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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.

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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)

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@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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Documents authored by Amalou, Abderaouf N

CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers

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