2 Search Results for "Amalou, Abderaouf N"

Document
Invited Talk
DOI: 10.4230/OASIcs.WCET.2024.7
Machine Learning for Timing Analysis: The Good, the Bad and the Ugly (Invited Talk)

Authors: Isabelle Puaut

Published in: OASIcs, Volume 121, 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024)

Abstract
The microarchitecture of processors is becoming increasingly complex and less documented, making the design of timing models for WCET calculation increasingly complicated, if not impossible. We have recently experimented with the use of machine learning techniques (ML) to predict the WCET of basic blocks [Amalou et al., 2021; Abderaouf N. Amalou et al., 2022; Amalou et al., 2023; Abderaouf Nassim Amalou, 2023; Abderaouf Nassim Amalou et al., 2024]. Predicted WCETs can then be integrated into static WCET calculation tools, resulting in a hybrid WCET calculation. In this keynote, we present our experience using ML for WCET calculation, across a range of architectures, from very simple ones (MSP430, Cortex M4) to more complex architectures. Rather than presenting only what worked, we also discuss in this keynote the bad, and even very bad, surprises encountered during the process, and how we overcame (most of) them.
Cite as

Isabelle Puaut. Machine Learning for Timing Analysis: The Good, the Bad and the Ugly (Invited Talk). In 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024). Open Access Series in Informatics (OASIcs), Volume 121, p. 7:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{puaut:OASIcs.WCET.2024.7,
  author =	{Puaut, Isabelle},
  title =	{{Machine Learning for Timing Analysis: The Good, the Bad and the Ugly}},
  booktitle =	{22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024)},
  pages =	{7:1--7:1},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-346-1},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{121},
  editor =	{Carle, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.WCET.2024.7},
  URN =		{urn:nbn:de:0030-drops-204753},
  doi =		{10.4230/OASIcs.WCET.2024.7},
  annote =	{Keywords: Worst-Case Execution Time (WCET) estimation, Machine Learning, Explainable ML models}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2023.7
CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers

Authors: Abderaouf N Amalou, Elisa Fromont, and Isabelle Puaut

Published in: LIPIcs, Volume 262, 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)

Abstract
This paper presents CAWET, a hybrid worst-case program timing estimation technique. CAWET identifies the longest execution path using static techniques, whereas the worst-case execution time (WCET) of basic blocks is predicted using an advanced language processing technique called Transformer-XL. By employing Transformers-XL in CAWET, the execution context formed by previously executed basic blocks is taken into account, allowing for consideration of the micro-architecture of the processor pipeline without explicit modeling. Through a series of experiments on the TacleBench benchmarks, using different target processors (Arm Cortex M4, M7, and A53), our method is demonstrated to never underestimate WCETs and is shown to be less pessimistic than its competitors.
Cite as

Abderaouf N Amalou, Elisa Fromont, and Isabelle Puaut. CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{amalou_et_al:LIPIcs.ECRTS.2023.7,
  author =	{Amalou, Abderaouf N and Fromont, Elisa and Puaut, Isabelle},
  title =	{{CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{7:1--7:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-280-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{262},
  editor =	{Papadopoulos, Alessandro V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2023.7},
  URN =		{urn:nbn:de:0030-drops-180367},
  doi =		{10.4230/LIPIcs.ECRTS.2023.7},
  annote =	{Keywords: Worst-case execution time, machine learning, transformers, hybrid technique}
}
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