1 files changed, 54 insertions, 0 deletions
diff --git a/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp
new file mode 100644
index 0000000000..739390d5a1
--- /dev/null
+++ b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp
@@ -0,0 +1,54 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include "NeonNormalizationFloat32Workload.hpp"
+#include "backends/NeonLayerSupport.hpp"
+#include "backends/ArmComputeUtils.hpp"
+
+namespace armnn
+{
+
+NeonNormalizationFloat32Workload::NeonNormalizationFloat32Workload(const NormalizationQueueDescriptor& descriptor,
+                                                                   const WorkloadInfo& info)
+    : Float32Workload<NormalizationQueueDescriptor>(descriptor, info)
+{
+    m_Data.ValidateInputsOutputs("NeonNormalizationFloat32Workload", 1, 1);
+    std::string reasonIfUnsupported;
+    if (!IsNeonNormalizationDescParamsSupported(&reasonIfUnsupported, m_Data.m_Parameters))
+    {
+        throw UnimplementedException(reasonIfUnsupported);
+    }
+
+    // input and output tensors have to have the same dimensionality
+    if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
+        || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
+        || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
+        || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
+    {
+        throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
+    }
+
+    arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+    const arm_compute::NormType normType =
+        ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
+    arm_compute::NormalizationLayerInfo normalizationInfo(normType,
+                                                          m_Data.m_Parameters.m_NormSize,
+                                                          m_Data.m_Parameters.m_Alpha,
+                                                          m_Data.m_Parameters.m_Beta,
+                                                          m_Data.m_Parameters.m_K,
+                                                          false);
+
+    m_NormalizationLayer.configure(&input, &output, normalizationInfo);
+}
+
+void NeonNormalizationFloat32Workload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuAcc, "NeonNormalizationFloat32Workload_Execute");
+    m_NormalizationLayer.run();
+}
+
+} //namespace armnn