Qinru Qiu

Energy efficiency has been one of the critical driving forces behind the major advancements in semiconductor technology over recent decades. Reduced energy consumption in IT devices not only alleviates environmental impact and extends battery life, but also enhances system reliability and life span. Throughout the evolution of semiconductor technology, new devices, circuits and architectures have been proposed to address energy concerns. Despite these efforts, electronic computers still lag far behind biological systems in terms of their energy efficiency. With only 20 Watts of power consumption, the human brain has a cognitive capability that surpasses the most advanced high-performance computers. In this talk, I will briefly review the history of VLSI technology, highlighting those energy reduction techniques, and discuss some distinctive attributes of biological neural systems that may contribute to their superior energy efficiency. Additionally, I will touch upon recent research advances in neuromorphic computing, which mimic the biological neural system to achieve energy-efficient machine intelligence.

Biography:

Dr. Qinru Qiu received her PhD in Electrical Engineering from the University of Southern California in 2001. She is currently a professor in the Department of Electrical Engineering and Computer Science at Syracuse University. Dr. Qiu is a recipient of the NSF CAREER award in 2009, IEEE Region 1 Technological Innovation award in 2020, and ACM Distinguished Member in 2022. Dr. Qiu’s research interests include neuromorphic computing, machine learning, and energy efficient computing. She serves as an associate editor for IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Circuit and Systems Magazine, IEEE Transactions on Cognitive and Developmental Systems, and Frontier on Neuroscience on Neuromorphic Engineering. She has also served as a technical program committee member at many conferences including DAC, ICCAD, ISLPED, DATE, etc. She is the director of the NSF I/UCRC (Industry University Collaborative Research Center) ASIC (Alternative Sustainable and Intelligent Computing) Center Syracuse Site. Dr. Qiu has been elevated to a Fellow of IEEE for her contributions to the modeling and optimization of energy-efficient computing systems.