ADC: Automated Deep Compression and Acceleration with Reinforcement Learning
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Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose Automated Deep Compression (ADC) that leverages reinforcement learning in order to efficiently sample the design space and greatly improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4x FLOPs reduction, we achieved 2.7 accuracy than hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved a 2x reduction in FLOPs, and a speedup of 1.49x on Titan Xp and 1.65x on an Android phone (Samsung Galaxy S7), with negligible loss of accuracy.
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