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SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI
Barcelona Supercomputing Center, Spain.
BRTA Basque Research and Technology Alliance, Spain.
RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.ORCID-id: 0000-0002-1043-8773
Navinfo Europe, Netherlands.
Vise andre og tillknytning
2023 (engelsk)Inngår i: Proceedings -Design, Automation and Test in Europe, DATE, Institute of Electrical and Electronics Engineers Inc. , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2023.
Emneord [en]
Deep learning, Embedded systems, Product design, Software testing, Stochastic systems, Advanced softwares, Automotives, Competitive factor, Economic success, Embedded-system, Functional Safety, Learning techniques, Safety requirements, Software functions, Software products, Fault tolerance
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-65679DOI: 10.23919/DATE56975.2023.10137128Scopus ID: 2-s2.0-85162662708ISBN: 9783981926378 (digital)OAI: oai:DiVA.org:ri-65679DiVA, id: diva2:1787152
Konferanse
2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023. Antwerp. 17 April through 19 April, 2023
Merknad

ACKNOWLEDGEMENTS The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595. BSC authors have also been supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GBC21/AEI/10.13039/501100011033.

Tilgjengelig fra: 2023-08-11 Laget: 2023-08-11 Sist oppdatert: 2023-08-11bibliografisk kontrollert

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