VEDLIoT: Next generation accelerated AIoT systems and applications
Paper in 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

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)

20th ACM International Conference on Computing Frontiers, CF 2023
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

More information

Latest update

1/3/2024 9