A 55nm Time-Domain Mixed-Signal Neuromorphic Accelerator with Stochastic Synapses and Embedded Reinforcement Learning for Autonomous Micro-Robots

📋 论文概要

该论文提出了一种55nm工艺的时间域混合信号神经形态加速器，集成了随机突触和嵌入式强化学习，旨在实现智能体的连续学习能力。通过混合信号电路和随机突触机制，解决了传统数字加速器在强化学习任务中效率低下的问题。

💡 主要创新点

• 采用时间域混合信号处理架构，结合模拟计算的高能效和数字电路的灵活性。
• 引入随机突触实现概率性权重更新，支持嵌入式强化学习算法。
• 在55nm工艺下实现完整的片上学习加速器，无需外部处理器即可完成环境交互和累积奖励最大化。

🏷 关键词

📄 原文摘要

networks (DNNs) and convolutional neural networks (CNNs) with most hardware demonstrations geared towards inference in vision-based platforms [1-5], we recognize that true autonomy in intelligent agents will only emerge when such bio-mimetic systems can perform continuous learning through interactions with the environment. Reinforcement learning (RL) presents one such computational paradigm inspired by behaviorist psychology, where autonomous agents take actions in an environment to maximize a notion of cumulative reward. This concept is deeply rooted in the human brain where dopamine mediated neurotransmitters (in the cortex, striatum and thalamus of the brain) have been shown to encourage reward-motivated behavior in all our social interactions (Fig. 7.4.1). In this paper, we present a 690μW (VCC=1.2V) neuromorphic accelerator

👥 作者与机构

Anvesha Amravati, Saad Bin Nasir, Sivaram Thangadurai, Insik Yoon, Arijit Raychowdhury

Georgia Institute of Technology, Atlanta, GA Even as rapid advances are being made in the areas of deep neural