XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Abstract

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-WeightNetworks, the filters are approximated with binary values resulting in 32× memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58× faster convolutional operations and 32× memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.

DOI: 10.1007/978-3-319-46493-0_32
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@inproceedings{Rastegari2016XNORNetIC, title={XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks}, author={Mohammad Rastegari and Vicente Ordonez and Joseph Redmon and Ali Farhadi}, booktitle={ECCV}, year={2016} }