diff --git a/src/backends/backendsCommon/test/ActivationTestImpl.hpp b/src/backends/backendsCommon/test/ActivationTestImpl.hpp
new file mode 100644
index 0000000..3b3ee93
--- /dev/null
+++ b/src/backends/backendsCommon/test/ActivationTestImpl.hpp
@@ -0,0 +1,560 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "ActivationFixture.hpp"
+#include "QuantizeHelper.hpp"
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Tensor.hpp>
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+#include <algorithm>
+
+template<typename T>
+LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory,
+                                        float upperBound, float lowerBound,
+                                        float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset,
+                                        const std::vector<T>& inputData, const std::vector<T>& outputExpectedData,
+                                        unsigned int inputWidth, unsigned int inputHeight,
+                                        unsigned int inputChannels, unsigned int inputBatchSize)
+{
+    unsigned int outputWidth = inputWidth;
+    unsigned int outputHeight = inputHeight;
+    unsigned int outputChannels = inputChannels;
+    unsigned int outputBatchSize = inputBatchSize;
+
+    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+        armnn::GetDataType<T>());
+
+    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+        armnn::GetDataType<T>());
+
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(inputScale);
+        inputTensorInfo.SetQuantizationOffset(inputOffset);
+
+        outputTensorInfo.SetQuantizationScale(outputScale);
+        outputTensorInfo.SetQuantizationOffset(outputOffset);
+    }
+
+    LayerTestResult<T, 4> result(inputTensorInfo);
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    // Setup bounded ReLu.
+    armnn::ActivationQueueDescriptor descriptor;
+    armnn::WorkloadInfo workloadInfo;
+    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+    descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu;
+    descriptor.m_Parameters.m_A = upperBound;
+    descriptor.m_Parameters.m_B = lowerBound;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
+
+    return result;
+}
+
+LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    unsigned int inputWidth = 4u;
+    unsigned int inputHeight = 5u;
+    unsigned int inputChannels = 1u;
+    unsigned int inputBatchSize = 1;
+
+    std::vector<float> input = std::vector<float>{
+      -2.0f,       0.1f,     0.5f,     1.25f,
+     0.786f,    0.9875f,    -1.5f,    0.384f,
+    1.0001f,       3.5f,     7.5f,    0.896f,
+     2.126f,       2.0f,     0.3f,     0.15f,
+     0.999f,       1.2f,    0.89f,      6.1f,
+    };
+
+    // Calculated manually.
+    std::vector<float> output = std::vector<float>{
+      -1.0f,       0.1f,     0.5f,      1.0f,
+     0.786f,    0.9875f,    -1.0f,    0.384f,
+       1.0f,       1.0f,     1.0f,    0.896f,
+       1.0f,       1.0f,     0.3f,     0.15f,
+     0.999f,       1.0f,    0.89f,      1.0f,
+    };
+
+    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,
+                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    unsigned int inputWidth = 4u;
+    unsigned int inputHeight = 5u;
+    unsigned int inputChannels = 1u;
+    unsigned int inputBatchSize = 1;
+
+    std::vector<float> input = std::vector<float>{
+      -1.0f,       0.1f,     0.5f,      6.25f,
+     0.786f,    5.9875f,    -0.5f,     0.384f,
+    6.0001f,       3.5f,     7.5f,     0.896f,
+     2.126f,      12.0f,     0.3f,      0.15f,
+     0.999f,       1.2f,    0.89f,       6.1f,
+    };
+
+    // Calculated manually.
+    std::vector<float> output = std::vector<float>{
+       0.0f,       0.1f,     0.5f,       6.0f,
+     0.786f,    5.9875f,     0.0f,     0.384f,
+       6.0f,       3.5f,     6.0f,     0.896f,
+     2.126f,       6.0f,     0.3f,      0.15f,
+     0.999f,       1.2f,    0.89f,       6.0f,
+    };
+
+    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,
+                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    unsigned int inputWidth     = 3u;
+    unsigned int inputHeight    = 2u;
+    unsigned int inputChannels  = 1u;
+    unsigned int inputBatchSize = 1;
+
+    std::vector<uint8_t> input = std::vector<uint8_t>{
+         51, 124, 28,
+        251,   8, 92
+    };
+
+    // Calculated manually.
+    std::vector<uint8_t> output = std::vector<uint8_t>{
+          0, 122,  0,
+        255,   0, 58
+    };
+
+    float inputScale     = 12.0f / 255.0f;
+    int32_t inputOffset  = 63;
+    float outputScale    = 6.0f / 255.0f;
+    int32_t outputOffset = 0;
+
+    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f,
+                                 inputScale, inputOffset, outputScale, outputOffset,
+                                 input, output,
+                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    unsigned int inputWidth     = 3u;
+    unsigned int inputHeight    = 2u;
+    unsigned int inputChannels  = 1u;
+    unsigned int inputBatchSize = 1;
+
+    std::vector<uint8_t> input = std::vector<uint8_t>{
+         51, 230, 28,
+        251,   8, 92
+    };
+
+    // Calculated manually.
+    std::vector<uint8_t> output = std::vector<uint8_t>{
+         51, 192, 32,
+        192,  32, 92
+    };
+
+    int32_t inputOffset = 112;
+    float inputScale    = 0.0125f;
+
+    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f,
+                                 inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same.
+                                 input, output,
+                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+namespace
+{
+
+struct BoundedReLuRandomInputTestTraits
+{
+    constexpr static unsigned int inputHeight = 31u;
+    constexpr static unsigned int inputWidth = 19u;
+    constexpr static unsigned int inputChannels = 4u;
+    constexpr static unsigned int inputBatchSize = 2;
+
+    constexpr static unsigned int outputHeight = inputHeight;
+    constexpr static unsigned int outputWidth = inputWidth;
+    constexpr static unsigned int outputChannels = inputChannels;
+    constexpr static unsigned int outputBatchSize = inputBatchSize;
+
+    static armnn::TensorInfo GetInputTensorInfo()
+    {
+        return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+            armnn::DataType::Float32);
+    }
+
+    static armnn::TensorInfo GetOutputTensorInfo()
+    {
+        return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+            armnn::DataType::Float32);
+    }
+};
+
+boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory,
+                                                        float lowerBound,
+                                                        float upperBound,
+                                                        const armnn::ActivationDescriptor& activationDescriptor)
+{
+    const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo();
+    const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();
+
+    boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo));
+
+    // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu
+    // range [lowerBound, upperBound].
+    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    // Set up bounded ReLu.
+    armnn::ActivationQueueDescriptor descriptor;
+    armnn::WorkloadInfo workloadInfo;
+    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+    descriptor.m_Parameters = activationDescriptor;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get());
+
+    return output;
+}
+
+} // namespace
+
+LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory,
+                                          armnn::IWorkloadFactory& otherWorkloadFactory,
+                                          float upperBound,
+                                          float lowerBound)
+{
+    LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());
+
+    armnn::ActivationDescriptor activationDescriptor;
+    activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
+    activationDescriptor.m_A = upperBound;
+    activationDescriptor.m_B = lowerBound;
+
+    result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor);
+    result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor);
+
+    return result;
+}
+
+template<typename T>
+LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory,
+                                                        float qScale = 0.0f,
+                                                        int32_t qOffset = 0)
+{
+    unsigned int inputHeight    = 20;
+    unsigned int inputWidth     = 17;
+    unsigned int inputChannels  = 3;
+    unsigned int batchSize      = 5;
+
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int shape[]  = {batchSize, inputChannels, inputHeight, inputWidth};
+
+    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    LayerTestResult<T, 4> ret(outputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    // Do linear activation that should leave the tensor unchanged.
+    armnn::ActivationQueueDescriptor data;
+    armnn::WorkloadInfo info;
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+    data.m_Parameters.m_A = 1.0f;
+    data.m_Parameters.m_B = 0.0f;
+    data.m_Parameters.m_Function = armnn::ActivationFunction::Linear;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561);
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    // Ensure output equals input.
+    ret.outputExpected = input;
+
+    return ret;
+}
+
+LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    return ConstantLinearActivationTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+    return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3);
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory,
+                                           armnn::ActivationFunction activationFunction,
+                                           float activationParameterA,
+                                           float activationParameterB,
+                                           float qScale,
+                                           int32_t qOffset,
+                                           const std::vector<float>& inputData,
+                                           const std::vector<float>& outputExpectedData)
+{
+    constexpr static unsigned int inputWidth = 16u;
+    constexpr static unsigned int inputHeight = 1u;
+    constexpr static unsigned int inputChannels = 1u;
+    constexpr static unsigned int inputBatchSize = 1u;
+
+    constexpr static unsigned int outputWidth = inputWidth;
+    constexpr static unsigned int outputHeight = inputHeight;
+    constexpr static unsigned int outputChannels = inputChannels;
+    constexpr static unsigned int outputBatchSize = inputBatchSize;
+
+    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+                                      armnn::GetDataType<T>());
+    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+                                       armnn::GetDataType<T>());
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    LayerTestResult<T, 4> result(inputTensorInfo);
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    // Setup bounded ReLu.
+    armnn::ActivationQueueDescriptor descriptor;
+    armnn::WorkloadInfo workloadInfo;
+    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+    descriptor.m_Parameters.m_Function = activationFunction;
+    descriptor.m_Parameters.m_A = activationParameterA;
+    descriptor.m_Parameters.m_B = activationParameterB;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+    // Calculated manually.
+    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData));
+
+    return result;
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+    std::vector<float> inputData = {
+        -0.1f, -0.2f, -0.3f, -0.4f,
+        0.1f,  0.2f,  0.3f,  0.4f,
+        -1.0f, -2.0f, -3.0f, -4.0f,
+        1.0f,  2.0f,  3.0f,  4.0f
+    };
+
+    // Calculate output values for input.
+    auto f = [](float value)
+    {
+        return 1.0f / (1.0f + std::exp(-value));
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<T>(workloadFactory,
+                                   armnn::ActivationFunction::Sigmoid,
+                                   0.f,
+                                   0.f,
+                                   qScale,
+                                   qOffset,
+                                   inputData,
+                                   outputExpectedData);
+}
+
+LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+    return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50);
+}
+
+template<typename T>
+LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory,
+                                               armnn::IWorkloadFactory& refWorkloadFactory,
+                                               armnn::ActivationFunction f,
+                                               unsigned int batchSize = 5,
+                                               float qScale = 0.0f,
+                                               int32_t qOffset = 0)
+{
+    unsigned int width     = 17;
+    unsigned int height    = 29;
+    unsigned int channels  = 2;
+
+    float a = 0.234f;
+    float b = -12.345f;
+
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int shape[] = {batchSize, channels, height, width};
+
+    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    float minVal = -10.f;
+    if (f == armnn::ActivationFunction::Sqrt)
+    {
+        minVal = 0.f;
+    }
+
+    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f);
+
+
+    LayerTestResult<T,4> ret(outputTensorInfo);
+    auto boostArrayExtents = boost::extents
+        [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)]
+    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]
+    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)]
+    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];
+    ret.output.resize(boostArrayExtents);
+    ret.outputExpected.resize(boostArrayExtents);
+
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::ActivationQueueDescriptor data;
+    armnn::WorkloadInfo info;
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+    data.m_Parameters.m_A        = a;
+    data.m_Parameters.m_B        = b;
+    data.m_Parameters.m_Function = f;
+
+    armnn::ActivationQueueDescriptor refData = data;
+    armnn::WorkloadInfo refInfo = info;
+    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
+    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
+    BOOST_ASSERT(workload != nullptr);
+    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo);
+    BOOST_ASSERT(workloadRef != nullptr);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+    inputHandleRef->Allocate();
+    outputHandleRef->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+
+    workload->Execute();
+    workloadRef->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+    return ret;
+}
+
+LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory,
+                                               armnn::IWorkloadFactory& refWorkloadFactory,
+                                               armnn::ActivationFunction f,
+                                               unsigned int batchSize)
+{
+    return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize);
+}
+
+LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory,
+                                                      armnn::IWorkloadFactory& refWorkloadFactory,
+                                                      armnn::ActivationFunction f)
+{
+    return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50);
+}