论文标题
节能边缘机器人技术的电路和系统技术
Circuit and System Technologies for Energy-Efficient Edge Robotics
论文作者
论文摘要
当我们朝着无处不在的情报时代迈进时，我们注意到AI和情报正在逐渐从云到边缘。 Edge-ai的成功是在创新的电路和硬件上枢纽的，这些电路和硬件可以在资源受限的边缘自主系统中实现推理和有限的学习。本文介绍了一系列超低功率加速器和系统设计，以实现边缘机器人平台中的智能，包括增强学习神经形态控制，群体智能以及同时的映射和本地化。我们强调了混合信号电路，神经启发的计算系统，基准测试和软件基础架构以及算法 - 硬件的共同设计，以实现下一代智能和自动级系统的最节能的边缘ASIC。
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.
