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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/1802.03494 |
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| _version_ | 1866917629684350976 |
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| author | He, Yihui Lin, Ji Liu, Zhijian Wang, Hanrui Li, Li-Jia Han, Song |
| author_facet | He, Yihui Lin, Ji Liu, Zhijian Wang, Hanrui Li, Li-Jia Han, Song |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1802_03494 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | AMC: AutoML for Model Compression and Acceleration on Mobile Devices He, Yihui Lin, Ji Liu, Zhijian Wang, Hanrui Li, Li-Jia Han, Song Computer Vision and Pattern Recognition 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. |
| title | AMC: AutoML for Model Compression and Acceleration on Mobile Devices |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1802.03494 |