Philip Brisk - Univ. of California, Riverside, CA
The rate of growth of Big Data, slowing down of Moore’s law, and the rise of emerging applications pose significant challenges in the design of large-scale computing systems with high-performance, energy-efficiency, and reliability. This tutorial will consider solutions based on machine learning and data analytics to address these challenges.
Some specific topics include:
1. How to use machine learning and statistical modeling for effective design space exploration of computing systems to optimize for power, performance, and thermal metrics?
2. How to use machine learning techniques to efficiently manage resources of computing systems (e.g., power, memory, interconnects) to improve performance and energy-efficiency?
3. How to use machine learning techniques same-generation GPU prediction, cross-generation GPU prediction, and CPU to FPGA prediction?
Jana Doppa is the George and Joan Berry Assistant Professor in the School of EECS at Washington State University, Pullman. He earned his PhD working with the AI group at Oregon State University (2014); and his M.Tech from Indian Institute of Technology, Kanpur, India (2006). His current research interests are at the intersection of machine learning and electronic design automation. He received NSF CAREER Award (2019), an Outstanding Paper Award at the AAAI (2013) conference, a Google Faculty Research Award (2015), the Outstanding Innovation in Technology Award from OSU (2015), and a Outstanding Reviewer Award at the NeurIPS (2018) conference.
Philip Brisk received the B.S., M.S., and Ph.D., all in Computer Science, from UCLA in 2002, 2003, and 2006 respectively. From 2006-2009, he was a postdoctoral scholar at EPFL in Lausanne, Switzerland. Since 2009, he has been with the University of California, Riverside; he has been promoted to Professor effective July 1, 2019. Dr. Brisk's research interests lie at the intersection between processor architecture, VLSI/CAD, compilers, FPGAs, and reconfigurable computing; most recently, he has been applying these principles to the design and analysis of biological instruments. He is a Senior Member of the ACM and IEEE, and is presently an Associate Editor of the IEEE Transactions of Computer-Aided Design on Integrated Circuits and Systems (TCAD) and Integration: The VLSI Journal.