Programm

Abstract:

Applications of machine learning (ML) techniques to design problems has seen a lot of excitement and promise at lower levels of abstraction. By contrast, corresponding approaches at the system level have been relatively less explored. In this talk, we will discuss applications of ML for system-level design. In particular, fast and accurate models that can drive rapid, early design space exploration, co-design and co-optimizations are critical for any systematic and well-defined design process. Recently, ML-based, predictive models have emerged to bridge the gap between traditional slow simulation-based methodologies and inaccurate analytical models. Such models are trained to learn and predict system behavior, performance and power consumption across abstraction levels, across heterogeneous hardware/software components and across time. We will discuss solutions, challenges and opportunities for ML-based cross-layer, cross-platform and cross-temporal modeling in the talk. Furthermore, we will demonstrate how such ML-based models can be leveraged to drive system architecture co-design and exploration, system programming and proactive runtime management.

Bio:

Andreas Gerstlauer is a Cullen Trust for Higher Education Endowed Professor and Associate Chair for Academic Affairs in the Electrical and Computer Engineering (ECE) Department at The University of Texas at Austin. He received his Ph.D. degree in Information and Computer Science (ICS) from the University of California, Irvine (UCI) in 2004. Prior to joining UT Austin in 2008, he was an Assistant Researcher in the Center for Embedded Computer Systems (CECS) at UC Irvine, leading a research group to develop electronic system-level design tools. Dr. Gerstlauer is co-author on 3 books and more than 150 conference and journal publications. His work was recognized with the 2024 HASP, 2021 MLCAD, 2016 DAC and 2015 SAMOS best paper awards, several best paper nominations from, among others, DAC, DATE, FCCM and HOST conferences, and as a 2021 IEEE HSTTC Top Pick in Hardware and Embedded Security and one of the most influential contributions in 10 years at DATE in 2008. He is the recipient of a 2016-2017 Humboldt Research Fellowship. He serves or has served as an Senior, Associate and Special Issue Editor for ACM TECS and TODAES journals as well as General or Program Chair for major international conferences such as ESWEEK, MEMOCODE, CODES+ISSS and SAMOS.  His research interests include system-level design automation, system modeling, design languages and methodologies, and embedded hardware and software synthesis.