OmniDRL An Energy-Efficient Deep Reinforcement Learning Processor With Dual-Mode

Abstract

In this article, we present an energy-efficient deep reinforcement learning (DRL) processor, OmniDRL, for DRL training on edge devices. Recently, the need for DRL train- ing is growing due to the DRL’s distinct characteristics that can be adapted to each user. However, a massive amount of external and internal memory access limits the implementation of DRL training on resource-constrained platforms. OmniDRL proposes four key features that can reduce external memory access by compressing as much d