IVGCVSW-7834 Add REVERSE_V2 to classic and opaque delegates

  * Adding support for ReverseV2 in the classic and opaque delegates
  * CMake files updated
  * Tests added
  * Gpu/Cpu Acc tests compiled out until functionality is written

Signed-off-by: Tracy Narine <tracy.narine@arm.com>
Change-Id: I8b41b7e71f2e28e5ea8dddbd00657900e6d7ab9a

10 files changed, 679 insertions, 0 deletions

diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index bb552d33bb..e46ac04092 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -182,6 +182,8 @@ if(BUILD_UNIT_TESTS)
         test/ReshapeTest.cpp
         test/ResizeTest.cpp
         test/ResizeTestHelper.hpp
+        test/ReverseV2Test.cpp
+        test/ReverseV2TestHelper.hpp
         test/RoundTest.cpp
         test/RoundTestHelper.hpp
         test/SoftmaxTest.cpp
diff --git a/delegate/classic/CMakeLists.txt b/delegate/classic/CMakeLists.txt
index 54e00e1c9f..8f872d6adc 100644
--- a/delegate/classic/CMakeLists.txt
+++ b/delegate/classic/CMakeLists.txt
@@ -34,6 +34,7 @@ list(APPEND armnnClassicDelegateObject_sources
         src/Redefine.hpp
         src/Reduce.hpp
         src/Resize.hpp
+        src/ReverseV2.hpp
         src/Round.hpp
         src/Shape.hpp
         src/SharedFunctions.hpp
diff --git a/delegate/classic/src/ReverseV2.hpp b/delegate/classic/src/ReverseV2.hpp
new file mode 100644
index 0000000000..d49d20b5c1
--- /dev/null
+++ b/delegate/classic/src/ReverseV2.hpp
@@ -0,0 +1,154 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <ClassicDelegateUtils.hpp>
+
+#include <armnn/utility/IgnoreUnused.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 <tensorflow/lite/kernels/internal/tensor_ctypes.h>
+
+namespace armnnDelegate
+{
+
+
+
+TfLiteStatus ValidateReverseV2Operator(DelegateData& delegateData,
+                                       TfLiteContext* tfLiteContext,
+                                       const armnn::TensorInfo& inputInfo0,
+                                       const armnn::TensorInfo& inputInfo1,
+                                       const armnn::TensorInfo& outputInfo)
+{
+    bool isSupported = false;
+    FORWARD_LAYER_SUPPORT_FUNC("REVERSEV2",
+                               tfLiteContext,
+                               IsReverseV2Supported,
+                               delegateData.m_Backends,
+                               isSupported,
+                               armnn::BackendId(),
+                               inputInfo0,
+                               inputInfo1,
+                               outputInfo);
+
+    return isSupported ? kTfLiteOk : kTfLiteError;
+}
+
+TfLiteStatus VisitReverseV2Operator(DelegateData& delegateData,
+                                    TfLiteContext* tfLiteContext,
+                                    TfLiteNode* tfLiteNode,
+                                    int nodeIndex,
+                                    int32_t reverseV2OperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+
+    // The first input contains the data that should be reversed
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+    if (IsDynamicTensor(tfLiteInputTensor))
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The second input contains an axis tensor.
+    const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+    if (IsDynamicTensor(tfLiteAxisTensor))
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Get the output tensor
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (IsDynamicTensor(tfLiteOutputTensor))
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
+
+    if (inputTensorInfo0.GetNumDimensions() != outputTensorInfo.GetNumDimensions())
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: input tensor dimension and output tensor dimension differ #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    for (unsigned i=0; i < inputTensorInfo0.GetNumDimensions(); i++)
+    {
+        if (inputTensorInfo0.GetShape()[i] != outputTensorInfo.GetShape()[i])
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnDelegate: input tensor dimension and output tensor differ #%d node #%d: ",
+                reverseV2OperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+    }
+
+    std::string layerName("ReverseV2");
+
+    const auto maxDimension = 4;
+
+    const auto axisTensorNumValues = static_cast<unsigned int>(tfLiteAxisTensor.dims->size);
+    if (axisTensorNumValues > maxDimension)
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: The Axis-Input-Tensor of the ReverseV2 operation requires a "
+            "dimension of <= %d but a tensor with a dimension of %d was given.                "
+            "Operator: #%d node #%d: ",
+            maxDimension, axisTensorNumValues, reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // No network pointer indicates that only support for this operator should be checked
+    if (!delegateData.m_Network)
+    {
+        return ValidateReverseV2Operator(delegateData,
+                                         tfLiteContext,
+                                         inputTensorInfo0,
+                                         inputTensorInfo1,
+                                         outputTensorInfo);
+    }
+
+    armnn::IConnectableLayer* reverseV2Layer = delegateData.m_Network->AddReverseV2Layer(layerName.c_str());
+
+    armnn::IOutputSlot& outputSlot = reverseV2Layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // Try to connect the Constant Inputs if there are any
+    if(ProcessInputs(reverseV2Layer, delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    ARMNN_ASSERT(reverseV2Layer != nullptr);
+
+    return Connect(reverseV2Layer, tfLiteNode, delegateData);
+}
+
+} // namespace armnnDelegate
diff --git a/delegate/classic/src/armnn_delegate.cpp b/delegate/classic/src/armnn_delegate.cpp
index e597d13fb2..0f9e8a624c 100644
--- a/delegate/classic/src/armnn_delegate.cpp
+++ b/delegate/classic/src/armnn_delegate.cpp
@@ -31,6 +31,7 @@
 #include "Redefine.hpp"
 #include "Reduce.hpp"
 #include "Resize.hpp"
+#include "ReverseV2.hpp"
 #include "Round.hpp"
 #include "Shape.hpp"
 #include "Slice.hpp"
@@ -949,6 +950,12 @@ TfLiteStatus ArmnnSubgraph::VisitNode(DelegateData& delegateData,
                                        tfLiteNode,
                                        nodeIndex,
                                        kTfLiteBuiltinResizeNearestNeighbor);
+        case kTfLiteBuiltinReverseV2:
+            return VisitReverseV2Operator(delegateData,
+                                          tfLiteContext,
+                                          tfLiteNode,
+                                          nodeIndex,
+                                          kTfLiteBuiltinReverseV2);
         case kTfLiteBuiltinRsqrt:
             return VisitElementwiseUnaryOperator(delegateData,
                                                  tfLiteContext,
diff --git a/delegate/opaque/CMakeLists.txt b/delegate/opaque/CMakeLists.txt
index 1e00709f01..787046d80c 100644
--- a/delegate/opaque/CMakeLists.txt
+++ b/delegate/opaque/CMakeLists.txt
@@ -31,6 +31,7 @@ list(APPEND armnnOpaqueDelegateObject_sources
         src/Redefine.hpp
         src/Reduce.hpp
         src/Resize.hpp
+        src/ReverseV2.hpp
         src/Round.hpp
         src/Shape.hpp
         src/SharedFunctions.cpp
diff --git a/delegate/opaque/src/ReverseV2.hpp b/delegate/opaque/src/ReverseV2.hpp
new file mode 100644
index 0000000000..e5714f4576
--- /dev/null
+++ b/delegate/opaque/src/ReverseV2.hpp
@@ -0,0 +1,174 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+
+TfLiteStatus ValidateReverseV2Operator(DelegateData& delegateData,
+                                       TfLiteOpaqueContext* tfLiteContext,
+                                       const armnn::TensorInfo& inputInfo0,
+                                       const armnn::TensorInfo& inputInfo1,
+                                       const armnn::TensorInfo& outputInfo)
+{
+    bool isSupported = false;
+    FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("REVERSEV2",
+                                       tfLiteContext,
+                                       IsReverseV2Supported,
+                                       delegateData.m_Backends,
+                                       isSupported,
+                                       armnn::BackendId(),
+                                       inputInfo0,
+                                       inputInfo1,
+                                       outputInfo);
+
+    return isSupported ? kTfLiteOk : kTfLiteError;
+}
+
+TfLiteStatus VisitReverseV2Operator(DelegateData& delegateData,
+                                    TfLiteOpaqueContext* tfLiteContext,
+                                    TfLiteOpaqueNode* tfLiteNode,
+                                    int nodeIndex,
+                                    int32_t reverseV2OperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Gather input indices and use to get input tensor.
+    auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+            nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The first input contains the data to be reversed
+    const TfLiteOpaqueTensor* tfLiteInputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+    if (IsDynamicTensor(tfLiteInputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The second input contains the axis tensor
+    const TfLiteOpaqueTensor* tfLiteAxisTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
+    if (IsDynamicTensor(tfLiteAxisTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+            nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Get the output tensor
+    const TfLiteOpaqueTensor* tfLiteOutputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (IsDynamicTensor(tfLiteOutputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo0 =
+            GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& inputTensorInfo1 =
+            GetTensorInfoForTfLiteOpaqueTensor(tfLiteAxisTensor);
+    const armnn::TensorInfo& outputTensorInfo =
+            GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+    if (inputTensorInfo0.GetNumDimensions() != outputTensorInfo.GetNumDimensions())
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: input tensor dimension and output tensor dimension differ #%d node #%d: ",
+            reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    for (unsigned i=0; i < inputTensorInfo0.GetNumDimensions(); i++)
+    {
+        if (inputTensorInfo0.GetShape()[i] != outputTensorInfo.GetShape()[i])
+        {
+            TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: input tensor dimension and output tensor differ #%d node #%d: ",
+                reverseV2OperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+    }
+
+    std::string layerName("ReverseV2");
+
+    // Get axis tensor data
+    auto axisTensorNumValues = static_cast<unsigned int>(TfLiteOpaqueTensorDim(tfLiteAxisTensor,0));
+
+    const auto maxDimension = 4;
+
+    if (axisTensorNumValues > maxDimension)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnOpaqueDelegate: The Axis-Input-Tensor of the ReverseV2 operation requires a "
+            "dimension of <= %d but a tensor with a dimension of %d was given.                      "
+            "Operator: #%d node #%d: ",
+            maxDimension, axisTensorNumValues, reverseV2OperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // No network pointer indicates that only support for this operator should be checked
+    if (!delegateData.m_Network)
+    {
+        return ValidateReverseV2Operator(delegateData,
+                                         tfLiteContext,
+                                         inputTensorInfo0,
+                                         inputTensorInfo1,
+                                         outputTensorInfo);
+    }
+
+    armnn::IConnectableLayer* reverseV2Layer = delegateData.m_Network->AddReverseV2Layer(layerName.c_str());
+
+    armnn::IOutputSlot& outputSlot = reverseV2Layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // try to connect the Constant Inputs if there are any
+    if(ProcessInputs(reverseV2Layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    ARMNN_ASSERT(reverseV2Layer != nullptr);
+
+    return Connect(reverseV2Layer, tfLiteContext, tfLiteNode, delegateData);
+}
+
+} // namespace armnnOpaqueDelegate
diff --git a/delegate/opaque/src/armnn_delegate.cpp b/delegate/opaque/src/armnn_delegate.cpp
index f32a6f43b8..510352eae9 100644
--- a/delegate/opaque/src/armnn_delegate.cpp
+++ b/delegate/opaque/src/armnn_delegate.cpp
@@ -30,6 +30,7 @@
 #include "Redefine.hpp"
 #include "Reduce.hpp"
 #include "Resize.hpp"
+#include "ReverseV2.hpp"
 #include "Round.hpp"
 #include "Shape.hpp"
 #include "Slice.hpp"
@@ -1032,6 +1033,12 @@ TfLiteStatus ArmnnSubgraph::VisitNode(DelegateData& delegateData,
                                        tfLiteNode,
                                        nodeIndex,
                                        kTfLiteBuiltinResizeBilinear);
+        case kTfLiteBuiltinReverseV2:
+            return VisitReverseV2Operator(delegateData,
+                                          tfLiteContext,
+                                          tfLiteNode,
+                                          nodeIndex,
+                                          kTfLiteBuiltinReverseV2);
         case kTfLiteBuiltinRsqrt:
             return VisitElementwiseUnaryOperator(delegateData,
                                                  tfLiteContext,
diff --git a/delegate/test/ReverseV2Test.cpp b/delegate/test/ReverseV2Test.cpp
new file mode 100644
index 0000000000..b261474e99
--- /dev/null
+++ b/delegate/test/ReverseV2Test.cpp
@@ -0,0 +1,183 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ReverseV2TestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+    void ReverseV2Float32Test(std::vector<armnn::BackendId>& backends)
+    {
+        // Set input data
+        std::vector<float> inputValues =
+        {
+            1.0f, 2.0f, 3.0f,
+            4.0f, 5.0f, 6.0f,
+            7.0f, 8.0f, 9.0f,
+
+            11.0f, 12.0f, 13.0f,
+            14.0f, 15.0f, 16.0f,
+            17.0f, 18.0f, 19.0f,
+
+            21.0f, 22.0f, 23.0f,
+            24.0f, 25.0f, 26.0f,
+            27.0f, 28.0f, 29.0f
+        };
+
+        // The output data
+        std::vector<float> expectedOutputValues =
+        {
+            3.0f, 2.0f, 1.0f,
+            6.0f, 5.0f, 4.0f,
+            9.0f, 8.0f, 7.0f,
+
+            13.0f, 12.0f, 11.0f,
+            16.0f, 15.0f, 14.0f,
+            19.0f, 18.0f, 17.0f,
+
+            23.0f, 22.0f, 21.0f,
+            26.0f, 25.0f, 24.0f,
+            29.0f, 28.0f, 27.0f
+        };
+
+        // The axis to reverse
+        const std::vector<int32_t> axisValues = {2};
+
+        // Shapes
+        const std::vector<int32_t> inputShape = {3, 3, 3};
+        const std::vector<int32_t> axisShapeDims = {1};
+        const std::vector<int32_t> expectedOutputShape = {3, 3, 3};
+
+        ReverseV2FP32TestImpl(tflite::BuiltinOperator_REVERSE_V2,
+                              backends,
+                              inputValues,
+                              inputShape,
+                              axisValues,
+                              axisShapeDims,
+                              expectedOutputValues,
+                              expectedOutputShape);
+    }
+
+    void ReverseV2NegativeAxisFloat32Test(std::vector<armnn::BackendId>& backends)
+    {
+        // Set input data
+        std::vector<float> inputValues =
+        {
+            1.0f, 2.0f, 3.0f,
+            4.0f, 5.0f, 6.0f,
+            7.0f, 8.0f, 9.0f,
+
+            11.0f, 12.0f, 13.0f,
+            14.0f, 15.0f, 16.0f,
+            17.0f, 18.0f, 19.0f,
+
+            21.0f, 22.0f, 23.0f,
+            24.0f, 25.0f, 26.0f,
+            27.0f, 28.0f, 29.0f
+        };
+
+        // The output data
+        std::vector<float> expectedOutputValues =
+        {
+            7.0f, 8.0f, 9.0f,
+            4.0f, 5.0f, 6.0f,
+            1.0f, 2.0f, 3.0f,
+
+            17.0f, 18.0f, 19.0f,
+            14.0f, 15.0f, 16.0f,
+            11.0f, 12.0f, 13.0f,
+
+            27.0f, 28.0f, 29.0f,
+            24.0f, 25.0f, 26.0f,
+            21.0f, 22.0f, 23.0f
+        };
+
+        // The axis to reverse
+        const std::vector<int32_t> axisValues = {-2};
+
+        // Shapes
+        const std::vector<int32_t> inputShape = {3, 3, 3};
+        const std::vector<int32_t> axisShapeDims = {1};
+        const std::vector<int32_t> expectedOutputShape = {3, 3, 3};
+
+        ReverseV2FP32TestImpl(tflite::BuiltinOperator_REVERSE_V2,
+                              backends,
+                              inputValues,
+                              inputShape,
+                              axisValues,
+                              axisShapeDims,
+                              expectedOutputValues,
+                              expectedOutputShape);
+    }
+
+#if defined(REVERSEV2_GPUACC)
+    TEST_SUITE("ReverseV2Tests_GpuAccTests")
+    {
+
+        TEST_CASE ("ReverseV2_Float32_GpuAcc_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+            ReverseV2Float32Test(backends);
+        }
+
+        TEST_CASE ("ReverseV2_NegativeAxis_Float32_GpuAcc_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+            ReverseV2NegativeAxisFloat32Test(backends);
+        }
+
+    } // TEST_SUITE("ReverseV2Tests_GpuAccTests")
+#endif
+
+
+#if defined(REVERSEV2_CPUACC)
+    TEST_SUITE("ReverseV2Tests_CpuAccTests")
+    {
+
+        TEST_CASE ("ReverseV2_Float32_CpuAcc_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+            ReverseV2Float32Test(backends);
+        }
+
+        TEST_CASE ("ReverseV2_NegativeAxis_Float32_CpuAcc_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+            ReverseV2NegativeAxisFloat32Test(backends);
+        }
+
+    } // TEST_SUITE("ReverseV2Tests_CpuAccTests")
+#endif
+
+
+    TEST_SUITE("ReverseV2Tests_CpuRefTests")
+    {
+
+        TEST_CASE ("ReverseV2_Float32_CpuRef_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+            ReverseV2Float32Test(backends);
+        }
+
+        TEST_CASE ("ReverseV2_NegativeAxis_Float32_CpuRef_Test")
+        {
+            std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+            ReverseV2NegativeAxisFloat32Test(backends);
+        }
+
+    } // TEST_SUITE("ReverseV2Tests_CpuRefTests")
+
+} // namespace armnnDelegate
diff --git a/delegate/test/ReverseV2TestHelper.hpp b/delegate/test/ReverseV2TestHelper.hpp
new file mode 100644
index 0000000000..8c4acd1a7e
--- /dev/null
+++ b/delegate/test/ReverseV2TestHelper.hpp
@@ -0,0 +1,148 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <DelegateTestInterpreter.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/version.h>
+
+#include <schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+    std::vector<char> CreateReverseV2TfLiteModel(tflite::BuiltinOperator operatorCode,
+                                                 tflite::TensorType inputTensorType,
+                                                 const std::vector <int32_t>& inputTensorShape,
+                                                 const std::vector <int32_t>& axisTensorData,
+                                                 const std::vector <int32_t>& axisTensorShape,
+                                                 const std::vector <int32_t>& outputTensorShape)
+    {
+        using namespace tflite;
+        flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+        std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+        buffers.push_back(CreateBuffer(flatBufferBuilder,
+                                       flatBufferBuilder.CreateVector(
+                                               reinterpret_cast<const uint8_t*>(axisTensorData.data()),
+                                               sizeof(int32_t) * axisTensorData.size())));
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+        std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+        tensors[0] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+                                                                          inputTensorShape.size()),
+                                  inputTensorType,
+                                  1,
+                                  flatBufferBuilder.CreateString("input_tensor"));
+
+        tensors[1] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
+                                                                          axisTensorShape.size()),
+                                  TensorType_INT32,
+                                  2,
+                                  flatBufferBuilder.CreateString("axis_input_tensor"));
+
+        tensors[2] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                          outputTensorShape.size()),
+                                  inputTensorType,
+                                  3,
+                                  flatBufferBuilder.CreateString("output_tensor"));
+
+        // Create Operator
+        tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+        flatbuffers::Offset<void> operatorBuiltinOption = 0;
+
+        const std::vector<int> operatorInputs{0, 1};
+        const std::vector<int> operatorOutputs{2};
+        flatbuffers::Offset <Operator> reverseV2Operator =
+                CreateOperator(flatBufferBuilder,
+                               0,
+                               flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+                               flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+                               operatorBuiltinOptionsType,
+                               operatorBuiltinOption);
+
+        const std::vector<int> subgraphInputs{0, 1};
+        const std::vector<int> subgraphOutputs{2};
+        flatbuffers::Offset <SubGraph> subgraph =
+                CreateSubGraph(flatBufferBuilder,
+                               flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+                               flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+                               flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+                               flatBufferBuilder.CreateVector(&reverseV2Operator, 1));
+
+        flatbuffers::Offset <flatbuffers::String> modelDescription =
+                flatBufferBuilder.CreateString("ArmnnDelegate: ReverseV2 Operator Model");
+        flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode);
+
+        flatbuffers::Offset <Model> flatbufferModel =
+                CreateModel(flatBufferBuilder,
+                            TFLITE_SCHEMA_VERSION,
+                            flatBufferBuilder.CreateVector(&opCode, 1),
+                            flatBufferBuilder.CreateVector(&subgraph, 1),
+                            modelDescription,
+                            flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+        flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
+
+        return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+                                 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+    }
+
+    void ReverseV2FP32TestImpl(tflite::BuiltinOperator operatorCode,
+                               std::vector<armnn::BackendId>& backends,
+                               std::vector<float>& inputValues,
+                               std::vector<int32_t> inputShape,
+                               std::vector<int32_t> axisValues,
+                               std::vector<int32_t> axisShapeDims,
+                               std::vector<float>& expectedOutputValues,
+                               std::vector<int32_t> expectedOutputShape)
+    {
+        using namespace delegateTestInterpreter;
+
+        std::vector<char> modelBuffer = CreateReverseV2TfLiteModel(operatorCode,
+                                                                   ::tflite::TensorType_FLOAT32,
+                                                                   inputShape,
+                                                                   axisValues,
+                                                                   axisShapeDims,
+                                                                   expectedOutputShape);
+
+        // Setup interpreter with just TFLite Runtime.
+        auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
+        CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
+        CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+        CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(axisValues, 1) == kTfLiteOk);
+        CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
+        std::vector<float>   tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0);
+        std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);
+
+        // Setup interpreter with Arm NN Delegate applied.
+        auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
+        CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
+        CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+        CHECK(armnnInterpreter.FillInputTensor<int32_t>(axisValues, 1) == kTfLiteOk);
+        CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
+        std::vector<float>   armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
+        std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);
+
+        armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
+        armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
+
+        tfLiteInterpreter.Cleanup();
+        armnnInterpreter.Cleanup();
+    }
+
+} // anonymous namespace
diff --git a/docs/05_03_delegate.dox b/docs/05_03_delegate.dox
index 78bc3ea0b8..632afa0cf0 100644
--- a/docs/05_03_delegate.dox
+++ b/docs/05_03_delegate.dox
@@ -167,6 +167,8 @@ The Arm NN SDK TensorFlow Lite delegate currently supports the following operato
 
 - RESIZE_NEAREST_NEIGHBOR
 
+- REVERSEV2
+
 - RSQRT
 
 - SHAPE