Deming Chen (University of Illinois)
Dr. Deming Chen obtained his BS in computer science from University of Pittsburgh, Pennsylvania in 1995, and his MS and PhD in computer science from University of California at Los Angeles in 2001 and 2005 respectively. He joined the ECE department of University of Illinois at Urbana-Champaign in 2005 and has been a full professor in the same department since 2015. His current research interests include system-level and high-level synthesis, machine learning, GPU and reconfigurable computing, and hardware security. He has given more than 110 invited talks sharing these research results worldwide. He obtained the Arnold O. Beckman Research Award from UIUC in 2007, the NSF CAREER Award in 2008, and eight Best Paper Awards. He also received the ACM SIGDA Outstanding New Faculty Award in 2010, and IBM Faculty Award in 2014 and 2015. In 2017, he led a team to win the first place of DAC International Hardware Design Contest in the IoT domain. He is included in the List of Teachers Ranked as Excellent in 2008 and 2017. He is the Donald Biggar Willett Faculty Scholar of College of Engineering, an IEEE Fellow, an ACM Distinguished Speaker, and the Editor-in-Chief of ACM Transactions on Reconfigurable Technology and Systems (TRETS). He is also involved with several startup companies, including co-founding Inspirit IoT, Inc. in 2016.
Keynote Title: Cognitive Computing on Heterogeneous Hardware Systems for the AI Revolution
Abstract: Many envision that AI (artificial intelligence) will usher in the next iteration of technology revolution, where humans and machines will work side-by-side to augment, enhance, or accelerate our ability to analyze, learn, create, and think. There are successful stories emerging fast already, such as IBM Watson, Microsoft HoloLens, and Google AlphaGo. One essential component to enable the new AI revolution is IoT (Internet of Things). Cognitive computing can learn from the rich IoT data, reason from models, and most importantly interact with us to perform complex tasks (ranging from healthcare to education to financial services) better than either humans or machines can do by themselves. Meanwhile, high-performance computing would be of paramount importance to help achieve the grand vision of cognitive computing. In this talk, Prof. Chen will share his recent research results on machine learning, reconfigurable computing, GPU computing, and cognitive application benchmarking. He will also present his recent work on extremely fast software and hardware modeling and the automated software/hardware co-design for accelerating cognitive computing workloads. Compelling AI applications will be introduced as well, such as autonomous driving, machine translation, and music synthesis.