Loss-aware Weight Quantization of Deep Networks

Lu Hou; James T. Kwok

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arXiv · 1802.08635

Loss-aware Weight Quantization of Deep Networks

Lu Hou·James T. Kwok

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Abstract

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.

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Lu Hou, James T. Kwok. 2018-02-23. Loss-aware Weight Quantization of Deep Networks. https://arxiv.org/abs/1802.08635

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