AMC: AutoML for Model Compression and Acceleration on Mobile Devices
This work addresses the challenge of efficiently deploying models on resource-constrained mobile devices, offering an automated solution that improves over manual, sub-optimal compression techniques.
The paper tackles the problem of compressing neural network models for mobile deployment by proposing AMC, an AutoML approach using reinforcement learning, which achieves 2.7% better accuracy than handcrafted methods under 4x FLOPs reduction for VGG-16 and speeds up MobileNet inference by 1.81x on Android with minimal accuracy loss.
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 4x 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 1.81x speedup of measured inference latency on an Android phone and 1.43x speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.