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AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Author(s)
Lin, Ji; Liu, Zhijian; Wang, Hanrui; Han, Song
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
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 learning to efficiently sample the design space and can improve the model compression quality. We achieved state-of-the-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.
Date issued
2018-10
URI
https://hdl.handle.net/1721.1/129576
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer Science and Business Media LLC
Citation
He, Yihui et al. "AMC: AutoML for Model Compression and Acceleration on Mobile Devices." Computer vision -- ECCV 2018 : 15th European Conference, Lecture Notes in Computer Science, 11211, Springer, 2018, 815-832 © 2018 The Author(s)
Version: Author's final manuscript
ISBN
9783030012205

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