VEDLIoT: Very Efficient Deep Learning in IoT
Paper in proceeding, 2022
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
Author
Martin Kaiser
Bielefeld University
R. Griessl
Bielefeld University
Nils Kucza
Bielefeld University
C. Haumann
Bielefeld University
L. Tigges
Bielefeld University
K. Mika
Bielefeld University
Jens Hagemeyer
Bielefeld University
F. Porrmann
Bielefeld University
U. Ruckert
Bielefeld University
Micha Vor Dem Berge
Christmann Informationstechnik + Medien
Stefan Krupop
Christmann Informationstechnik + Medien
Mario Porrmann
Osnabrück University
M. Tassemeier
Osnabrück University
Pedro Petersen Moura Trancoso
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
F. Qararyah
Stavroula Zouzoula
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
A.C. Casimiro
University of Lisbon
A. Bessani
University of Lisbon
J. Cecilio
University of Lisbon
S. Andersson
Veoneer
Oliver Brunnegård
Veoneer
O. Eriksson
Veoneer
R. Weiss
Siemens
F. McIerhofer
Siemens
Hans Salomonsson
EmbeDL AB
E. Malekzadeh
EmbeDL AB
D. Odman
EmbeDL AB
A. Khurshid
RISE Research Institutes of Sweden
Pascal Felber
University of Neuchatel
Marcelo Pasin
University of Neuchatel
Valerio Schiavoni
University of Neuchatel
J. Menetrey
Antmicro
K. Gugala
Antmicro
P. Zierhoffer
Antmicro
Eric Knauss
University of Gothenburg
Hans Martin Heyn
University of Gothenburg
Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022
963-968
9783981926361 (ISBN)
2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022
Virtual, Online, Belgium,
Virtual, Online, Belgium,
Very Efficient Deep Learning in IOT (VEDLIoT)
European Commission (EC) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.
Subject Categories
Embedded Systems
Computer Science
Computer Systems
DOI
10.23919/DATE54114.2022.9774653