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RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs

Authors Khalil Esper , Jan Spieck , Pierre-Louis Sixdenier , Stefan Wildermann , Jürgen Teich

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Author Details

Khalil Esper
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Jan Spieck
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Pierre-Louis Sixdenier
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Stefan Wildermann
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Jürgen Teich
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

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Khalil Esper, Jan Spieck, Pierre-Louis Sixdenier, Stefan Wildermann, and Jürgen Teich. RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs. In Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023). Open Access Series in Informatics (OASIcs), Volume 108, pp. 7:1-7:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


In embedded systems, applications frequently have to meet non-functional requirements regarding, e.g., real-time or energy consumption constraints, when executing on a given MPSoC target platform. Feedback-based controllers have been proposed that react to transient environmental factors by adapting the DVFS settings or degree of parallelism following some predefined control strategy. However, it is, in general, not possible to give formal guarantees for the obtained controllers to satisfy a given set of non-functional requirements. Run-time requirement enforcement has emerged as a field of research for the enforcement of non-functional requirements at run-time, allowing to define and formally verify properties on respective control strategies specified by automata. However, techniques for the automatic generation of such controllers have not yet been established. In this paper, we propose a technique using reinforcement learning to automatically generate verifiable feedback-based enforcers. For that, we train a control policy based on a representative input sequence at design time. The learned control strategy is then transformed into a verifiable enforcement automaton which constitutes our run-time control model that can handle unseen input data. As a case study, we apply the approach to generate controllers that are able to increase the probability of satisfying a given set of requirement verification goals compared to multiple state-of-the-art approaches, as can be verified by model checkers.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Multicore architectures
  • Theory of computation → Linear logic
  • Theory of computation → Modal and temporal logics
  • Hardware → Finite state machines
  • Computer systems organization → Self-organizing autonomic computing
  • Theory of computation → Verification by model checking
  • Mathematics of computing → Probabilistic representations
  • Computing methodologies → Reinforcement learning
  • Verification
  • Runtime Requirement Enforcement
  • Reinforcement Learning


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