14.1 A 65nm 1.1-to-9.1TOPS/W Hybrid-Digital-Mixed-Signal Computing Platform for Accelerating Model-Based and Model-Free Swarm Robotics

• Main Authors: Cao, Ningyuan, Chang, Muya, Raychowdhury, Arijit
• Format: Conference Proceeding
• Language: eng ; jpn
• Subjects: Computational modeling; Heuristic algorithms; Integrated circuit modeling; Predator prey systems; Robots; Semiconductor device measurement; Swarm robotics
• ISSN: 2376-8606
• DOI: 10.1109/ISSCC.2019.8662311

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 edge-devices [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 powerful algorithms that have been developed over the past two decades (e.g., pattern formation) rely on a mathematical structure and represent a more traditional model-based approach. The next generation of swarm hardware needs to support both of these approaches. In this paper, we identify the commonalities and shared compute primitives across a variety of model-based and model-free swarm algorithms and present a unified, fully-programmable, energy-efficient and scalable platform capable of real-time swarm intelligence.