  Network quantization is an effective method for the deployment of neural
networks on memory and energy constrained mobile devices. In this paper, we
propose a Dynamic Network Quantization (DNQ) framework which is composed of two
modules: a bit-width controller and a quantizer. Unlike most existing
quantization methods that use a universal quantization bit-width for the whole
network, we utilize policy gradient to train an agent to learn the bit-width of
each layer by the bit-width controller. This controller can make a trade-off
between accuracy and compression ratio. Given the quantization bit-width
sequence, the quantizer adopts the quantization distance as the criterion of
the weights importance during quantization. We extensively validate the
proposed approach on various main-stream neural networks and obtain impressive
results.
