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WORTEX: Worst-Case Execution Time and Energy Estimation in Low-Power Microprocessors Using Explainable ML

Authors Hugo Reymond , Abderaouf Nassim Amalou , Isabelle Puaut

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Author Details

Hugo Reymond
  • Univ. Rennes, INRIA, CNRS, IRISA, Rennes, France
Abderaouf Nassim Amalou
  • Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France
Isabelle Puaut
  • Univ. Rennes, INRIA, CNRS, IRISA, Rennes, France

Funding

  • Reymond, Hugo: This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the "Investing for the Future" program under reference ANR-10-LABX-07-01.
  • Amalou, Abderaouf Nassim: This work was supported by the Agence Nationale de la Recherche under grant number ANR-22-CE92-0066.

Acknowledgements

The authors would like to thank Hector Chabot for his essential work on the creation of this paper dataset.

Cite AsGet BibTex

Hugo Reymond, Abderaouf Nassim Amalou, and Isabelle Puaut. WORTEX: Worst-Case Execution Time and Energy Estimation in Low-Power Microprocessors Using Explainable ML. In 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024). Open Access Series in Informatics (OASIcs), Volume 121, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.WCET.2024.1

Abstract

Real-time and energy-constrained systems heavily rely on estimates of the worst-case execution time (WCET) and worst-case energy consumption (WCEC) of code snippets to ensure trustworthy operation. Designing architecture-specific analytical models for time and energy is often challenging and time-consuming. In situations where analytical models are unavailable or incomplete, machine learning (ML) techniques emerge as a promising solution to build WCEC/WCET models. This paper introduces WORTEX, a toolkit for WCEC/WCET estimation of basic blocks based on ML techniques. To ensure the real-world applicability of its models, WORTEX extracts large datasets of basic blocks from real programs and precisely measures their energy consumption/execution time on the physical target platform. The dataset is used to train various WCEC/WCET models using different ML techniques. Experimental results on simple and time-predictable hardware show that even the most basic ML techniques provide accurate results, that never underestimate actual values. We also discuss the use of explainability techniques to gain trustworthiness for the models.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
Keywords
  • Worst-Case Execution Time (WCET)
  • Worst-Case Energy Consumption (WCEC)
  • Machine Learning
  • Explainable ML models

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Supplementary Materials

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