IVGCVSW-1898 : Ref backend folder structure

* Reference backend is renamed to backends/reference as per
  https://confluence.arm.com/display/MLENG/Pluggable+backends

Change-Id: I27a13c274eb60995dfb459e3c49c0e2f60bcd32c

1 files changed, 62 insertions, 0 deletions

diff --git a/src/backends/reference/workloads/FullyConnected.cpp b/src/backends/reference/workloads/FullyConnected.cpp
new file mode 100644
index 0000000000..bf5814d2ad
--- /dev/null
+++ b/src/backends/reference/workloads/FullyConnected.cpp
@@ -0,0 +1,62 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "FullyConnected.hpp"
+
+#include <boost/assert.hpp>
+
+namespace armnn
+{
+
+void FullyConnected(const float*      inputData,
+                    float*            outputData,
+                    const TensorInfo& inputTensorInfo,
+                    const TensorInfo& outputTensorInfo,
+                    const float*      weightData,
+                    const float*      biasData,
+                    bool              transposeWeights)
+{
+    unsigned int N = outputTensorInfo.GetShape()[1]; // Outputs Vector Size.
+
+    BOOST_ASSERT(inputTensorInfo.GetNumDimensions() > 1); // Needs some data.
+
+    unsigned int K = 1; // Total number of activations in the input.
+    for (unsigned int i = 1; i < inputTensorInfo.GetNumDimensions(); i++)
+    {
+        K *= inputTensorInfo.GetShape()[i];
+    }
+
+    for (unsigned int n = 0; n < inputTensorInfo.GetShape()[0]; n++)
+    {
+        for (unsigned int channelOutput = 0; channelOutput < N; channelOutput++)
+        {
+            float outval = 0.f;
+
+            for (unsigned int channelInput = 0; channelInput < K; channelInput++)
+            {
+                float weight;
+                if (transposeWeights)
+                {
+                    weight = weightData[channelOutput * K + channelInput];
+                }
+                else
+                {
+                    weight = weightData[channelInput * N + channelOutput];
+                }
+
+                outval += weight * inputData[n * K + channelInput];
+            }
+
+            if (biasData)
+            {
+                outval += biasData[channelOutput];
+            }
+
+            outputData[n * N + channelOutput] = outval;
+        }
+    }
+}
+
+} //namespace armnn