⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一种65nm工艺的混合数字-混合信号计算平台，用于加速基于模型和无模型的群体智能算法。通过混合信号处理与数字逻辑的协同设计，实现了1.1至9.1TOPS/W的能效范围，适用于群体机器人协同任务如模式形成和强化学习。
Artificial swarm intelligence, inspired by biological studies of insects, ants and other organisms, present an emerging computing paradigm, where seemingly simple elements interact with each other to collectively solve challenging problems. In particular, swarm robotics, where multiple robots co-ordinate in real-time to solve diverse problems such as pattern-formation, cooperative reinforcement learning (RL), path-planning etc. [1], find extensive uses in exploration, reconnaissance and disaster relief. This is partly motivated by the robustness of swarm dynamics to failures and malfunctions of individual robots. Successful hardware demonstrations of neuro-inspired algorithms on edgedevices [2-6] is now leading to the emergence of intelligence and control in swarms as the next frontier. Although certain swarm algorithms rely on real-time learning (e.g., cooperative RL) representing a model-free approach, many
Ningyuan Cao, Muya Chang, Arijit Raychowdhury
Georgia Institute of Technology, Atlanta, GA