KEYNOTE 3 – Dr. Lothar Thiele (ETH)

The quest for resilient embedded systems in the era of machine learning

Abstract: It is a long journey that data must travel: starting from the processes observed by an embedded system with the help of sensors, to the processing and communication of the corresponding data, and eventually to the extraction of knowledge and decision-making that could impact the observed processes. This sequence of stages remains largely unaffected by whether the involved processes are biological, physical, chemical, or of human origin. The corresponding distributed embedded systems are typically deeply integrated within their physical environments. Therefore, the associated challenges extend well beyond the typical considerations of real-time and predictable behavior that encompass a comprehensive understanding of the interplay between concurrent activities on shared resources. Instead, key scientific questions are closely linked to the need for adaptive and resilient functionality in the face of extreme resource constraints and fluctuating environmental conditions such as sensor degradation, data and concept drift, energy availability, and wireless connectivity. Consequently, it becomes necessary to consider embedded machine learning, information processing and communication across components and abstraction layers.

The focus will center on models and methods for designing resilient distributed embedded systems while discussing future challenges associated with embedded machine learning. However, the discussion extends beyond the scientific challenges encountered at each stage of the data journey. Can novel data-driven techniques and machine learning methods support us in gaining a better understanding of environmental processes, such as destructive processes in high alpine regions, and pave the way for reliable early warning systems? Additionally, we will explore examples relevant to the prediction of air pollution in highly contaminated areas and the subsequent derivation of embedded control mechanisms from such predictions.

Bio: Lothar Thiele joined ETH Zurich, Switzerland, as a full Professor of Computer Engineering, in 1994. His research interests include models, methods and software tools for the design of real-time embedded systems, internet of things, cyberphysical systems, sensor networks, embedded software and bioinspired optimization techniques.

In 1986 he received the “Dissertation Award” of the Technical University of Munich, in 1987, the “Outstanding Young Author Award” of the IEEE Circuits and Systems Society, in 1988, the Browder J. Thompson Memorial Award of the IEEE, and in 2000-2001, the “IBM Faculty Partnership Award”. In 2004, he joined the German Academy of Sciences Leopoldina. In 2005, he was the recipient of the Honorary Blaise Pascal Chair of University Leiden, The Netherlands. Since 2010, he is a member of the Academia Europaea. In 2013, he joined the National Research Council of the Swiss National Science Foundation SNF. Lothar Thiele received the “EDAA Lifetime Achievement Award” in 2015. Since 2017, he is Associate Vice President of ETH Zurich for Digital Transformation. Lothar Thiele has been elected IFIP Fellow by the International Federation for Information Processing (IFIP) as part of its first cohort of fellows in 2020. In 2021, he received the IEEE TCRTS Achievement and Leadership Award. In 2022 he received the “Test-of-Time-Awards” at SenSys and EMSOFT for the contributions on “Low-Power Wireless Bus” and “Real-Time Interfaces for Composing Real-Time Systems”.