Model compression is a critical 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 heuristics and rule-based policies that 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 leverage reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. Under 4× FLOPs reduction, we achieved 2.7% better accuracy than the handcrafted model compression policy for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81× speedup of measured inference latency on an Android phone and 1.43× speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 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% ? %
Automatic Model Compression (AMC) REF explores the pruning strategy by automatically learning the compression ratio of each layer through reinforcement learning (RL).
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
ECCV
Computer Science