Aitia: Embedded AI techniques for industrial applications

Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review

Contributors

  • Marcelo Brandalero - , Brandenburg University of Technology (Author)
  • Mitko Veleski - , Brandenburg University of Technology (Author)
  • Hector Gerardo Munoz Hernandez - , Brandenburg University of Technology (Author)
  • Muhammad Ali - , Chair of Adaptive Dynamic Systems (Author)
  • Laurens Le Jeune - , KU Leuven (Author)
  • Toon Goedemé - , KU Leuven (Author)
  • Nele Mentens - , KU Leuven, Leiden University (Author)
  • Jurgen Vandendriessche - , Vrije Universiteit Brussel (Author)
  • Lancelot Lhoest - , Vrije Universiteit Brussel (Author)
  • Bruno da Silva - , Vrije Universiteit Brussel (Author)
  • Abdellah Touhafi - , Vrije Universiteit Brussel (Author)
  • Diana Goehringer - , Chair of Adaptive Dynamic Systems (Author)
  • Michael Hübner - , Brandenburg University of Technology (Author)

Abstract

Motivated by an increasing interest from startups in embedded Artificial Intelligence (AI) and by their limited expertise, the AITIA Project targets the development of embedded AI techniques for industrial applications. This extended abstract presents the motivation and the solutions being developed towards four use cases: smart sensors, network intrusion detection, driver-assistance systems, and Industry 4.0.

Details

Original languageEnglish
Title of host publicationProceedings - 2021 31st International Conference on Field-Programmable Logic and Applications, FPL 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-375
Number of pages2
ISBN (electronic)9781665437592
Publication statusPublished - 2021
Peer-reviewedYes

Conference

Title31st International Conference on Field-Programmable Logic and Applications, FPL 2021
Duration30 August - 3 September 2021
CityVirtual, Dresden
CountryGermany

External IDs

ORCID /0000-0003-2571-8441/work/159607521

Keywords

ASJC Scopus subject areas

Keywords

  • Artificial intelligence, Driver-assistance systems, Embedded systems, Industry 4.0, Machine learning, Network intrusion detection, Smart sensors