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Documents authored by Spieck, Jan

Document
DOI: 10.4230/OASIcs.NG-RES.2025.3
Co-Design of Systems-On-Chip for Sustainability

Authors: Jan Spieck, Dominik Walter, Jan Waschkeit, and Jürgen Teich

Published in: OASIcs, Volume 128, Sixth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2025)

Abstract
This paper introduces a novel approach to the co-design of sustainable embedded systems through multi-objective design space exploration (DSE). We propose a two-phase methodology that optimizes both the multiprocessor system-on-chip (MPSoC) architecture and application mappings, considering sustainability, reliability, performance, and cost as optimization objectives of equal importance. Unlike existing approaches, our method thereby accounts for both operational and embodied emissions, providing a more comprehensive assessment of sustainability. The first phase employs intra-application DSEs to explore Pareto-optimal constraint graphs for each application. The second phase, an inter-application DSE, combines these results to explore sustainable target architectures and corresponding application mappings. Our approach incorporates detailed models for embodied emissions (scope 1 and scope 2), operational emissions, reliability, performance, and cost. The evaluation demonstrates that our sustainability-aware DSE is able to explore design spaces overlooked by traditional approaches, supported by superior results in four key objectives. This enables the development of more environmentally friendly embedded systems while still achieving high performance and reliability.
Cite as

Jan Spieck, Dominik Walter, Jan Waschkeit, and Jürgen Teich. Co-Design of Systems-On-Chip for Sustainability. In Sixth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2025). Open Access Series in Informatics (OASIcs), Volume 128, pp. 3:1-3:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{spieck_et_al:OASIcs.NG-RES.2025.3,
  author =	{Spieck, Jan and Walter, Dominik and Waschkeit, Jan and Teich, J\"{u}rgen},
  title =	{{Co-Design of Systems-On-Chip for Sustainability}},
  booktitle =	{Sixth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2025)},
  pages =	{3:1--3:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-366-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{128},
  editor =	{Yomsi, Patrick Meumeu and Wildermann, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2025.3},
  URN =		{urn:nbn:de:0030-drops-229898},
  doi =		{10.4230/OASIcs.NG-RES.2025.3},
  annote =	{Keywords: System-on-Chip, Sustainability, Multi-objective Optimization, Design Space Exploration, Embedded Systems, Carbon Emissions}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2023.7
RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs

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

Published in: OASIcs, Volume 108, Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)

Abstract
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.
Cite as

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)

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@InProceedings{esper_et_al:OASIcs.NG-RES.2023.7,
  author =	{Esper, Khalil and Spieck, Jan and Sixdenier, Pierre-Louis and Wildermann, Stefan and Teich, J\"{u}rgen},
  title =	{{RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs}},
  booktitle =	{Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)},
  pages =	{7:1--7:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-268-6},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{108},
  editor =	{Terraneo, Federico and Cattaneo, Daniele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2023.7},
  URN =		{urn:nbn:de:0030-drops-177380},
  doi =		{10.4230/OASIcs.NG-RES.2023.7},
  annote =	{Keywords: Verification, Runtime Requirement Enforcement, Reinforcement Learning}
}
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