• Medientyp: E-Book; Sonderdruck
  • Titel: Approximating WCET and energy consumption for fast multi-objective memory allocation
  • Beteiligte: Jadhav, Shashank [VerfasserIn]; Falk, Heiko [VerfasserIn]
  • Körperschaft: Technische Universität Hamburg ; Technische Universität Hamburg, Institute of Embedded Systems
  • Erschienen: Hamburg, 2022
  • Erschienen in: RTNS 2022: Proceedings of the 30th International Conference on Real-Time Networks and Systems
  • Umfang: 1 Online-Ressource (11 Seiten); Diagramme
  • Sprache: Deutsch
  • DOI: 10.15480/882.4375
  • ISBN: 9781450396509
  • Identifikator:
  • Schlagwörter: Multi-objective optimization ; Hard real-time systems, ; Approxima- tion ; Metaheuristic algorithms, ; SPM allocation
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: Worst-Case Execution Time (WCET) is the most important design criterion in the domain of hard real-time systems. Most embedded systems also need to satisfy additional design criteria like, e.g., energy consumption. Performing WCET and energy analyses statically at compile-time can be time-consuming. Consequently, minimizing WCET and energy consumption of the code at the compiler level using multi-objective optimization can be a time-consuming process. In this paper, we propose an approximation model to quickly approximate the WCET and energy consumption of the code at compile-time. Instead of using traditional WCET and energy analyses, we exploit this approximation model to perform ScratchPad Memory (SPM) allocation-based multi-objective optimization. Furthermore, we solve the multi-objective optimization problem using metaheuristic algorithms and explore the trade-offs between WCET and energy consumption. Using the proposed approximation model, we achieved, on average, a 94.12% reduction in compilation time and maintained the quality of the Pareto optimal solutions while performing the multi-objective optimization. Furthermore, the approximation error while using the proposed approximation model was in an acceptable range of 2% - 4% on average.
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