“XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”, Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi2016-03-16 (CNN, reduced-precision NNs)⁠:

[code] We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks & XNOR-Networks. In Binary-Weight-Networks, 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.