Abstract: The artificial intelligence platforms are being increasingly deployed in safety-critical applications and autonomous systems, self-driving cars, robots, drones, to name a few. Unlike AI at the cloud environments, the AI at these platforms need to perform reliably under changing environmental conditions and robust against different types of noise, while meeting stringent energy and time constraints. The reliability of AI platforms in unreliable environments is therefore a key challenge for deployment of AI in real-time safety-critical systems. This paper will present a broad perspective on how to design AI platforms to achieve this unique goal. First, we will present examples of AI architecture and algorithm that can assist in improving robustness against dynamic environment and noise, natural and adversarial. Next, we will discuss examples of how to make AI platforms robust against hardware induced noise and variation. The preceding discussions will focus on AI based on statistical machine learning models, including, deep learning. Finally, we will present a new generation of AI models that couple statistical learning with dynamical systems and neuro-inspired learning to enhance the reliability of AI models. The talk will conclude with future research opportunities and directions in this area.

Saibal Mukhopadhyay received his B. E. degree in Electronics and Telecommunication Engineering from Jadavpur University, Calcutta, India, in 2000. He received a Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 2006. He was with the IBM T. J. Watson Research Center, Yorktown Heights, NY as a Research Staff Member. Since September 2007 he has been with the School of Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, GA, where he is currently a Joseph M. Pettit Professor of Electrical and Computer Engineering. His current research interests include neuromorphic computing and mixed-signal systems. Dr. Mukhopadhyay received the Office of Naval Research Young Investigator Award in 2012, the National Science Foundation CAREER Award in 2011, the IBM Faculty Partnership Award in 2009 and 2010, the SRC Inventor Recognition Award in 2008, the SRC Technical Excellence Award in 2005, and the IBM PhD Fellowship Award for years 2004-2005. He has received the IEEE Transactions on VLSI Systems (TVLSI) Best Paper Award in 2014, the IEEE Transactions on Component, Packaging, and Manufacturing Technology (TCPMT) Best Paper Award in 2014, the IEEE/ACM International Symposium on Low-power Electronic Design (ISLPED) Best Paper Award in 2014, the International Conference on Computer Design (ICCD) Best Paper Award in 2004, the IEEE Nano Best Student Paper Award in 2003, and multiple Best in Session Awards in SRC TECHCON in 2014 and 2005. He has authored or co-authored over 150 papers in refereed journals and conferences, and has been awarded six (6) U.S. patents. He is a Senior Member of IEEE.