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2024 (English)In: 2024 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION, DATE, Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]
There is an evident need to complement embedded critical control logic with AI inference, but today's AI-capable hardware, software, and processes are primarily targeted towards the needs of cloud-centric actors. Telecom and defense airspace industries, which make heavy use of specialized hardware, face the challenge of manually hand-tuning AI workloads and hardware, presenting an unprecedented cost and complexity due to the diversity and sheer number of deployed instances. Furthermore, embedded AI functionality must not adversely affect real-time and safety requirements of the critical business logic. To address this, end-to-end AI pipelines for critical platforms are needed to automate the adaption of networks to fit into resource-constrained devices under critical and real-time constraints, while remaining interoperable with de-facto standard AI tools and frameworks used in the cloud. We present two industrial applications where such solutions are needed to bring AI to critical and resource-constrained hardware, and a generalized end-to-end AI pipeline that addresses these needs. Crucial steps to realize it are taken in the industry-academia collaborative FASTER-AI project.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
Design Automation and Test in Europe Conference and Exhibition, ISSN 1530-1591
Keywords
machine learning, embedded systems
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-68988 (URN)10.23919/DATE58400.2024.10546824 (DOI)001253778900307 ()979-8-3503-4860-6 (ISBN)
Conference
27th Design, Automation and Test in Europe Conference and Exhibition (DATE), MAR 25-27, 2024, Valencia, SPAIN
2024-11-132024-11-132025-02-12Bibliographically approved