2017/02/27 by Graham, Benjamin
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)
paper · doi:10.48550/arxiv.1702.08231
Artificial neural networks can be trained with relatively low-precision floating-point and fixed-point arithmetic, using between one and 16 bits. Previous works have focused on relatively wide-but-shallow, feed-forward networks. We introduce a quantization scheme that is compatible with training very deep neural networks. Quantizing the network activations in the middle of each batch-normalization module can greatly reduce the amount of memory and computational power needed, with little loss in accuracy.