  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.
