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ACETONE: Predictable Programming Framework for ML Applications in Safety-Critical Systems

Authors Iryna De Albuquerque Silva , Thomas Carle , Adrien Gauffriau, Claire Pagetti

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Author Details

Iryna De Albuquerque Silva
  • ONERA, Toulouse, France
Thomas Carle
  • IRIT - Univ Toulouse 3 - CNRS, France
Adrien Gauffriau
  • Airbus, Toulouse, France
Claire Pagetti
  • ONERA, Toulouse, France

Funding

This project received funding from the French "Investing for the Future - PIA3" program within the Artificial and Natural Intelligence Toulouse Institute (ANITI).

Cite As Get BibTex

Iryna De Albuquerque Silva, Thomas Carle, Adrien Gauffriau, and Claire Pagetti. ACETONE: Predictable Programming Framework for ML Applications in Safety-Critical Systems. In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 231, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ECRTS.2022.3

Abstract

Machine learning applications have been gaining considerable attention in the field of safety-critical systems. Nonetheless, there is up to now no accepted development process that reaches classical safety confidence levels. This is the reason why we have developed a generic programming framework called ACETONE that is compliant with safety objectives (including traceability and WCET computation) for machine learning. More practically, the framework generates C code from a detailed description of off-line trained feed-forward deep neural networks that preserves the semantics of the original trained model and for which the WCET can be assessed with OTAWA. We have compared our results with Keras2c and uTVM with static runtime on a realistic set of benchmarks.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering → Software notations and tools
Keywords
  • Real-time safety-critical systems
  • Worst Case Execution Time analysis
  • Artificial Neural Networks implementation

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