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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2104.10853 |
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| _version_ | 1866910771571589120 |
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| author | Fu, Yonggan Yu, Zhongzhi Zhang, Yongan Jiang, Yifan Li, Chaojian Liang, Yongyuan Jiang, Mingchao Wang, Zhangyang Lin, Yingyan Celine |
| author_facet | Fu, Yonggan Yu, Zhongzhi Zhang, Yongan Jiang, Yifan Li, Chaojian Liang, Yongyuan Jiang, Mingchao Wang, Zhangyang Lin, Yingyan Celine |
| contents | The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices. Therefore, we propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operate at variable bit-widths. Extensive experiments show that the proposed InstantNet consistently outperforms state-of-the-art designs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_10853 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks Fu, Yonggan Yu, Zhongzhi Zhang, Yongan Jiang, Yifan Li, Chaojian Liang, Yongyuan Jiang, Mingchao Wang, Zhangyang Lin, Yingyan Celine Machine Learning The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices. Therefore, we propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operate at variable bit-widths. Extensive experiments show that the proposed InstantNet consistently outperforms state-of-the-art designs. |
| title | InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2104.10853 |