License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.NG-RES.2022.4
URN: urn:nbn:de:0030-drops-161120
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16112/
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Cattaneo, Daniele ; Magnani, Gabriele ; Cherubin, Stefano ; Agosta, Giovanni

Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time

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OASIcs-NG-RES-2022-4.pdf (0.5 MB)

Abstract

Precision tuning is an approximate computing technique for trading precision with lower execution time, and it has been increasingly important in embedded and high-performance computing applications. In particular, embedded applications benefit from lower precision in order to reduce or remove the dependency on computationally-expensive data types such as floating point. Amongst such applications, an important fraction are mission-critical tasks, such as control systems for vehicles or medical use-cases. In this context, the usefulness of precision tuning is limited by concerns about verificability of real-time and quality-of-service constraints. However, with the introduction of optimisations techniques based on integer linear programming and rigorous WCET (Worst-Case Execution Time) models, these constraints not only can be verified automatically, but it becomes possible to use precision tuning to automatically enforce these constraints even when not previously possible. In this work, we show how to combine precision tuning with WCET analysis to enforce a limit on the execution time by using a constraint-based code optimisation pass with a state-of-the-art precision tuning framework.

BibTeX - Entry

@InProceedings{cattaneo_et_al:OASIcs.NG-RES.2022.4,
  author =	{Cattaneo, Daniele and Magnani, Gabriele and Cherubin, Stefano and Agosta, Giovanni},
  title =	{{Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{4:1--4:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16112},
  URN =		{urn:nbn:de:0030-drops-161120},
  doi =		{10.4230/OASIcs.NG-RES.2022.4},
  annote =	{Keywords: Approximate Computing, Precision Tuning, Worst-Case Execution Time}
}

Keywords: Approximate Computing, Precision Tuning, Worst-Case Execution Time
Collection: Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)
Issue Date: 2022
Date of publication: 11.06.2022

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