Abstract
Advances in neural network revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts; specifically, in cost-sensitive applications on lower-end embedded devices. The talk discusses challenges in offering real-time machine intelligence services at the edge to support applications in resource constrained environments. The intersection of IoT applications, real-time requirements, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on low-cost edge devices while retaining inference quality and offering confidence estimates in results? How to reduce the need for expensive manual labeling of IoT application data? How to improve the responsiveness of AI components to critical real-time stimuli in their physical environment? How to prioritize and schedule the execution of intelligent data processing workflows on edge-devi! ce GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? The talk discusses recent advances and presents evaluation results in the context of different real-time edge AI applications.
Biography
Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He serves as an Editor-in-Chief of the Journal of Real-Time Systems for over 10 years, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher’s research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interacti! on with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a fellow of IEEE and ACM.