エネルギー効率の高いエッジロボティクス向け回路・システム技術
Circuit and System Technologies for Energy-Efficient Edge Robotics
リソース制約のあるエッジロボット上で強化学習ニューロモルフィック制御や群知能、SLAMを実現する超低消費電力アクセラレータとシステム設計を紹介し、アルゴリズム・ハードウェア協調設計の重要性を示した論文。
著者: Zishen Wan, Ashwin Sanjay Lele, Arijit Raychowdhury
分類: cs.AR, cs.RO
原文アブストラクト
As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixed-signal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.
