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.
A loss-aware weight quantization algorithm that directly considers its effect on the loss is proposed. Proposes a method of compressing network by means of weight ternarization.  The paper proposes a new method to train DNNs with quantized weights, by including the quantization as a constraint in a proximal quasi-Newton algorithm, which simultaneously learns a scaling for the quantized values. The paper extends the loss-aware weight binarization scheme to terarization and arbitrary m-bit quantization and demonstrate its promising performance.