A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
TimeTuesday, December 7th2:35pm - 3:00pm PST
Event Type
Research Manuscript
Virtual Programs
Presented In-Person
AI/ML System Design
DescriptionDriven by the explosive interest in applying deep reinforcement learning (DRL) agents for 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 the odds with the 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 the test scores and hardware efficiency. Extensive experiments consistently validate the superiority of our A3C-S over state-of-the-art techniques.