A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2021
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| _version_ | 1866913634661171200 |
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| author | Fu, Yonggan Zhang, Yongan Li, Chaojian Yu, Zhongzhi Lin, Yingyan Celine |
| author_facet | Fu, Yonggan Zhang, Yongan Li, Chaojian Yu, Zhongzhi Lin, Yingyan Celine |
| contents | Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing demand to deploy DRL agents to empower daily-life intelligent devices, while the prohibitive complexity of DRL stands at odds with limited on-device resources. In this work, we propose an Automated Agent Accelerator Co-Search (A3C-S) framework, which to our best knowledge is the first to automatically co-search the optimally matched DRL agents and accelerators that maximize both test scores and hardware efficiency. Extensive experiments consistently validate the superiority of our A3C-S over state-of-the-art techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_06577 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning Fu, Yonggan Zhang, Yongan Li, Chaojian Yu, Zhongzhi Lin, Yingyan Celine Machine Learning Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing demand to deploy DRL agents to empower daily-life intelligent devices, while the prohibitive complexity of DRL stands at odds with limited on-device resources. In this work, we propose an Automated Agent Accelerator Co-Search (A3C-S) framework, which to our best knowledge is the first to automatically co-search the optimally matched DRL agents and accelerators that maximize both test scores and hardware efficiency. Extensive experiments consistently validate the superiority of our A3C-S over state-of-the-art techniques. |
| title | A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2106.06577 |