Mixed-Precision Quantization~(MQ) can achieve a competitive
accuracy-complexity trade-off for models. Conventional training-based search
methods require time-consuming candidate training to search optimized per-layer
bit-width configurations in MQ. Recently, some training-free approaches have
presented various MQ proxies and significantly improve search efficiency.
However, the correlation between these proxies and quantization accuracy is
poorly understood. To address the gap, we first build the MQ-Bench-101, which
involves different bit configurations and quantization results. Then, we
observe that the existing training-free proxies perform weak correlations on
the MQ-Bench-101. To efficiently seek superior proxies, we develop an automatic
search of proxies framework for MQ via evolving algorithms. In particular, we
devise an elaborate search space involving the existing proxies and perform an
evolution search to discover the best correlated MQ proxy. We proposed a
diversity-prompting selection strategy and compatibility screening protocol to
avoid premature convergence and improve search efficiency. In this way, our
Evolving proxies for Mixed-precision Quantization~(EMQ) framework allows the
auto-generation of proxies without heavy tuning and expert knowledge. Extensive
experiments on ImageNet with various ResNet and MobileNet families demonstrate
that our EMQ obtains superior performance than state-of-the-art mixed-precision
methods at a significantly reduced cost. The code will be released.

Mixed-Precision Quantization (MQ) 模型的精度与复杂度之间实现了有竞争力的权衡。我们提出了一种自动搜索代理的框架 (EMQ)，通过进化算法发现与 MQ 最相关的代理，实现了代理的自动生成，并在 ImageNet 上通过广泛的实验证明了其卓越性能。