AMC: AutoML for Model Compression and Acceleration on Mobile Devices [chapter]

Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han
2018 Lecture Notes in Computer Science  
Model compression is an effective technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted features and require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverages reinforcement
more » ... ning to efficiently sample the design space and can improve the model compression quality. We achieved state-ofthe-art model compression results in a fully automated way without any human efforts. Under 4× FLOPs reduction, we achieved 2.7% better accuracy than the hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet-V1 and achieved a speedup of 1.53× on the GPU (Titan Xp) and 1.95× on an Android phone (Google Pixel 1), with negligible loss of accuracy. Reward= -Error*log(FLOP) Agent: DDPG Action: Compress with Sparsity ratio at (e.g. 50%) Embedding st=[N,C,H,W,i...] Environment: Channel Pruning Layer t-1 Layer t Layer t+1 Critic Actor Embedding Original NN Model Compression by Human: Labor Consuming, Sub-optimal Model Compression by AI: Automated, Higher Compression Rate, Faster Compressed NN AMC Engine Original NN Compressed NN 30% 50% ? %
doi:10.1007/978-3-030-01234-2_48 fatcat:2rmjdaogf5ap7fg2n3mf5jpnbi