A 55-nm 1004V 125-pJ per MAC Time-Domain Mixed-Signal Neuromorphic Accelerator W

Abstract

Reinforcement learning (RL) is a bio-mimetic learn- ing approach, where agents can learn about an environment by performing specific tasks without any human supervision. RL is inspired by behavioral psychology, where agents take actions to maximize a cumulative reward. In this paper, we present an RL neuromorphic accelerator capable of performing obstacle avoidance in a mobile robot at the edge of the cloud. We propose an energy-efficient time-domain mixed-signal (TD-MS) computa- tional framework.