Abstract
Modern embedded and cyber-physical applications consist of critical and non-critical tasks co-located on multiprocessor systems on chip (MPSoCs). Co-location of tasks results in contention for shared resources, resulting in interference on interconnect, processing units, storage, etc. Hence, machine learning-based resource managers must operate even non-critical tasks within certain constraints to ensure proper execution of critical tasks. In this paper we demonstrate and evaluate countermeasures based on backup policies to enhance rule-based reinforcement learning to enforce constraints. Detailed experiments reveal the CPUs’ performance degradation caused by different designs, as well as their effectiveness in preventing constraint violations. Further, we exploit the interpretability of our approach to further improve the resource manager’s operation by adding designers’ experience into the rule set.
Funding source: DFG
Award Identifier / Grant number: HE4584/7-2
About the authors
Florian Maurer received his BSc and MSc degrees from the Technical University of Munich, Munich, Germany, in 2016 and 2018, respectively. He is currently a PhD candidate at the Technical University of Munich, Munich, Germany and working toward the PhD degree in electrical and computer engineering. His research interests include self-aware and self-adaptive multi- and many-core systems.
Moritz Thoma received his BEn degree from the Baden-Wuerttemberg Cooperative State University Stuttgart, Germany and his MSc degree from the Technical University of Munich, Germany. He is currently a PhD candidate at BMW Group Munich, Germany pursuing a PhD degree in electrical and computer engineering. His research interests include efficient AI deployment and efficient MLOps.
Anmol Surhonne received the BE degree from the PES Institute of Technology, and the MSc degree from Nanyang Technological University and Technical University of Munich. He is currently working towards a PhD degree in electrical and computer engineering at the Technical University of Munich. His research interests include self aware multi/many core systems and machine learning.
Prof. Bryan Donyanavard is an Assistant Professor in the Computer Science department at San Diego State University. Prior to that, he was a researcher in the IoT and Cyber-physical Systems group at Ericsson in Stockholm. His research focuses on runtime management of resource-constrained systems in software and architecture. He received his Ph.D. in Computer Science from UC Irvine in 2019. He has previously worked as a software engineer at Sun Microsystems and Google, and spent time as a visiting researcher at TU Munich.
Prof. Dr. sc.techn. Andreas Herkersdorf is a professor and the Head of Department Computer Engineering at Technical University of Munich (TUM). He received a Dr. degree from ETH Zurich, Switzerland, in 1991. Between 1988 and 2003, he has been in technical and management positions with the IBM Research Laboratory in Rüschlikon, Switzerland. Since 2003, Dr. Herkersdorf is the Chair Professor of Integrated Systems at TUM. He is a senior member of the IEEE, member of National Academy of Science and Engineering (acatech) and serves as editor for Springer and De Gruyter journals for design automation and information technology. His research interests include application-specific multi-processor architectures, IP network processing, Network on Chip and self-adaptive fault-tolerant computing.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: Funded by DFG (grant number HE4584/7-2).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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