VEDLIoT: Very Efficient Deep Learning in IoT
Paper i 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.

Författare

Martin Kaiser

Universität Bielefeld

R. Griessl

Universität Bielefeld

Nils Kucza

Universität Bielefeld

C. Haumann

Universität Bielefeld

L. Tigges

Universität Bielefeld

K. Mika

Universität Bielefeld

Jens Hagemeyer

Universität Bielefeld

F. Porrmann

Universität Bielefeld

U. Ruckert

Universität Bielefeld

Micha Vor Dem Berge

Christmann Informationstechnik + Medien

Stefan Krupop

Christmann Informationstechnik + Medien

Mario Porrmann

Universität Osnabrück

M. Tassemeier

Universität Osnabrück

Pedro Petersen Moura Trancoso

Chalmers, Data- och informationsteknik, Datorteknik

F. Qararyah

Stavroula Zouzoula

Chalmers, Data- och informationsteknik, Datorteknik

A.C. Casimiro

Universidade de Lisboa

A. Bessani

Universidade de Lisboa

J. Cecilio

Universidade de Lisboa

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

Université de Neuchâtel

Marcelo Pasin

Université de Neuchâtel

Valerio Schiavoni

Université de Neuchâtel

J. Menetrey

Antmicro

K. Gugala

Antmicro

P. Zierhoffer

Antmicro

Eric Knauss

Göteborgs universitet

Hans Martin Heyn

Göteborgs universitet

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)

Europeiska kommissionen (EU) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.23919/DATE54114.2022.9774653

Mer information

Senast uppdaterat

2023-10-06