Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

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 neuro-morphic 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.