Using machine learning for timing analysis: where do we stand?
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| Titel: | Using machine learning for timing analysis: where do we stand? |
|---|---|
| Autoren: | Amalou, Abderaouf Nassim, Puaut, Isabelle |
| Weitere Verfasser: | AMALOU, Abderaouf Nassim |
| Quelle: | Real-Time Systems. 61:300-305 |
| Verlagsinformationen: | Springer Science and Business Media LLC, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | [INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR], [INFO.INFO-PF] Computer Science [cs]/Performance [cs.PF], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO] Computer Science [cs], [INFO.INFO-ES] Computer Science [cs]/Embedded Systems |
| Beschreibung: | This paper presents our experience using Machine Learning (ML) to predict the Worst-Case Execution Time (WCET) of small code snippets on single-core platforms. We provide a concise overview of our work, highlight key observations made throughout our study, and advocate for further exploration of this topic. Keywords Worst-case execution time (WCET) estimation • Machine learning (ML) |
| Publikationsart: | Article |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| ISSN: | 1573-1383 0922-6443 |
| DOI: | 10.1007/s11241-025-09442-y |
| Rights: | Springer Nature TDM CC BY |
| Dokumentencode: | edsair.doi.dedup.....be06abce4e5405b4d41e8338bea0d5a0 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | This paper presents our experience using Machine Learning (ML) to predict the Worst-Case Execution Time (WCET) of small code snippets on single-core platforms. We provide a concise overview of our work, highlight key observations made throughout our study, and advocate for further exploration of this topic. Keywords Worst-case execution time (WCET) estimation • Machine learning (ML) |
|---|---|
| ISSN: | 15731383 09226443 |
| DOI: | 10.1007/s11241-025-09442-y |