  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.
