  The huge size of deep networks hinders their use in small computing devices.
In this paper, we consider compressing the network by weight quantization. We
extend a recently proposed loss-aware weight binarization scheme to
ternarization, with possibly different scaling parameters for the positive and
negative weights, and m-bit (where m > 2) quantization. Experiments on
feedforward and recurrent neural networks show that the proposed scheme
outperforms state-of-the-art weight quantization algorithms, and is as accurate
(or even more accurate) than the full-precision network.
