  To accelerate and compress deep neural networks (DNNs), many network
quantization algorithms have been proposed. Although the quantization strategy
of any algorithm from the state-of-the-arts may outperform others in some
network architectures, it is hard to prove the strategy is always better than
others, and even cannot judge that the strategy is always the best choice for
all layers in a network. In other words, existing quantization algorithms are
suboptimal as they ignore the different characteristics of different layers and
quantize all layers by a uniform quantization strategy. To solve the issue, in
this paper, we propose a differentiable quantization strategy search (DQSS) to
assign optimal quantization strategy for individual layer by taking advantages
of the benefits of different quantization algorithms. Specifically, we
formulate DQSS as a differentiable neural architecture search problem and adopt
an efficient convolution to efficiently explore the mixed quantization
strategies from a global perspective by gradient-based optimization. We conduct
DQSS for post-training quantization to enable their performance to be
comparable with that in full precision models. We also employ DQSS in
quantization-aware training for further validating the effectiveness of DQSS.
To circumvent the expensive optimization cost when employing DQSS in
quantization-aware training, we update the hyper-parameters and the network
parameters in a single forward-backward pass. Besides, we adjust the
optimization process to avoid the potential under-fitting problem.
Comprehensive experiments on high level computer vision task, i.e., image
classification, and low level computer vision task, i.e., image
super-resolution, with various network architectures show that DQSS could
outperform the state-of-the-arts.
