资源受限的信息物理系统的高效和可扩展计算
日期：2021/12/03 - 2021/12/03
学术讲座： 资源受限的信息物理系统的高效和可扩展计算
主讲人：An Zou, Assistant Professor, UM-SJTU Joint Institute, Shanghai Jiao Tong University
时间：2021年12月3日（周五）上午10:00-11:30
地点：上海交通大学密西根学院龙宾楼3楼中集报告厅
飞书：https://vc.feishu.cn/j/235090819
讲座摘要
With the evolution of computing and communication technology, cyber-physical systems such as self-driving cars, unmanned aerial vehicles, and mobile cognitive robots are achieving increasing levels of multifunctionality and miniaturization, enabling them to execute versatile tasks in a resource-constrained environment. Therefore, the computing systems that power these cyber-physical systems have to achieve high efficiency and scalability. First of all, given a fixed amount of onboard energy, these computing systems should not only be power-efficient but also exhibit sufficiently high performance to gracefully handle complex algorithms for learning-based perception and AI-driven decision-making. Meanwhile, scalability requires that the current computing system and its components can be extended both horizontally, with more resources, and vertically, with emerging advanced technology. To achieve efficient and scalable computing systems, his research broadly investigates a set of techniques and solutions via a bottom-up layered approach. This layered approach leverages the characteristics of each system layer (e.g., the circuit, architecture, and operating system layers) and their interactions to discover and explore the optimal system tradeoffs among performance, efficiency, and scalability. The layer-spanning approach successfully improves both power and performance efficiencies of the computing systems, which further strengthens whole cyber-physical systems.
主讲人简介
An Zou is an assistant professor at the UM-SJTU Joint Institute Shanghai Jiao Tong University. He received his B.S., M.S degrees from Harbin Institute of Technology in 2013 and 2015, and Ph.D. in Electrical Engineering from Washington University in St. Louis in 2021. He interned at Facebook Reality Labs working on AR/VR processor designs. His research focuses on computer architecture and embedded systems. He broadly investigated a set of techniques and solutions via a bottom-up layered approach to improve computing power and performance efficiency. His work has been extensively published and recognized at top-tier conferences and journals. His work received the best paper nominations at DAC (2017) and MLCAD (2020). He is the recipient of the A. Richard Newton Young Student Fellowship and a Chinese National Scholarship. He also services as the TPC member and reviewer for several conferences and journals.