diff --git a/src/backends/neon/workloads/NeonGatherNdWorkload.cpp b/src/backends/neon/workloads/NeonGatherNdWorkload.cpp
new file mode 100644
index 0000000..00c66cf
--- /dev/null
+++ b/src/backends/neon/workloads/NeonGatherNdWorkload.cpp
@@ -0,0 +1,147 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonGatherNdWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
+#include <armnn/utility/PolymorphicDowncast.hpp>
+#include <aclCommon/ArmComputeUtils.hpp>
+#include "backendsCommon/WorkloadUtils.hpp"
+
+namespace armnn
+{
+arm_compute::Status NeonGatherNdWorkloadValidate(const TensorInfo& paramInfo,
+                                                 const TensorInfo& indicesInfo,
+                                                 const TensorInfo& outputInfo)
+{
+    // Calculate ND, K, W, C.
+    std::map<std::string, unsigned int> keyIndices = CalculateGatherNdKeyIndices(paramInfo, indicesInfo);
+
+    /// Call Gather with adequate shapes
+    // Reshape params into { K, C }
+    armnn::TensorInfo params_K_C_Info =  paramInfo;
+    params_K_C_Info.SetShape({ keyIndices["K"], keyIndices["C"] });
+
+    // Reshape indices into { W }
+    armnn::TensorInfo indices_W_Info = indicesInfo;
+    indices_W_Info.SetShape({ keyIndices["W"] });
+
+    // Reshape output to have the shape given by gather { W, C }
+    // (the original outputInfo has the shape given by gatherNd)
+    armnn::TensorInfo outputGather_Info = outputInfo;
+    outputGather_Info.SetShape({ keyIndices["W"], keyIndices["C"] });
+
+    const arm_compute::TensorInfo aclParamsInfo  = BuildArmComputeTensorInfo(params_K_C_Info);
+    const arm_compute::TensorInfo aclIndicesInfo = BuildArmComputeTensorInfo(indices_W_Info);
+    const arm_compute::TensorInfo aclOutputInfo  = BuildArmComputeTensorInfo(outputGather_Info);
+
+    auto aclAxis = ComputeAclAxis(0, params_K_C_Info);
+    return arm_compute::NEGather::validate(&aclParamsInfo, &aclIndicesInfo, &aclOutputInfo, aclAxis);
+}
+
+NeonGatherNdWorkload::NeonGatherNdWorkload(const GatherNdQueueDescriptor& descriptor,
+                                           const WorkloadInfo& info)
+        : NeonBaseWorkload<GatherNdQueueDescriptor>(descriptor, info)
+{
+    m_Data.ValidateInputsOutputs("NeonGatherNdWorkload", 2, 1);
+
+    TensorInfo paramsInfo  = info.m_InputTensorInfos[0];
+    TensorInfo indicesInfo = info.m_InputTensorInfos[1];
+    TensorInfo outputInfo  = info.m_OutputTensorInfos[0];
+
+    arm_compute::ITensor& input   = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& indices = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+    arm_compute::ITensor& output  = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+    // Calculate ND, K, W, C.
+    std::map<std::string, unsigned int> keyIndices = CalculateGatherNdKeyIndices(paramsInfo, indicesInfo);
+
+    /// Calculate flattened indices: m_FlattenedIndices = indices * m_FlattenedCoeff.
+    /// This could be done using MatMul instead of multiplication followed by reduce sum operation,
+    /// but GeMM does not support s32 at the moment.
+
+    // Prepare the tensor to store the output of the reduce_sum operation
+    armnn::TensorInfo flattenedIndices_Info = indicesInfo;
+    flattenedIndices_Info.SetShape({ keyIndices["W"] });
+    BuildArmComputeTensor(m_FlattenedIndices, flattenedIndices_Info);
+    armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedIndices);
+
+    // Reshape indices into { W, ND }
+    indices.info()->set_tensor_shape(BuildArmComputeTensorShape({ keyIndices["W"], keyIndices["ND"] }));
+
+    // Calculate the m_FlattenedCoeff
+    TensorShape paramsShape = paramsInfo.GetShape();
+    std::vector<unsigned int> flattenedCoeff(keyIndices["ND"], 1);
+    for (unsigned int i = 1; i < keyIndices["ND"]; ++i)
+    {
+        flattenedCoeff[i - 1] = paramsShape[i];
+    }
+    for (unsigned int i = keyIndices["ND"] - 1; i > 0; --i)
+    {
+        flattenedCoeff[i - 1] *= flattenedCoeff[i];
+    }
+    armnn::TensorInfo flattenedCoeff_Info = indicesInfo;
+    flattenedCoeff_Info.SetShape({ keyIndices["ND"] });
+    BuildArmComputeTensor(m_FlattenedCoeff, flattenedCoeff_Info);
+    armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedCoeff);
+    CopyArmComputeITensorData(flattenedCoeff.data(), m_FlattenedCoeff);
+
+    // Prepare the tensor to store the output of the multiplication
+    armnn::TensorInfo outputMul_Info = indicesInfo;
+    outputMul_Info.SetShape({ keyIndices["W"], keyIndices["ND"] });
+    BuildArmComputeTensor(m_outputMul, outputMul_Info);
+    armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_outputMul);
+
+    // Multiply
+    auto convertPolicy = (IsQuantizedType(info.m_InputTensorInfos[0].GetDataType()) ||
+                          IsQuantizedType(info.m_InputTensorInfos[1].GetDataType())) ?
+                          arm_compute::ConvertPolicy::SATURATE :
+                          arm_compute::ConvertPolicy::WRAP;
+
+    m_MulLayer.configure(&indices,
+                         &m_FlattenedCoeff,
+                         &m_outputMul,
+                         1.0f,
+                         convertPolicy,
+                         arm_compute::RoundingPolicy::TO_ZERO,
+                         arm_compute::ActivationLayerInfo());
+
+    // Reduce Sum
+    const std::vector<unsigned int> armnnReduceAxes(1, 1);
+    arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(m_outputMul.info()->num_dimensions(),
+                                                                          outputMul_Info.GetNumDimensions(),
+                                                                          armnnReduceAxes);
+    m_ReduceSumLayer.configure(&m_outputMul,
+                               &m_FlattenedIndices,
+                               static_cast<unsigned int>(coords[0]),
+                               arm_compute::ReductionOperation::SUM,
+                               false);
+
+    /// Call Gather with adequate shapes
+    // Reshape params into { K, C }
+    paramsInfo.SetShape({ keyIndices["K"], keyIndices["C"] });
+    input.info()->set_tensor_shape(BuildArmComputeTensorShape(paramsInfo.GetShape()));
+
+    // Reshape output to have the shape given by gather { W, C }
+    // (the original outputInfo has the shape given by gatherNd)
+    armnn::TensorInfo outputGather_Info = outputInfo;
+    outputGather_Info.SetShape({ keyIndices["W"], keyIndices["C"] });
+    BuildArmComputeTensor(m_outputGather, outputGather_Info);
+    armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_outputGather);
+
+    m_GatherLayer.configure(&input, &m_FlattenedIndices, &m_outputGather, ComputeAclAxis(0, paramsInfo));
+
+    // Reshape output to the original output shape
+    m_ReshapeLayer.configure(&m_outputGather, &output);
+}
+
+void NeonGatherNdWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonGatherNdWorkload_Execute", this->GetGuid());
+    m_MulLayer.run();
+    m_ReduceSumLayer.run();
+    m_GatherLayer.run();
+    m_ReshapeLayer.run();
+}
+} //namespace armnn
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