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author | David Beck <david.beck@arm.com> | 2018-09-19 12:03:20 +0100 |
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committer | Matthew Bentham <matthew.bentham@arm.com> | 2018-10-10 16:16:56 +0100 |
commit | 10b4dfd8e9ccd7a03df7bb053ee1c644cb37f8ab (patch) | |
tree | 1ac5b4f415531e2ef759439ab8e113f177bea7c5 /src/backends/RefWorkloads/FullyConnected.cpp | |
parent | a3f165624b2cdfbced674af5a6e11856b1e746d9 (diff) | |
download | armnn-10b4dfd8e9ccd7a03df7bb053ee1c644cb37f8ab.tar.gz |
IVGCVSW-1897 : build infrastructure for the src/backends folder
Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb
Diffstat (limited to 'src/backends/RefWorkloads/FullyConnected.cpp')
-rw-r--r-- | src/backends/RefWorkloads/FullyConnected.cpp | 62 |
1 files changed, 62 insertions, 0 deletions
diff --git a/src/backends/RefWorkloads/FullyConnected.cpp b/src/backends/RefWorkloads/FullyConnected.cpp new file mode 100644 index 0000000000..bf5814d2ad --- /dev/null +++ b/src/backends/RefWorkloads/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 |