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

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. 2012 ACM Subject Classification Computer systems organization → Real-time systems; Software and its engineering → Software notations and tools
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https://ut3-toulouseinp.hal.science/hal-03707284
Contributor : Thomas Carle Connect in order to contact the contributor
Submitted on : Tuesday, June 28, 2022 - 2:38:59 PM
Last modification on : Monday, July 4, 2022 - 11:56:24 AM

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Iryna De Albuquerque Silva, Thomas Carle, Adrien Gauffriau, Claire Pagetti. ACETONE: Predictable Programming Framework for ML Applications in Safety-Critical Systems. 24th Euromicro Conference on Real-Time Systems (ECRTS 2022), Jun 2022, Modène, Italy. ⟨10.4230/DARTS.8.1.6⟩. ⟨hal-03707284⟩

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