A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2106.06577v1
• Date: Fri, 11 Jun 2021 18:56:44 GMT
• Title: A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
• Authors: Yonggan Fu, Yongan Zhang, Chaojian Li, Zhongzhi Yu, Yingyan Lin
• Abstract summary: 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. Our experiments consistently validate the superiority of our A3C-S over state-of-the-art techniques.
• Score: 16.96187187108041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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