AutoMC: Automated Model Compression Based on Domain Knowledge and Progressive Search

Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments. Despite their great success, the selection of suitable compression methods and design of details of the compression scheme are difficult, requiring lots of domain knowledge as support, which is not friendly to non-expert users. To make more users easily access to the model compression scheme that best meet their needs, in this paper, we propose AutoMC, an effective and efficient automatic tool for model compression. In order to improve the search efficiency and quality, in AutoMC, we build the domain knowledge on model compression to deeply understand the characteristics and advantages of each compression method under different settings. This method can provide AutoMC with the more reasonable guidance and thus reduce useless evaluation. In addition, we present a progressive search strategy to efficiently explore pareto optimal compression scheme according to the learned prior knowledge combined with the historical evaluation information. This strategy can help AutoMC selectively and gradually explore more valuable search space, and thus reduce the search difficulty and improve the search efficiency. Extensive experimental results show that AutoMC can provide users with better compression schemes within short time compared to the existing compression methods and AutoML algorithms, which demonstrates the effectiveness and significance of our proposed algorithm.