Abstract

Modern autonomous systems are an ensemble of multiple components implementing machine learning, control, scheduling, and security. Current design flows aim for each of these components to work perfectly, and system design consists of composing these components together. As a result research in machine learning aims towards near-perfect classification or estimation, scheduling techniques aim to meet all deadlines, and security algorithms aim towards fully secure systems. While such separation of concerns has served us well till now, as systems become more complex, this goal towards achieving perfection is becoming unreasonable. In this talk we will argue that we can design safe autonomous systems, without requiring its components to be perfect -- as long as the imperfections of one component are balanced by suitable actions from other components. Such a design approach is potentially more reasonable and cost effective, and we will provide examples of how it plays out. 

Speaker's Bio

Samarjit Chakraborty is a Kenan Distinguished Professor and Chair of the Department of Computer Science at UNC Chapel Hill. Prior to coming here in 2019, he was a professor of Electrical Engineering at the Technical University of Munich in Germany, where he held the Chair of Real-Time Computer Systems for 11 years. Before that he was an assistant professor of Computer Science at the National University of Singapore for 5 years. He obtained his PhD from ETH Zurich in 2003. His research interests can be best described as a random walk through various aspects of designing hardware and software for embedded computers. He is a Fellow of the IEEE and received the 2023 Humboldt Professorship Award from Germany.