Energy-Efficient Deep Reinforcement Learning Accelerator Designs for Mobile Autonomous Systems

Abstract

Deep reinforcement learning (DRL) is widely used for autonomous systems including autonomous driving, robots, and drones. DRL training is essential for human-level control and adaptation to rapidly changing environments in mobile autonomous systems. However, acceleration of DRL training has three challenges: 1) large memory access, 2) various data patterns, 3) complex data dependency due to utilization of multiple DNNs. Two CMOS DRL accelerators have been proposed to support high speed, high energy-efficiency DRL training in mobile autonomous systems. One accelerator handles different data patterns with transposable PE architecture and reduces large feature map memory access with top-3 experience compression. The other accelerator supports group-sparse training for weight compression and integrates the on-line DRL task scheduler to support multi-DNNs operations.