Brain-inspired Learning Systems for Ubiquitous AI

Speaker: Bipin Rajendran
Affiliation: New Jersey Institute of Technology

Abstract: The dream of human-level artificial intelligence (AI) may well be within reach, thanks to the significant advances in machine learning over the past decade. However, a fundamental reimagining of the algorithmic paradigms, system architecture, and underlying technologies is necessary to bring intelligence and analytics capability to the trillions of energy-constrained devices that will be embedded in our physical environment. In this talk, I will first discuss the target specifications for nanoscale devices and circuits necessary to build energy-efficient intelligent systems capable of learning in the field, as well as in an accelerated manner, surpassing human performance. I will then present recent results from our research on new devices, algorithms, and systems for event-driven learning and adaptation that are inspired by the key architectural and organizational principles of the brain.

Biography: Bipin Rajendran received a B.Tech degree from I.I.T. Kharagpur in 2000 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 2003 and 2006, respectively.  He was a Master Inventor and Research Staff Member at IBM T.J. Watson Research Center in New York during 2006-2012 and a faculty member in the Electrical Engineering Department at I.I.T. Bombay during 2012-2015. His research focuses on building algorithms, devices and systems for brain-inspired computing. He has authored over 60 papers in peer-reviewed journals and conferences, and has been issued 55 U.S. patents. He is currently an Associate Professor of Electrical & Computer Engineering at New Jersey Institute of Technology.

For more information, contact Prof. Danijela Cabric ()

Date(s) - Nov 17, 2017
1:00 pm - 2:30 pm

EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095