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arXiv · 1807.10598

Spatial Correlation and Value Prediction in Convolutional Neural Networks

Gil Shomron·Uri Weiser
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Abstract

Convolutional neural networks (CNNs) are a widely used form of deep neural networks, introducing state-of-the-art results for different problems such as image classification, computer vision tasks, and speech recognition. However, CNNs are compute intensive, requiring billions of multiply-accumulate (MAC) operations per input. To reduce the number of MACs in CNNs, we propose a value prediction method that exploits the spatial correlation of zero-valued activations within the CNN output feature maps, thereby saving convolution operations. Our method reduces the number of MAC operations by 30.4%, averaged on three modern CNNs for ImageNet, with top-1 accuracy degradation of 1.7%, and top-5 accuracy degradation of 1.1%.

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Gil Shomron, Uri Weiser. 2018-07-21. Spatial Correlation and Value Prediction in Convolutional Neural Networks. https://doi.org/10.1109/lca.2018.2890236

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