VEDLIoT: Next generation accelerated AIoT systems and applications
Paper in proceeding, 2023
Distributed Attestation and Security
Acceleration
Machine Learning (ML)
Artificial Intelligence of Things (AIoT)
Reconfigurable and Heterogeneous Computing
Author
Kevin Mika
Bielefeld University
René Griessl
Bielefeld University
Nils Kucza
Bielefeld University
Florian Porrmann
Bielefeld University
Martin Kaiser
Bielefeld University
Lennart Tigges
Bielefeld University
Jens Hagemeyer
Bielefeld University
Pedro Petersen Moura Trancoso
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Muhammad Waqar Azhar
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Fareed Mohammad Qararyah
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Stavroula Zouzoula
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Jämes Ménétrey
University of Neuchatel
Marcelo Pasin
University of Neuchatel
Pascal Felber
University of Neuchatel
Carina Marcus
Veoneer
Oliver Brunnegard
Veoneer
Olof Eriksson
Veoneer
Hans Salomonsson
Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd
Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023
291-296
979-8-4007-0140-5 (ISBN)
Bologna, Italy,
Very Efficient Deep Learning in IOT (VEDLIoT)
European Commission (EC) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.
Areas of Advance
Information and Communication Technology
Subject Categories
Embedded Systems
Computer Science
Computer Systems
DOI
10.1145/3587135.3592175