Workshop on Next Generation Real-Time Embedded Systems

Invited Paper

Khalil Esper, Stefan Wildermann, Jürgen Teich

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany


Khalil Esper is a PhD student at Friedrich-Alexander-Universität Erlangen Nürnberg (FAU), Germany. He received his European joint master degree in embedded computing systems from TU Kaiserslautern, Germany and NTNU, Norway in 2020. His current research focuses on optimization techniques in multi-core systems. He is also involved in the Transregional Research Center 89 "Invasive Computing".

Stefan Wildermann received the Diploma degree and Ph.D. degree (Dr.-Ing.) in computer science from Friedrich-Alexander-Universität Erlangen Nürnberg (FAU), Erlangen, Germany, in 2006 and 2012, respectively. He leads the Reconfigurable Computing Group, CS Department, FAU. Since then he has been a Research Assistant, a Lecturer, and a Group Leader with the Chair of Hardware/Software Co-Design, FAU. His current research interests include reconfigurable computing and system-level design automation for embedded systems.

Jürgen Teich (Fellow, IEEE) received the M.S. degree (Dipl.-Ing. with honors) from the University of Kaiserslautern, Kaiserslautern, Germany, in 1989 and the Ph.D. degree (summa cum laude) from the University of Saarland, Saarbrücken, Germany, in 1993.,In 1994, he joined the DSP design group of Prof. E. A. Lee in the Department of Electrical Engineering and Computer Sciences (EECS), University of California at Berkeley, Berkeley, CA, USA (postdoctoral work). From 1995 to 1998, he held a position at the Institute of Computer Engineering and Communications Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland (habilitation). From 1998 to 2002, he was Full Professor in the Electrical Engineering and Information Technology Department, University of Paderborn, Paderborn, Germany. Since 2003, he has been Full Professor in the Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany, holding a Chair in Hardware/Software Co-Design.,Prof. Teich was elected member of the Academia Europaea in 2011. Since 2010, he has also been the coordinator of the Transregional Research Center 89 on Invasive Computing funded by the German Research Foundation (DFG).


Many applications vary a lot in execution time depending on their workload. A prominent example is image processing applications, where the execution time is dependent on the content or the size of the processed input images. An interesting case is when these applications have quality-of-service requirements such as soft deadlines, that they should meet as good as possible. A further complicated case is when such applications have one or even multiple further objectives to optimize like, e.g., energy consumption. Approaches that dynamically adapt the processing resources to application needs under multiple optimization goals and constraints can be characterized into the application-specific and feedback-based techniques. Whereas application-specific approaches typically statically use an offline stage to determine the best configuration for each known workload, feedback-based approaches, using, e.g., control theory, adapt the system without the need of knowing the effect of workload on these goals. In this paper, we evaluate a state-of-the-art approach of each of the two categories and compare them for image processing applications in terms of energy consumption and number of deadline misses on a given many-core architecture. In addition, we propose a second feedback-based approach that is based on finite state machines (FSMs). The obtained results suggest that whereas the state-of-the-art application-specific approach is able to meet a specified latency deadline whenever possible while consuming the least amount of energy, it requires a perfect characterization of the workload on a given many-core system. If such knowledge is not available, the feedback-based approaches have their strengths in achieving comparable energy savings, but missing deadlines more often.