ADC: Automated Deep Compression and Acceleration with Reinforcement Learning
share
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.
READ FULL TEXT
