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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}
}

Document

DOI: 10.4230/DagSemProc.07181.7

Parallel universes to improve the diagnosis of cardiac arrhythmias

Authors: Elisa Fromont, René Quiniou, and Marie-Odile Cordier

Published in: Dagstuhl Seminar Proceedings, Volume 7181, Parallel Universes and Local Patterns (2007)

Abstract

We are interested in using parallel universes to learn interpretable models that can be subsequently used to automatically diagnose cardiac arrythmias. In our study, parallel universes are heterogeneous sources such as electrocardiograms, blood pressure measurements, phonocardiograms etc. that give relevant information about the cardiac state of a patient. To learn interpretable rules, we use an inductive logic programming (ILP) method on a symbolic version of our data. Aggregating the symbolic data coming from all the sources before learning, increases both the number of possible relations that can be learned and the richness of the language. We propose a two-step strategy to deal with these dimensionality problems when using ILP. First, rules are learned independently in each universe. Second, the learned rules are used to bias a new learning process from the aggregated data. The results show that this method is much more efficient than learning directly from the aggregated data. Furthermore the good accuracy results confirm the benefits of using multiple sources when trying to improve the diagnosis of cardiac arrythmias.

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Elisa Fromont, René Quiniou, and Marie-Odile Cordier. Parallel universes to improve the diagnosis of cardiac arrhythmias. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{fromont_et_al:DagSemProc.07181.7,
  author =	{Fromont, Elisa and Quiniou, Ren\'{e} and Cordier, Marie-Odile},
  title =	{{Parallel universes to improve the diagnosis of cardiac arrhythmias}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.7},
  URN =		{urn:nbn:de:0030-drops-12600},
  doi =		{10.4230/DagSemProc.07181.7},
  annote =	{Keywords: Parallel universes, inductive logic programming, medical application, declarative bias}
}

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

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Parallel universes to improve the diagnosis of cardiac arrhythmias

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