1 files changed, 77 insertions, 0 deletions
diff --git a/libs/kernel/acl/src/neon/Softmax.cpp b/libs/kernel/acl/src/neon/Softmax.cpp
new file mode 100644
index 000000000..79d614418
--- /dev/null
+++ b/libs/kernel/acl/src/neon/Softmax.cpp
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <OperationsUtils.h>
+#include <NeuralNetworks.h>
+
+#include <arm_compute/core/TensorShape.h>
+#include <arm_compute/core/TensorInfo.h>
+#include "../IO_accessor.h"
+#include "../shape.h"
+#include "../util.h"
+#include "../NEUniqueTensor.h"
+
+namespace nnfw {
+namespace kernel {
+namespace acl {
+namespace neon {
+
+bool softmaxFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
+                    const float beta,
+                    float* outputData, const nnfw::rt::Shape& outputShape)
+{
+  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
+  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
+
+  NEUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
+  NEUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
+
+  auto softmax_f = std::make_shared<arm_compute::NESoftmaxLayer>();
+  softmax_f->configure(input.ptr(), output.ptr(), beta);
+
+  input.allocate();
+  output.allocate();
+
+  if (inputShape.dimensions.size() == 4)
+  {
+    TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
+
+    softmax_f->run();
+
+    TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
+  }
+  else if (inputShape.dimensions.size() == 2)
+  {
+    // Softmax comes with 1xN matrix and this is translated to N vector in arm_compute::TensorShape
+    TensorAccess<VectorInputAccessor>(input.ref(), inputData, inputShape);
+
+    softmax_f->run();
+
+    TensorAccess<VectorOutputAccessor>(output.ref(), outputData, outputShape);
+  }
+  else
+  {
+    assert("undefined dimension of input" && 0);
+    return false;
+  }
+
+  return true;
+}
+
+} // namespace neon
+} // namespace acl
+} // namespace kernel
+} // namespace nnfw