License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ECRTS.2023.7
URN: urn:nbn:de:0030-drops-180367
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18036/
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Amalou, Abderaouf N ; Fromont, Elisa ; Puaut, Isabelle

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

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LIPIcs-ECRTS-2023-7.pdf (1 MB)

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.

BibTeX - Entry

@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/opus/volltexte/2023/18036},
  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}
}

Keywords: Worst-case execution time, machine learning, transformers, hybrid technique
Collection: 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)
Issue Date: 2023
Date of publication: 03.07.2023

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