Revert "MLCE-418 Reduce layer does not support multiple axes"

This reverts commit d905decd256558bbee165e636ce4242ac3b9c917.

Reason for revert: LargeGraph_TENSOR_FLOAT32/FLOAT16 CTS tests failures

Change-Id: Ie69826549e73775825f45134375b5b2c41aebd01

9 files changed, 24 insertions, 558 deletions

diff --git a/Android.mk b/Android.mk
index 168b32a400..d9230e5585 100644
--- a/Android.mk
+++ b/Android.mk
@@ -393,7 +393,6 @@ LOCAL_SRC_FILES := \
         src/armnn/test/optimizations/OptimizeInversePermutesTests.cpp \
         src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp \
         src/armnn/test/optimizations/PermuteAsReshapeTests.cpp \
-        src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp \
         src/armnn/test/optimizations/SquashEqualSiblingsTests.cpp \
         src/armnn/test/optimizations/TransposeAsReshapeTests.cpp \
         src/armnn/test/OptimizerTests.cpp \
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c15dcb0eda..51f08bade9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -566,7 +566,6 @@ if(BUILD_UNIT_TESTS)
         src/armnn/test/optimizations/OptimizeInversePermutesTests.cpp
         src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp
         src/armnn/test/optimizations/PermuteAsReshapeTests.cpp
-        src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
         src/armnn/test/optimizations/SquashEqualSiblingsTests.cpp
         src/armnn/test/optimizations/TransposeAsReshapeTests.cpp
         src/armnn/test/OptionalTest.cpp
diff --git a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
deleted file mode 100644
index 28c0ea1466..0000000000
--- a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
+++ /dev/null
@@ -1,288 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "../GraphUtils.hpp"
-#include "../TestUtils.hpp"
-
-#include <armnn/INetwork.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(Optimizer)
-
-INetworkPtr CreateSimpleReduceNetwork(ReduceDescriptor reduceDescriptor,
-                                      TensorShape& inputShape,
-                                      TensorShape& outputShape)
-{
-    // Create a network
-    INetworkPtr network = INetwork::Create();
-
-    const std::string layerName("reduce_layer");
-    const TensorInfo inputInfo (inputShape, DataType::Float32);
-    const TensorInfo outputInfo(outputShape, DataType::Float32);
-
-    IConnectableLayer* const inputLayer  = network->AddInputLayer(0);
-    IConnectableLayer* const reduceLayer = network->AddReduceLayer(reduceDescriptor, layerName.c_str());
-    IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
-
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    reduceLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    inputLayer->GetOutputSlot(0).Connect(reduceLayer->GetInputSlot(0));
-    reduceLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
-    return network;
-}
-
-void ReduceWithMultipleAxesTest(INetworkPtr& network,
-                                const TensorShape& outputShape,
-                                const std::vector<float>& inputData,
-                                const std::vector<float>& expectedOutput,
-                                const size_t numOfAxes,
-                                Compute backendId)
-{
-    // Create ArmNN runtime
-    IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions());
-
-    // Optimise ArmNN network
-    IOptimizedNetworkPtr optNet = Optimize(*network, {backendId}, run->GetDeviceSpec());
-
-    Graph& graph = GetGraphForTesting(optNet.get());
-    if (numOfAxes == 2)
-    {
-        BOOST_CHECK(graph.GetNumLayers() == 4);
-        BOOST_TEST(CheckSequence(graph.cbegin(),
-                                 graph.cend(),
-                                 &IsLayerOfType<InputLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<OutputLayer>));
-    }
-    else
-    {
-        BOOST_CHECK(graph.GetNumLayers() == 5);
-        BOOST_TEST(CheckSequence(graph.cbegin(),
-                                 graph.cend(),
-                                 &IsLayerOfType<InputLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<OutputLayer>));
-    }
-
-    // Get last layer in new chain, layers name follow 0, 1, 2 pattern
-    std::string layerName = "reduce_layer_" + std::to_string(numOfAxes - 1);
-    Layer* const reduceLayer = GetFirstLayerWithName(graph, layerName);
-    BOOST_TEST(reduceLayer);
-    auto reduceTensorInfo = reduceLayer->GetOutputSlot().GetTensorInfo();
-
-    // Tensorshape and the data type are correct
-    BOOST_TEST((reduceTensorInfo.GetShape() == outputShape));
-    BOOST_TEST((reduceTensorInfo.GetDataType() == DataType::Float32));
-
-    // Load network into runtime
-    NetworkId networkIdentifier;
-    run->LoadNetwork(networkIdentifier, std::move(optNet));
-
-    // Create input and output tensors
-    std::vector<float> outputData(expectedOutput.size());
-    InputTensors inputTensors
-    {
-        { 0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data()) }
-    };
-    OutputTensors outputTensors
-    {
-        { 0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data()) }
-    };
-
-    // Run inference
-    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
-
-    // Checks the results
-    BOOST_TEST(outputData == expectedOutput);
-}
-
-void ReduceSumWithTwoAxesKeepDimsTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 1, 2 };
-    reduceDescriptor.m_KeepDims = true;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 1, 3, 2, 4 };
-    TensorShape outputShape = { 1, 1, 1, 4 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,   3.0f,   4.0f,
-                                          5.0f,   6.0f,   7.0f,   8.0f,
-
-                                          10.0f,  20.0f,  30.0f,  40.0f,
-                                          50.0f,  60.0f,  70.0f,  80.0f,
-
-                                          100.0f, 200.0f, 300.0f, 400.0f,
-                                          500.0f, 600.0f, 700.0f, 800.0f });
-    const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithTwoAxesTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 1, 2 };
-    reduceDescriptor.m_KeepDims = false;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 1, 3, 2, 4 };
-    TensorShape outputShape = { 1, 4 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,   3.0f,   4.0f,
-                                          5.0f,   6.0f,   7.0f,   8.0f,
-
-                                          10.0f,  20.0f,  30.0f,  40.0f,
-                                          50.0f,  60.0f,  70.0f,  80.0f,
-
-                                          100.0f, 200.0f, 300.0f, 400.0f,
-                                          500.0f, 600.0f, 700.0f, 800.0f });
-    const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithThreeAxesKeepDimsTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 0, 2, 3 };
-    reduceDescriptor.m_KeepDims = true;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 2, 2, 2, 2 };
-    TensorShape outputShape = { 1, 2, 1, 1 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,
-                                          3.0f,   4.0f,
-
-                                          5.0f,   6.0f,
-                                          7.0f,   8.0f,
-
-                                          10.0f,  20.0f,
-                                          30.0f,  40.0f,
-
-                                          50.0f,  60.0f,
-                                          70.0f,  80.0f });
-    const std::vector<float> expectedOutput({ 110.0f, 286.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithThreeAxesTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 0, 2, 3 };
-    reduceDescriptor.m_KeepDims = false;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 2, 2, 2, 2 };
-    TensorShape outputShape = { 2 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,
-                                          3.0f,   4.0f,
-
-                                          5.0f,   6.0f,
-                                          7.0f,   8.0f,
-
-                                          10.0f,  20.0f,
-                                          30.0f,  40.0f,
-
-                                          50.0f,  60.0f,
-                                          70.0f,  80.0f });
-    const std::vector<float> expectedOutput({ 110.0f, 286.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-using namespace armnn;
-#if defined(ARMCOMPUTENEON_ENABLED)
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsCpuAccTest)
-{
-    ReduceSumWithTwoAxesKeepDimsTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesCpuAccTest)
-{
-    ReduceSumWithTwoAxesTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsCpuAccTest)
-{
-    ReduceSumWithThreeAxesKeepDimsTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesCpuAccTest)
-{
-    ReduceSumWithThreeAxesTest(Compute::CpuAcc);
-}
-#endif
-
-#if defined(ARMCOMPUTECL_ENABLED)
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsGpuAccTest)
-{
-    ReduceSumWithTwoAxesKeepDimsTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesGpuAccTest)
-{
-    ReduceSumWithTwoAxesTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsGpuAccTest)
-{
-    ReduceSumWithThreeAxesKeepDimsTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesGpuAccTest)
-{
-    ReduceSumWithThreeAxesTest(Compute::GpuAcc);
-}
-#endif
-
-BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp b/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
index 9439ddb61e..a0fca46330 100644
--- a/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
@@ -6,9 +6,6 @@
 #pragma once
 
 #include <armnn/backends/OptimizationViews.hpp>
-#include <armnn/utility/Assert.hpp>
-
-#include <aclCommon/ArmComputeUtils.hpp>
 
 namespace armnn
 {
@@ -150,86 +147,4 @@ LayerType* FuseLayerWithWeightsAndBiases(OptimizationViews& optimizationViews,
     return replacementLayer;
 }
 
-//
-// If reduce layer has multiple axes, add new layer for each axis to simulate the same behaviour
-// as currently only one axis is supported.
-//
-template<typename LayerType>
-void ChainReduceLayers(OptimizationViews& optimizationViews,
-                       LayerType* baseLayer,
-                       ReduceDescriptor& reduceDescriptor)
-{
-    // If layer has single axis don't chain layers.
-    if (!reduceDescriptor.m_vAxis.empty() && reduceDescriptor.m_vAxis.size() > 1)
-    {
-        // Save base layer output shape to compare against the output of the final layer added.
-        const TensorInfo baseLayerInfo = baseLayer->GetOutputSlot(0).GetTensorInfo();
-
-        // Vector of new chained layers, used for substitution.
-        std::vector<Layer*> layers;
-
-        // Vector of axes so each layer is reshaped correctly.
-        std::vector<uint32_t> reduceAxis;
-        unsigned int recalulateAxis = 0;
-
-        for (unsigned int i = 0; i != reduceDescriptor.m_vAxis.size(); ++i)
-        {
-            // Get TensorInfo to populate subsequent layers with.
-            TensorInfo layerInfoToModify = baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo();
-
-            reduceAxis.emplace_back(reduceDescriptor.m_vAxis[i]);
-
-            // Calculate new shape based on the axes.
-            const TensorShape& reducedShape = ComputeReductionTensorShape(layerInfoToModify,
-                                                                          reduceAxis,
-                                                                          reduceDescriptor.m_KeepDims);
-            layerInfoToModify.SetShape(reducedShape);
-
-            // Create a vector for the single axis to be assigned to the descriptor.
-            // Update axis if keepDims is set reduce layers correctly.
-            std::vector<uint32_t> singleAxis(1, reduceDescriptor.m_vAxis[i] - recalulateAxis);
-
-            // Create a descriptor and assign single axis.
-            ReduceDescriptor newReduceDescriptor = baseLayer->GetParameters();
-            newReduceDescriptor.m_vAxis.assign(singleAxis.begin(), singleAxis.end());
-
-            // Add new layer to graph.
-            std::string layerName = "reduce_layer_" + std::to_string(i);
-            Layer* replacementLayer = optimizationViews.GetGraph().AddLayer<LayerType>(newReduceDescriptor,
-                                                                                       layerName.c_str());
-
-            // Connect previous layer with new layer.
-            // The first and last layer will be connected when the subgraph is replaced.
-            if (!layers.empty())
-            {
-                layers[i - 1]->GetOutputSlot(0).Connect(replacementLayer->GetInputSlot(0));
-            }
-
-            // Set updated tensorInfo for new layer.
-            replacementLayer->GetOutputSlot(0).SetTensorInfo(layerInfoToModify);
-
-            if (!reduceDescriptor.m_KeepDims)
-            {
-                recalulateAxis++;
-            }
-
-            layers.emplace_back(replacementLayer);
-        }
-
-        // Check if the TensorInfo from the last layer equals the inferred output from the original layer.
-        ARMNN_ASSERT(baseLayerInfo == layers.back()->GetOutputSlot().GetTensorInfo());
-
-        std::list<Layer*> replacementLayers(layers.begin(), layers.end());
-
-        // Substitute new chained subgraph for original reduce layer.
-        SubgraphView substitutionSubgraph(baseLayer);
-        SubgraphView replacementSubgraph(CreateInputsFrom({replacementLayers.front()}),
-                                         CreateOutputsFrom({replacementLayers.back()}),
-                                         std::move(replacementLayers));
-
-        optimizationViews.AddSubstitution({substitutionSubgraph, replacementSubgraph});
-
-    }
-}
-
 } // namespace armnn
diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp
index 5bc5abcb05..d9efab288f 100644
--- a/src/backends/aclCommon/ArmComputeUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeUtils.hpp
@@ -7,7 +7,6 @@
 #include <armnn/Descriptors.hpp>
 #include <armnn/Tensor.hpp>
 #include <armnn/utility/Assert.hpp>
-#include <armnn/utility/NumericCast.hpp>
 #include <backendsCommon/WorkloadData.hpp>
 
 #include <arm_compute/core/Types.h>
@@ -268,58 +267,4 @@ inline arm_compute::ReductionOperation ConvertReductionOperationToAcl(const Redu
     }
 }
 
-/// Function to compute the output tensor shape based on the axes and if keepDims is set.
-inline const TensorShape ComputeReductionTensorShape(const armnn::TensorInfo& input,
-                                                     const std::vector<uint32_t>& vAxis,
-                                                     const bool keepDims)
-{
-    unsigned int rank = input.GetNumDimensions();
-    unsigned int outputRank = 0;
-
-    // Calculate output dimension
-    if (keepDims)
-    {
-        outputRank = rank;
-    }
-    else if (vAxis.empty())
-    {
-        outputRank = 1;
-    }
-    else if (vAxis.size() > input.GetNumDimensions())
-    {
-        throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions");
-    }
-    else
-    {
-        outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(vAxis.size());
-        if (outputRank == 0)
-        {
-            outputRank = 1;
-        }
-    }
-
-    std::vector<unsigned int> dimSizes(outputRank, 1);
-    if (!vAxis.empty())
-    {
-        // Skip the dimension that has been reduced unless keepDims is true.
-        unsigned int outputIndex = 0;
-        for (unsigned int i = 0; i < input.GetNumDimensions(); ++i)
-        {
-            if (std::find(vAxis.begin(), vAxis.end(), i) == vAxis.end())
-            {
-                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input.GetShape()[i]);
-                ++outputIndex;
-            }
-            else if (keepDims)
-            {
-                dimSizes[outputIndex] = 1;
-                ++outputIndex;
-            }
-        }
-    }
-
-    const TensorShape inferredShape = TensorShape(outputRank, dimSizes.data());
-    return inferredShape;
-}
-
 } // namespace armnn
diff --git a/src/backends/cl/ClBackend.cpp b/src/backends/cl/ClBackend.cpp
index 92a06aa8e1..f97cb4bba8 100644
--- a/src/backends/cl/ClBackend.cpp
+++ b/src/backends/cl/ClBackend.cpp
@@ -29,7 +29,6 @@
 #include "workloads/ClDivisionWorkload.hpp"
 #include "workloads/ClFullyConnectedWorkload.hpp"
 #include "workloads/ClMultiplicationWorkload.hpp"
-#include "workloads/ClReduceWorkload.hpp"
 #include "workloads/ClSubtractionWorkload.hpp"
 
 #include <Optimizer.hpp>
@@ -189,8 +188,7 @@ OptimizationViews ClBackend::OptimizeSubgraphView(const SubgraphView& subgraph,
         if ((base.GetType() == LayerType::DepthwiseConvolution2d || base.GetType() == LayerType::Convolution2d
             || base.GetType() == LayerType::BatchNormalization || base.GetType() == LayerType::FullyConnected
             || base.GetType() == LayerType::Addition || base.GetType() == LayerType::Multiplication
-            || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division
-            || base.GetType() == LayerType::Reduce)
+            || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division)
             && (base.GetAdditionalInformation<ActivationDescriptor>() == nullptr))
         {
             for (auto output = base.BeginOutputSlots(); output != base.EndOutputSlots(); ++output)
@@ -414,26 +412,6 @@ OptimizationViews ClBackend::OptimizeSubgraphView(const SubgraphView& subgraph,
                                 }
                             }
                         }
-
-                        // Separate check for Reduce as we aren't fusing with activation layer
-                        if (base.GetType() == LayerType::Reduce)
-                        {
-                            ReduceLayer* baseLayer = PolymorphicDowncast<ReduceLayer*>(&base);
-
-                            // Get params from base layer
-                            ReduceDescriptor reduceDescriptor = baseLayer->GetParameters();
-
-                            arm_compute::Status status = ClReduceWorkloadValidate(
-                                    baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
-                                    baseLayer->GetOutputSlot(0).GetTensorInfo(),
-                                    reduceDescriptor);
-
-                            if (status)
-                            {
-                                ChainReduceLayers<ReduceLayer>(optimizationViews, baseLayer, reduceDescriptor);
-                                untouched.erase(baseLayer->GetGuid());
-                            }
-                        }
                     }
                 }
             }
diff --git a/src/backends/cl/workloads/ClReduceWorkload.cpp b/src/backends/cl/workloads/ClReduceWorkload.cpp
index 0ad6259cc2..6f594ff7a9 100644
--- a/src/backends/cl/workloads/ClReduceWorkload.cpp
+++ b/src/backends/cl/workloads/ClReduceWorkload.cpp
@@ -20,52 +20,23 @@ arm_compute::Status ClReduceWorkloadValidate(const TensorInfo& input,
                                              const ReduceDescriptor& desc)
 {
     const arm_compute::TensorInfo aclInputInfo  = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+    const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+    if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+                                   "ClReduceWorkload: Reduction is supported only on 1 axis.");
+    }
 
     arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclInputInfo.num_dimensions(),
                                                                           input.GetNumDimensions(),
                                                                           desc.m_vAxis);
 
-    // As ACL only support one axis, validate the layer for each axis if more than one is present.
-    if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
-    {
-        arm_compute::Status status;
-
-        for (unsigned int i = 0; i != desc.m_vAxis.size(); ++i)
-        {
-            TensorInfo inputToModify = input;
-            std::vector<uint32_t> singleAxis(1, desc.m_vAxis[i]);
 
-            // Calculate the output shape using the input shape for a single axis.
-            // Currently the output TensorInfo inferred will be reduced upon multiple axis
-            // which will fail validation as only one axis is supported.
-            const TensorShape& reducedShape = ComputeReductionTensorShape(inputToModify, singleAxis, desc.m_KeepDims);
-            inputToModify.SetShape(reducedShape);
-
-            const arm_compute::TensorInfo aclOutputInfoModified =
-                    armcomputetensorutils::BuildArmComputeTensorInfo(inputToModify);
-
-            status = arm_compute::CLReductionOperation::validate(&aclInputInfo,
-                                                                 &aclOutputInfoModified,
-                                                                 static_cast<unsigned int>(coords[i]),
-                                                                 ConvertReductionOperationToAcl(desc),
-                                                                 desc.m_KeepDims);
-            if (!status)
-            {
-                break;
-            }
-        }
-        return status;
-    }
-    else
-    {
-        const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
-
-        return arm_compute::CLReductionOperation::validate(&aclInputInfo,
-                                                           &aclOutputInfo,
-                                                           static_cast<unsigned int>(coords[0]),
-                                                           ConvertReductionOperationToAcl(desc),
-                                                           desc.m_KeepDims);
-    }
+    return arm_compute::CLReductionOperation::validate(&aclInputInfo,
+                                                       &aclOutputInfo,
+                                                       static_cast<unsigned int>(coords[0]),
+                                                       ConvertReductionOperationToAcl(desc),
+                                                       desc.m_KeepDims);
 }
 
 ClReduceWorkload::ClReduceWorkload(const ReduceQueueDescriptor& descriptor, const WorkloadInfo& info)
diff --git a/src/backends/neon/NeonBackend.cpp b/src/backends/neon/NeonBackend.cpp
index 6d5eab0ddf..a1299fb458 100644
--- a/src/backends/neon/NeonBackend.cpp
+++ b/src/backends/neon/NeonBackend.cpp
@@ -29,7 +29,6 @@
 #include "workloads/NeonDivisionWorkload.hpp"
 #include "workloads/NeonFullyConnectedWorkload.hpp"
 #include "workloads/NeonMultiplicationWorkload.hpp"
-#include "workloads/NeonReduceWorkload.hpp"
 #include "workloads/NeonSubtractionWorkload.hpp"
 
 #include <Optimizer.hpp>
@@ -165,8 +164,7 @@ OptimizationViews NeonBackend::OptimizeSubgraphView(const SubgraphView& subgraph
         if ((base.GetType() == LayerType::DepthwiseConvolution2d || base.GetType() == LayerType::Convolution2d
              || base.GetType() == LayerType::BatchNormalization || base.GetType() == LayerType::FullyConnected
              || base.GetType() == LayerType::Addition || base.GetType() == LayerType::Multiplication
-             || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division
-             || base.GetType() == LayerType::Reduce)
+             || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division)
             && (base.GetAdditionalInformation<ActivationDescriptor>() == nullptr))
         {
             for (auto output = base.BeginOutputSlots(); output != base.EndOutputSlots(); ++output)
@@ -391,26 +389,6 @@ OptimizationViews NeonBackend::OptimizeSubgraphView(const SubgraphView& subgraph
                                 }
                             }
                         }
-
-                        // Separate check for Reduce as we aren't fusing with activation layer
-                        if (base.GetType() == LayerType::Reduce)
-                        {
-                            ReduceLayer* baseLayer = PolymorphicDowncast<ReduceLayer*>(&base);
-
-                            // Get params from base layer
-                            ReduceDescriptor reduceDescriptor = baseLayer->GetParameters();
-
-                            arm_compute::Status status = NeonReduceWorkloadValidate(
-                                    baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
-                                    baseLayer->GetOutputSlot(0).GetTensorInfo(),
-                                    reduceDescriptor);
-
-                            if (status)
-                            {
-                                ChainReduceLayers<ReduceLayer>(optimizationViews, baseLayer, reduceDescriptor);
-                                untouched.erase(baseLayer->GetGuid());
-                            }
-                        }
                     }
                 }
             }
diff --git a/src/backends/neon/workloads/NeonReduceWorkload.cpp b/src/backends/neon/workloads/NeonReduceWorkload.cpp
index 6125f3609d..0e1b46a3a1 100644
--- a/src/backends/neon/workloads/NeonReduceWorkload.cpp
+++ b/src/backends/neon/workloads/NeonReduceWorkload.cpp
@@ -21,52 +21,22 @@ arm_compute::Status NeonReduceWorkloadValidate(const TensorInfo& input,
                                                const ReduceDescriptor& desc)
 {
     const arm_compute::TensorInfo aclInputInfo  = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+    const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+    if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+                                   "NeonReduceWorkload: Reduction is supported only on 1 axis.");
+    }
 
     arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclInputInfo.num_dimensions(),
                                                                           input.GetNumDimensions(),
                                                                           desc.m_vAxis);
 
-    // As ACL only support one axis, validate the layer for each axis if more than one is present.
-    if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
-    {
-        arm_compute::Status status;
-
-        for (unsigned int i = 0; i != desc.m_vAxis.size(); ++i)
-        {
-            TensorInfo inputToModify = input;
-            std::vector<uint32_t> singleAxis(1, desc.m_vAxis[i]);
-
-            // Calculate the output shape using the input shape for a single axis.
-            // Currently the output TensorInfo inferred will be reduced upon multiple axis
-            // which will fail validation as only one axis is supported.
-            const TensorShape& reducedShape = ComputeReductionTensorShape(inputToModify, singleAxis, desc.m_KeepDims);
-            inputToModify.SetShape(reducedShape);
-
-            const arm_compute::TensorInfo aclOutputInfoModified =
-                    armcomputetensorutils::BuildArmComputeTensorInfo(inputToModify);
-
-            status = arm_compute::NEReductionOperation::validate(&aclInputInfo,
-                                                                 &aclOutputInfoModified,
-                                                                 static_cast<unsigned int>(coords[i]),
-                                                                 ConvertReductionOperationToAcl(desc),
-                                                                 desc.m_KeepDims);
-            if (!status)
-            {
-                break;
-            }
-        }
-        return status;
-    }
-    else
-    {
-        const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
-
-        return arm_compute::NEReductionOperation::validate(&aclInputInfo,
-                                                           &aclOutputInfo,
-                                                           static_cast<unsigned int>(coords[0]),
-                                                           ConvertReductionOperationToAcl(desc),
-                                                           desc.m_KeepDims);
-    }
+    return arm_compute::NEReductionOperation::validate(&aclInputInfo,
+                                                       &aclOutputInfo,
+                                                       static_cast<unsigned int>(coords[0]),
+                                                       ConvertReductionOperationToAcl(desc),
+                                                       desc.m_KeepDims);
 }
 
 NeonReduceWorkload::NeonReduceWorkload(const ReduceQueueDescriptor& descriptor, const WorkloadInfo& info)
@@ -80,7 +50,6 @@ NeonReduceWorkload::NeonReduceWorkload(const ReduceQueueDescriptor& descriptor,
     arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(input.info()->num_dimensions(),
                                                                           info.m_InputTensorInfos[0].GetNumDimensions(),
                                                                           m_Data.m_Parameters.m_vAxis);
-
     m_Layer.configure(&input,
                       &output,
                       static_cast<unsigned int>(coords[0]),