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Erschienen in:
CBAM: Convolutional Block Attention Module Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

verfasst von : Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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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\(\times \) 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\(\times \) on the GPU (Titan Xp) and 1.95\(\times \) on an Android phone (Google Pixel 1), with negligible loss of accuracy.

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Metadaten
Titel
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
verfasst von
Yihui He
Ji Lin
Zhijian Liu
Hanrui Wang
Li-Jia Li
Song Han
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01233-5

Electronic ISBN: 978-3-030-01234-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01234-2_48