndSetData(8, request, indata);
-
-    // add memory for the output
-    android::sp<IMemory> outMemory = AddPoolAndGetData(8, request);
-    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
-    // run the execution
-    Execute(preparedModel, request);
-
-    // check the result
-    BOOST_TEST(outdata[0] == 1);
-    BOOST_TEST(outdata[1] == 2);
-    BOOST_TEST(outdata[2] == 3);
-    BOOST_TEST(outdata[3] == 4);
-    BOOST_TEST(outdata[4] == 5);
-    BOOST_TEST(outdata[5] == 6);
-    BOOST_TEST(outdata[6] == 7);
-    BOOST_TEST(outdata[7] == 8);
-}
-
 BOOST_AUTO_TEST_SUITE_END()