Edge AI represents an increasingly important computing paradigm where AI algorithms are deployed on the network edge devices. Edge AI can enable a broad class of applications which interact with the physical world in an intelligent, real-time, and privacy-preserving manner. In this talk, I will briefly discuss our recent work on Edge AI for autonomous driving and smart health systems.
 
Autonomous driving will revolutionize the future transportation systems. However, recent pilot commercial deployments have caused widespread concerns about the safety of emerging autonomous driving systems. I will discuss our recent work on leveraging intelligent roadside infrastructure to improve the safety of autonomous driving. First, we have deployed the world’s largest open smart lamppost testbed on CUHK campus which offers various real-time services such as target detection and dynamic route planning for vehicles. Second, we propose a novel real-time deep learning task framework, which integrate model architecture optimization and real-time scheduling to enable oncurrent execution of multiple deep learning tasks. Third, I will present two new systems for real-time 3D perception fusion between vehicle and infrastructure with centimeter accuracy.
 
Lastly, I will briefly discuss our recent work on tracking challenges of Federated Learning for health systems, including privacy, long-tail data distribution, heterogeneous sensor modality, and small (labelled) data. In collaboration with CUHK medical school and major local public hospitals, our technologies are being deployed and validated through a large-scale clinical trial.

邢国良现任香港中文大学信息工程系教授，IEEE Fellow, 2006年获美国圣路易斯华盛顿大学博士学位，并曾在美国密歇根州立大学任终身教授。邢教授团队专注嵌入式人工智能、自动驾驶、智能健康、物联网等方向的系统研究，发表150 多篇论文，总的引用数超过10,000 次。邢教授于2010 年获得美国自然科学基金会青年科学家事业奖（CAREER）, 2014年获美国密歇根州立大学Withrow杰出教授奖。邢教授的工作在包括MobiCom、IPSN、ICNP、IoTDI等多个国际顶级会议上获得最佳论文奖和最佳论文候选,开发的数项智能系统技术已被工业界产业化。