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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
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XNOR-Nets as discussed by the authors approximate convolutions using primarily binary operations, which results in 58x faster convolutional operations and 32x memory savings, and outperforms BinaryConnect and BinaryNets by large margins on ImageNet.Abstract:
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x 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 58x faster convolutional operations and 32x 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.read more
Citations
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Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
Ritchie Zhao,Weinan Song,Wentao Zhang,Tianwei Xing,Jeng-Hau Lin,Mani Srivastava,Rajesh Gupta,Zhiru Zhang +7 more
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Eriko Nurvitadhi,Ganesh Venkatesh,Jaewoong Sim,Debbie Marr,Randy Renfu Huang,Jason Ong Gee Hock,Yeong Tat Liew,Krishnan Srivatsan,Duncan J. M. Moss,Suchit Subhaschandra,Guy Boudoukh +10 more
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Scaling for edge inference of deep neural networks
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Binary Neural Networks: A Survey
TL;DR: A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.
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