Keynote Speech #1
The Art of Open Source and Real-Time for Autonomous Driving
TIER IV, the Autoware Foundation, and the University of Tokyo
Abstract: Autonomous driving technologies are becoming the future of automotive and mobility applications, yet not many of us find their norm and common sense. This talk presents Autoware, an open-source software project that provides full-stack capabilities of autonomous driving, alongside the lessons learned on real-time computing challenges.
Biography: Shinpei is the founder and CEO / CTO of TIER IV, and a co-founder and chairman of the Autoware Foundation. He also works as a specially appointed professor for the Graduate School of Information Science and Technology at the University of Tokyo. An internationally renowned expert in computer science, and a pioneer in the evolution of open-source software for autonomous driving technology. Shinpei was an associate professor at the Graduate School of Information Science, Nagoya University, from 2012 to 2016, where his team created the world’s first open-source software for autonomous driving technology, Autoware. He was also a postdoctoral scholar at Keio University, the University of Tokyo, Carnegie Mellon University, and University of California. His expertise includes computer architectures and operating systems for embedded and real-time systems as well as parallel and distributed systems.
Keynote Speech #2
Real-time Edge AI Services and Foundation Models for Internet of Things Applications
Department of Computer Science
University of Illinois Urbana-Champaign
Abstract: Advances in neural networks revolutionized modern machine intelligence, but important challenges remain when applying these solutions in real-time and IoT contexts; specifically, on lower-end embedded devices with multimodal sensors and distributed heterogeneous hardware. The talk discusses challenges in offering real-time machine intelligence services to support applications in resource-constrained distributed IoT environments. The intersection of IoT applications, real-time requirements, distribution challenges, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on embedded edge devices while retaining inference quality? 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-device GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? How to develop foundation models for IoT that offer extended inference capabilities from time-series data? The talk discusses recent advances in real-time edge AI and foundation models and presents evaluation results in the context of different real-time IoT 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/co-authored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He served as an Editor-in-Chief of the Journal of Real-Time Systems, 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 interaction with an external physical and social 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.