用于节能边缘机器人的电路和系统技术
Circuit and System Technologies for Energy-Efficient Edge Robotics
摘要：
当我们迈向泛在智能时代时，我们注意到人工智能和智能正逐步从云端走向边缘。Edge-AI的成功取决于创新的电路和硬件，这些电路和硬件可以在资源受限的边缘自治系统中实现推理和有限学习。本文介绍了一系列超低功耗加速器和系统设计，使边缘机器人平台的智能，包括强化学习神经形态控制，群体智能，同时映射和定位。我们重点关注混合信号电路、神经启发计算系统、基准测试和软件基础设施以及算法-硬件协同设计的影响，以实现下一代智能和自主系统的最节能的Edge-AI ASIC。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
