  Since model quantization helps to reduce the model size and computation
latency, it has been successfully applied in many applications of mobile
phones, embedded devices and smart chips. The mixed-precision quantization
model can match different quantization bit-precisions according to the
sensitivity of different layers to achieve great performance. However, it is a
difficult problem to quickly determine the quantization bit-precision of each
layer in deep neural networks according to some constraints (e.g., hardware
resources, energy consumption, model size and computation latency). To address
this issue, we propose a novel sequential single path search (SSPS) method for
mixed-precision quantization,in which the given constraints are introduced into
its loss function to guide searching process. A single path search cell is used
to combine a fully differentiable supernet, which can be optimized by
gradient-based algorithms. Moreover, we sequentially determine the candidate
precisions according to the selection certainties to exponentially reduce the
search space and speed up the convergence of searching process. Experiments
show that our method can efficiently search the mixed-precision models for
different architectures (e.g., ResNet-20, 18, 34, 50 and MobileNet-V2) and
datasets (e.g., CIFAR-10, ImageNet and COCO) under given constraints, and our
experimental results verify that SSPS significantly outperforms their uniform
counterparts.
