diff --git a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp
index 05b5899..f66849a 100644
--- a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp
+++ b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp
@@ -1,17 +1,12 @@
 //
-// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include "NeonBatchToSpaceNdWorkload.hpp"
 
-#include "NeonWorkloadUtils.hpp"
-
-#include <armnn/utility/NumericCast.hpp>
 #include <armnn/utility/PolymorphicDowncast.hpp>
 
-#include <ResolveType.hpp>
-
 namespace armnn
 {
 
@@ -21,21 +16,71 @@
                                                        const TensorInfo& output,
                                                        const BatchToSpaceNdDescriptor& descriptor)
 {
-    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
+    arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
+    arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
 
-    // ArmNN blockShape is [H, W] Cl asks for W, H
+    arm_compute::Status statusBatchToSpace  = arm_compute::Status(arm_compute::ErrorCode::OK);
+    arm_compute::Status statusReshapeInput  = arm_compute::Status(arm_compute::ErrorCode::OK);
+    arm_compute::Status statusReshapeOutput = arm_compute::Status(arm_compute::ErrorCode::OK);
+
+    arm_compute::TensorInfo aclReshapeInputInfo  = aclInputInfo;
+    arm_compute::TensorInfo aclReshapeOutputInfo = aclOutputInfo;
+
+    // When a spacial dimension is missing (rank=3) set W to 1
+    const unsigned int rank = input.GetNumDimensions();
+    if (rank == 3)
+    {
+        const arm_compute::TensorShape inputShape  = aclInputInfo.tensor_shape();
+        const arm_compute::TensorShape outputShape = aclOutputInfo.tensor_shape();
+
+        if (descriptor.m_DataLayout == armnn::DataLayout::NHWC)
+        {
+            // In ACL dimensions are right to left: C, W, H, N
+            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
+        }
+        else if (descriptor.m_DataLayout == armnn::DataLayout::NCHW)
+        {
+            // In ACL dimensions are right to left: W, H, C, N
+            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
+        }
+        else
+        {
+            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
+        }
+
+        statusReshapeInput = arm_compute::NEReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo);
+        statusReshapeOutput = arm_compute::NEReshapeLayer::validate(&aclReshapeOutputInfo, &aclOutputInfo);
+    }
+
+    // ArmNN blockShape is [H, W] ACl asks for W, H
     int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
-    int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
+    int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
 
-    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor);
+    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor, rank);
 
-    const arm_compute::Status aclStatus = arm_compute::NEBatchToSpaceLayer::validate(&aclInputInfo,
-                                                                                     blockWidth,
-                                                                                     blockHeight,
-                                                                                     &aclOutputInfo,
-                                                                                     cropInfo);
-    return aclStatus;
+    statusBatchToSpace = arm_compute::NEBatchToSpaceLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo,
+                                                                    blockWidth,
+                                                                    blockHeight,
+                                                                    rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo,
+                                                                    cropInfo);
+
+    if (statusReshapeInput.error_code()  == arm_compute::ErrorCode::OK &&
+        statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK &&
+        statusBatchToSpace.error_code()  == arm_compute::ErrorCode::OK)
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::OK,
+                                   "All BatchToSpace layers validate status OK.");
+    }
+    else
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+                                   "BatchToSpace layer validate status failed."
+                                   + statusBatchToSpace.error_description()
+                                   + statusReshapeInput.error_description()
+                                   + statusReshapeOutput.error_description());
+    }
 }
 
 NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& descriptor,
@@ -50,33 +95,85 @@
 
     m_Data.ValidateInputsOutputs("NeonBatchToSpaceNdWorkload", 1, 1);
 
-    arm_compute::ITensor& input  =
-            armnn::PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
-    arm_compute::ITensor& output =
-            armnn::PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
+    arm_compute::ITensor& input  = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& output = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
 
     arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
     input.info()->set_data_layout(aclDataLayout);
     output.info()->set_data_layout(aclDataLayout);
 
-    // ArmNN blockShape is [H, W] Cl asks for W, H
-    int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[0]);
-    int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
+    arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0],
+                                                                            m_Data.m_Parameters.m_DataLayout);
+    arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0],
+                                                                             m_Data.m_Parameters.m_DataLayout);
 
-    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor.m_Parameters);
+    const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions();
+    if (rank == 3)
+    {
+        const arm_compute::TensorShape inputShape  = input.info()->tensor_shape();
+        const arm_compute::TensorShape outputShape = output.info()->tensor_shape();
+
+        // When a spacial dimension is missing set W to 1
+        if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC)
+        {
+            // In ACL dimensions are right to left: C, W, H, N
+            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
+        }
+        else if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NCHW)
+        {
+            // In ACL dimensions are right to left: W, H, C, N
+            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
+        }
+        else
+        {
+            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
+        }
+
+        m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo);
+        m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo);
+
+        InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor);
+        InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor);
+
+        m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer());
+        m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer());
+
+        m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor);
+        m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output);
+    }
+
+    // ArmNN blockShape is [H, W] ACl asks for W, H
+    int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[0]);
+    int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
+
+    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor.m_Parameters, rank);
 
     m_Layer.reset(new arm_compute::NEBatchToSpaceLayer());
-    m_Layer->configure(&input, blockWidth, blockHeight, &output, cropInfo);
+    m_Layer->configure(rank == 3 ? &m_ReshapeInputTensor : &input,
+                       blockWidth,
+                       blockHeight,
+                       rank == 3 ? &m_ReshapeOutputTensor : &output,
+                       cropInfo);
     m_Layer->prepare();
 }
 
 void NeonBatchToSpaceNdWorkload::Execute() const
 {
+    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonBatchToSpaceNdWorkload_Execute", this->GetGuid());
+    if (m_LayerReshapeInput)
+    {
+        m_LayerReshapeInput->run();
+    }
     if (m_Layer)
     {
-        ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid());
         m_Layer->run();
     }
+    if (m_LayerReshapeOutput)
+    {
+        m_LayerReshapeOutput->run();
+    }
 }
 
 } //namespace armnn
diff --git a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.hpp b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.hpp
index 55f773e..ef5bd13 100644
--- a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.hpp
+++ b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.hpp
@@ -1,16 +1,15 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #pragma once
 
-#include <armnn/Tensor.hpp>
-#include <armnn/Descriptors.hpp>
-
 #include "NeonBaseWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
 
 #include <arm_compute/runtime/NEON/functions/NEBatchToSpaceLayer.h>
+#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h>
 
 namespace armnn
 {
@@ -30,6 +29,10 @@
 
 private:
     mutable std::unique_ptr<arm_compute::NEBatchToSpaceLayer> m_Layer;
+    mutable std::unique_ptr<arm_compute::NEReshapeLayer> m_LayerReshapeInput;
+    mutable std::unique_ptr<arm_compute::NEReshapeLayer> m_LayerReshapeOutput;
+    arm_compute::Tensor m_ReshapeInputTensor;
+    arm_compute::Tensor m_ReshapeOutputTensor;
 };
 
 }
diff --git a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp
index e0adc62..291fa81 100644
--- a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp
+++ b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp
@@ -5,13 +5,8 @@
 
 #include "NeonSpaceToBatchNdWorkload.hpp"
 
-#include "NeonWorkloadUtils.hpp"
-
-#include <armnn/utility/NumericCast.hpp>
 #include <armnn/utility/PolymorphicDowncast.hpp>
 
-#include <ResolveType.hpp>
-
 namespace armnn
 {
 
@@ -21,24 +16,77 @@
                                                        const TensorInfo& output,
                                                        const SpaceToBatchNdDescriptor& descriptor)
 {
-    const arm_compute::TensorInfo aclInputInfo  = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
+    arm_compute::TensorInfo aclInputInfo  = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
+    arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
 
-    // ArmNN blockShape is [H, W] Cl asks for W, H
+    arm_compute::Status statusSpaceToBatch  = arm_compute::Status(arm_compute::ErrorCode::OK);
+    arm_compute::Status statusReshapeInput  = arm_compute::Status(arm_compute::ErrorCode::OK);
+    arm_compute::Status statusReshapeOutput = arm_compute::Status(arm_compute::ErrorCode::OK);
+
+    arm_compute::TensorInfo aclReshapeInputInfo  = aclInputInfo;
+    arm_compute::TensorInfo aclReshapeOutputInfo = aclOutputInfo;
+
+    // When a spacial dimension is missing (rank=3) set W to 1
+    const unsigned int rank = input.GetNumDimensions();
+    if (rank == 3)
+    {
+        const arm_compute::TensorShape inputShape  = aclInputInfo.tensor_shape();
+        const arm_compute::TensorShape outputShape = aclOutputInfo.tensor_shape();
+
+        if (descriptor.m_DataLayout == armnn::DataLayout::NHWC)
+        {
+            // In ACL dimensions are right to left: C, W, H, N
+            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
+        }
+        else if (descriptor.m_DataLayout == armnn::DataLayout::NCHW)
+        {
+            // In ACL dimensions are right to left: W, H, C, N
+            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
+        }
+        else
+        {
+            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
+        }
+
+        statusReshapeInput = arm_compute::NEReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo);
+        statusReshapeOutput = arm_compute::NEReshapeLayer::validate(&aclReshapeOutputInfo, &aclOutputInfo);
+    }
+
+    // ArmNN blockShape is [H, W] ACl asks for W, H
     int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
-    int32_t blockWidth  = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
+    int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
 
-    arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(
-            descriptor.m_PadList[1].first, descriptor.m_PadList[0].first);
-    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(
-            descriptor.m_PadList[1].second, descriptor.m_PadList[0].second);
+    unsigned int padLeft  = (rank == 3) ? 0 : descriptor.m_PadList[1].first;
+    unsigned int padRight = (rank == 3) ? 0 : descriptor.m_PadList[1].second;
+    arm_compute::Size2D paddingLeftTop      = BuildArmComputeSize2D(padLeft,
+                                                                    descriptor.m_PadList[0].first);
+    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(padRight,
+                                                                    descriptor.m_PadList[0].second);
 
-    return arm_compute::NESpaceToBatchLayer::validate(&aclInputInfo,
-                                                      blockWidth,
-                                                      blockHeight,
-                                                      paddingLeftTop,
-                                                      paddingRightBottom,
-                                                      &aclOutputInfo);
+    statusSpaceToBatch = arm_compute::NESpaceToBatchLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo,
+                                                                    blockWidth,
+                                                                    blockHeight,
+                                                                    paddingLeftTop,
+                                                                    paddingRightBottom,
+                                                                    rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo);
+
+    if (statusReshapeInput.error_code()  == arm_compute::ErrorCode::OK &&
+        statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK &&
+        statusSpaceToBatch.error_code()  == arm_compute::ErrorCode::OK)
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::OK,
+                                   "All SpaceToBatch layers validate status OK.");
+    }
+    else
+    {
+        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+                                   "SpaceToBatch layer validate status failed."
+                                   + statusSpaceToBatch.error_description()
+                                   + statusReshapeInput.error_description()
+                                   + statusReshapeOutput.error_description());
+    }
 }
 
 NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor& descriptor,
@@ -53,41 +101,91 @@
 
     m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1);
 
-    arm_compute::ITensor& input  =
-            PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
-    arm_compute::ITensor& output =
-            PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
-
-    // ArmNN blockShape is [H, W] Cl asks for W, H
-    int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
-    int32_t blockWidth  = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]);
-
-    arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(
-            m_Data.m_Parameters.m_PadList[1].first, m_Data.m_Parameters.m_PadList[0].first);
-    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(
-            m_Data.m_Parameters.m_PadList[1].second, m_Data.m_Parameters.m_PadList[0].second);
+    arm_compute::ITensor& input  = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& output = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
 
     arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
     input.info()->set_data_layout(aclDataLayout);
     output.info()->set_data_layout(aclDataLayout);
 
+    arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0],
+                                                                            m_Data.m_Parameters.m_DataLayout);
+    arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0],
+                                                                             m_Data.m_Parameters.m_DataLayout);
+
+    const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions();
+    if (rank == 3)
+    {
+        const arm_compute::TensorShape inputShape  = input.info()->tensor_shape();
+        const arm_compute::TensorShape outputShape = output.info()->tensor_shape();
+
+        // When a spacial dimension is missing set W to 1
+        if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC)
+        {
+            // In ACL dimensions are right to left: C, W, H, N
+            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
+        }
+        else if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NCHW)
+        {
+            // In ACL dimensions are right to left: W, H, C, N
+            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
+            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
+        }
+        else
+        {
+            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
+        }
+
+        m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo);
+        m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo);
+
+        InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor);
+        InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor);
+
+        m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer());
+        m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer());
+
+        m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor);
+        m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output);
+    }
+
+    // ArmNN blockShape is [H, W] ACl asks for W, H
+    int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
+    int32_t blockWidth = (rank == 3) ? 1: armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
+
+    unsigned int padLeft  = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].first;
+    unsigned int padRight = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].second;
+    arm_compute::Size2D paddingLeftTop      = BuildArmComputeSize2D(padLeft,
+                                                                    descriptor.m_Parameters.m_PadList[0].first);
+    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(padRight,
+                                                                    descriptor.m_Parameters.m_PadList[0].second);
+
     m_Layer.reset(new arm_compute::NESpaceToBatchLayer());
-    m_Layer->configure(&input,
+    m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input,
                        blockWidth,
                        blockHeight,
                        paddingLeftTop,
                        paddingRightBottom,
-                       &output);
+                       (rank == 3) ? &m_ReshapeOutputTensor : &output);
     m_Layer->prepare();
 }
 
 void NeonSpaceToBatchNdWorkload::Execute() const
 {
+    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid());
+    if (m_LayerReshapeInput)
+    {
+        m_LayerReshapeInput->run();
+    }
     if (m_Layer)
     {
-        ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid());
         m_Layer->run();
     }
+    if (m_LayerReshapeOutput)
+    {
+        m_LayerReshapeOutput->run();
+    }
 }
 
 } //namespace armnn
\ No newline at end of file
diff --git a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.hpp b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.hpp
index 4bd7d2d..35d70d3 100644
--- a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.hpp
+++ b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.hpp
@@ -1,16 +1,15 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #pragma once
 
-#include <armnn/Tensor.hpp>
-#include <armnn/Descriptors.hpp>
-
 #include "NeonBaseWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
 
 #include <arm_compute/runtime/NEON/functions/NESpaceToBatchLayer.h>
+#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h>
 
 namespace armnn
 {
@@ -30,6 +29,10 @@
 
 private:
     mutable std::unique_ptr<arm_compute::NESpaceToBatchLayer> m_Layer;
+    mutable std::unique_ptr<arm_compute::NEReshapeLayer> m_LayerReshapeInput;
+    mutable std::unique_ptr<arm_compute::NEReshapeLayer> m_LayerReshapeOutput;
+    arm_compute::Tensor m_ReshapeInputTensor;
+    arm_compute::Tensor m_ReshapeOutputTensor;
 };
 
 } //namespace armnn
\ No newline at end of file