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,

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

More information

Latest update

10/6/2023