Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain

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Abstract
Various network compression methods, such as pruning and quantization, have been proposed to synergistically reduce resource requirements. However, existing joint compression works are based on black-box optimization and do not interpret the interaction mechanism between these two compression techniques, leading to a slow and unstable convergence of compression strategy. To address this issue, we present Markov-PQ, the first interpretable pruning-quantization co-compression framework using a Markov Chain. In Markov-PQ, the joint strategy search is modeled as a Markov Chain and decoupled with Bayes Rule into pruning and quantization strategy searching. Specifically, the quantization state accounts for the co-compression state from the last time and is updated by a learnable transition probability matrix. To ensure differentiability, we design a forward-hard and backward-soft quantization. The pruning state is influenced not only by the last co-compression state but also by the concurrent quantization state. In addition, to perceive the current layer-wise bit sensitivity and alleviate the long-tail problem, a complexity-aware regularizer is devised to re-evaluate the filter importance. Extensive experiments demonstrate the superiority of Markov-PQ. For example, with an accuracy loss of only 0.33%, we can achieve a 56.12× acceleration for ResNet-18 on ImageNet2012.
Funding Information
  • National Natural Science Foundation of China (62121001, U22B2014)
  • Youth Talent Promotion Project of China Association for Science and Technology (2020QNRC001)
  • Fundamental Research Funds for the Central Universities (QTZX23048)
  • Innovation Fund of Xidian University (20109235456)