diff --git a/delegate/classic/src/Unpack.hpp b/delegate/classic/src/Unpack.hpp
new file mode 100644
index 0000000..c9b7370
--- /dev/null
+++ b/delegate/classic/src/Unpack.hpp
@@ -0,0 +1,214 @@
+//
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/utility/IgnoreUnused.hpp>
+
+#include <DelegateUtils.hpp>
+
+#include <tensorflow/lite/builtin_ops.h>
+#include <tensorflow/lite/c/builtin_op_data.h>
+#include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/minimal_logging.h>
+#include <numeric>
+
+namespace armnnDelegate
+{
+
+TfLiteStatus VisitUnpackOperator(DelegateData& delegateData,
+                                 TfLiteContext* tfLiteContext,
+                                 TfLiteNode* tfLiteNode,
+                                 int nodeIndex,
+                                 int32_t operatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Get Unpack Axis
+    const auto params = reinterpret_cast<TfLiteUnpackParams*>(tfLiteNode->builtin_data);
+
+    const unsigned int unpackAxis = NonNegative(params->axis, nodeIndex);
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+
+    if (unpackAxis >= inputTensorInfo.GetNumDimensions())
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: The unpack axis #%d cannot be greater than or equal to "
+            "the number of input dimensions #%d in operator #%d node #%d",
+            unpackAxis, inputTensorInfo.GetNumDimensions(), operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Get Unpack Num
+    unsigned int unpackNum = NonNegative(params->num, nodeIndex);
+
+    // If num is not defined, automatically infer from the length of the dimension axis.
+    if(unpackNum == 0)
+    {
+        unpackNum = inputTensorInfo.GetShape()[unpackAxis];
+    }
+
+    // If unpack number cannot be inferred and is still zero, return kTfLiteError.
+    if(unpackNum == 0)
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Number to unpack must greater than zero in operator #%d node #%d: ",
+            operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Check outputs
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, unpackNum, nodeIndex));
+
+
+    auto inputDimSize = inputTensorInfo.GetNumDimensions();
+    std::vector<unsigned int> unpackDimSizes(inputDimSize);
+
+    // Add current input shape to unpackDimSizes
+    for (unsigned int i = 0; i < inputDimSize; ++i)
+    {
+        unpackDimSizes[i] = inputTensorInfo.GetShape()[i];
+    }
+
+    if (unpackDimSizes[unpackAxis] != unpackNum)
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Number to unpack must be the same as length "
+            "of the dimension to unpack along in operator #%d node #%d: ",
+            operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    unpackDimSizes[unpackAxis] /= unpackNum;
+
+    armnn::SplitterDescriptor splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size()));
+    for (unsigned int j = 0; j < unpackNum; ++j)
+    {
+        // Set the size of the views.
+        for (unsigned int dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx)
+        {
+            splitDesc.SetViewSize(j, dimIdx, unpackDimSizes[dimIdx]);
+        }
+        splitDesc.SetViewOriginCoord(j, unpackAxis, unpackDimSizes[unpackAxis] * j);
+    }
+
+    std::vector<armnn::TensorInfo> outputs;
+    for (unsigned int i = 0; i < unpackNum; ++i)
+    {
+        const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]];
+        if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
+        {
+            return kTfLiteError;
+        }
+        outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true));
+    }
+    const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end());
+
+    // Determine the shape of the Splitter layer outputs for validation
+    armnn::TensorShape splitOutShape = armnn::TensorShape(static_cast<unsigned int>(unpackDimSizes.size()),
+                                                          unpackDimSizes.data());
+
+    std::vector<armnn::TensorInfo> splitterOutputs;
+    for (unsigned int outputIndex = 0; outputIndex < outputTensorInfos.size(); ++outputIndex)
+    {
+        splitterOutputs.push_back(armnn::TensorInfo(splitOutShape,
+                                                    outputTensorInfos[outputIndex].get().GetDataType(),
+                                                    outputTensorInfos[outputIndex].get().GetQuantizationScale(),
+                                                    outputTensorInfos[outputIndex].get().GetQuantizationOffset()));
+    }
+    std::vector<std::reference_wrapper<armnn::TensorInfo>> splitterOutputTensorInfos(splitterOutputs.begin(),
+                                                                                     splitterOutputs.end());
+
+    armnn::BackendId setBackendSplit;
+    if (!delegateData.m_Network)
+    {
+        // Check if splitter is supported
+        bool isSupported = false;
+        FORWARD_LAYER_SUPPORT_FUNC("UNPACK",
+                                   tfLiteContext,
+                                   IsSplitterSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   setBackendSplit,
+                                   inputTensorInfo,
+                                   splitterOutputTensorInfos,
+                                   splitDesc);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Create Reshape descriptor from the first outputTensorInfo to validate a single Reshape layer
+    // Use this descriptor later when creating every ReshapeLayer as all Reshape Layers should be the same
+    armnn::ReshapeDescriptor reshapeDescriptor;
+    reshapeDescriptor.m_TargetShape = outputTensorInfos[0].get().GetShape();
+
+    armnn::BackendId setBackendReshape;
+    if (!delegateData.m_Network)
+    {
+        bool isSupported = false;
+        FORWARD_LAYER_SUPPORT_FUNC("RESHAPE",
+                                   tfLiteContext,
+                                   IsReshapeSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   setBackendReshape,
+                                   splitterOutputTensorInfos[0],
+                                   outputTensorInfos[0],
+                                   reshapeDescriptor);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    };
+
+    std::string splitterLayerName("Unpack Splitter");
+
+    armnn::IConnectableLayer* splitterLayer = delegateData.m_Network->AddSplitterLayer(splitDesc,
+                                                                                       splitterLayerName.c_str());
+    splitterLayer->SetBackendId(setBackendSplit);
+    ARMNN_ASSERT(splitterLayer != nullptr);
+
+    for (unsigned int k = 0; k < splitterLayer->GetNumOutputSlots(); ++k)
+    {
+        splitterLayer->GetOutputSlot(k).SetTensorInfo(outputs[k]);
+    }
+
+    // Connect the input slots
+    delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(splitterLayer->GetInputSlot(0));
+
+    // Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter.
+    for (unsigned int outputIndex = 0; outputIndex < splitterLayer->GetNumOutputSlots(); ++outputIndex)
+    {
+        std::string reshapeLayerName("Unpack Reshape");
+        armnn::IConnectableLayer* reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor,
+                                                                                         reshapeLayerName.c_str());
+        reshapeLayer->SetBackendId(setBackendReshape);
+        ARMNN_ASSERT(reshapeLayer != nullptr);
+
+        splitterLayer->GetOutputSlot(outputIndex).SetTensorInfo(splitterOutputTensorInfos[outputIndex]);
+        splitterLayer->GetOutputSlot(outputIndex).Connect(reshapeLayer->GetInputSlot(0));
+
+        armnn::TensorInfo outputTensorInfo  = outputTensorInfos[outputIndex];
+        reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+        armnn::IOutputSlot& slot = reshapeLayer->GetOutputSlot(0);
+
+        delegateData.m_OutputSlotForNode[
+            static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &slot;
+
+    }
+
+    return kTfLiteOk;
+}
+
+} // namespace armnnDelegate