VEDLIoT: Next generation accelerated AIoT systems and applications
Paper i proceeding, 2023

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

Distributed Attestation and Security

Acceleration

Machine Learning (ML)

Artificial Intelligence of Things (AIoT)

Reconfigurable and Heterogeneous Computing

Författare

Kevin Mika

Universität Bielefeld

René Griessl

Universität Bielefeld

Nils Kucza

Universität Bielefeld

Florian Porrmann

Universität Bielefeld

Martin Kaiser

Universität Bielefeld

Lennart Tigges

Universität Bielefeld

Jens Hagemeyer

Universität Bielefeld

Pedro Petersen Moura Trancoso

Chalmers, Data- och informationsteknik, Datorteknik

Muhammad Waqar Azhar

Chalmers, Data- och informationsteknik, Datorteknik

Fareed Mohammad Qararyah

Chalmers, Data- och informationsteknik, Datorteknik

Stavroula Zouzoula

Chalmers, Data- och informationsteknik, Datorteknik

Jämes Ménétrey

Université de Neuchâtel

Marcelo Pasin

Université de Neuchâtel

Pascal Felber

Université de Neuchâtel

Carina Marcus

Veoneer

Oliver Brunnegard

Veoneer

Olof Eriksson

Veoneer

Hans Salomonsson

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd

Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023

291-296
979-8-4007-0140-5 (ISBN)

20th ACM International Conference on Computing Frontiers, CF 2023
Bologna, Italy,

Very Efficient Deep Learning in IOT (VEDLIoT)

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

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1145/3587135.3592175

Mer information

Senast uppdaterat

2024-01-03