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Phoenix, AZ /

Applying machine learning algorithms for energy-efficient self-driving vehicles

University of Advancing Technology 2625 W Baseline Rd, Room: 206/207 , Tempe, AZ (map)

• What we'll do


* 6:30 pm - 7:30 pm: Applying machine learning algorithms towards Energy-efficient Autonomous Drive • 7:30 pm: Raffle & Networking at UAT

Room No: 136 (Theater), University of Advanced Technology

Topic: Applying machine learning algorithms towards Energy-efficient Autonomous Drive

Speaker: Yu (Kevin) Cao


Machine learning algorithms have made significant progresses, achieving the accuracy close to, or even better than human-level perception in various tasks. Integrated with multiple types of sensors, they enable autonomous drive (AD) and advanced drive assistance systems (ADAS), which are highly visible in Arizona. On the other hand, hardware implementation of deep learning algorithms is still too expensive in both computation and power consumption. There is a timely need to map the latest learning algorithms in ADAS/AD to application-specific hardware (e.g., FPGA, ASIC, etc.), in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness.


Dr. Cao received the B.S. degree in physics from Peking University in 1996. He received the M.A. degree in biophysics and the Ph.D. degree in electrical engineering from University of California, Berkeley, in 1999 and 2002, respectively.

He is now a Professor of Electrical Engineering at Arizona State University, Tempe, Arizona. He has published numerous articles and two books on nanoscale CMOS design. His research interests include physical design of nanoelectronics, design solutions for reliability, and hardware integration for on-chip learning. He is an IEEE Fellow.

• What to bring

A curious mind and of course you can always bring a friend with you.

• Important to know


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