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VEDLIoT -- Next generation accelerated AIoT systems and applications

by Ménétrey, Jämes , Zierhoffer, Piotr , Zouzoula, Stavroula , Kaiser, Martin , Knauss, Eric , Kucza, Nils , Mika, Kevin , Casimiro, Antonio , Marcus, Carina , Tigges, Lennart , Latosinski, Grzegorz , Mao, Yufei , Felber, Pascal , Tassemeier, Marco , Porrmann, Florian , Hagemeyer, Jens , Hans-Martin Heyn , Salomonsson, Hans , Gugala, Karol , Bessani, Alysson , Pasin, Marcelo , Ödman, Daniel , Eriksson, Olof , Qararyah, Fareed , Carvalho, Tiago , Trancoso, Pedro , Meierhöfer, Franz , Brunnegard, Oliver , Porrmann, Mario , Griessl, René , Ask, Andreas , Muhammad Waqar Azhar

in Algorithms / Artificial intelligence / Computation / Energy efficiency / Hardware / Industrial applications / Machine learning / Smart buildings

2023

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in Algorithms / Artificial intelligence / Computation / Energy efficiency / Hardware / Industrial applications / Machine learning / Smart buildings

2023

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VEDLIoT -- Next generation accelerated AIoT systems and applications

in Algorithms / Artificial intelligence / Computation / Energy efficiency / Hardware / Industrial applications / Machine learning / Smart buildings

2023

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Paper

VEDLIoT -- Next generation accelerated AIoT systems and applications

Ménétrey, Jämes,

Zierhoffer, Piotr,

Zouzoula, Stavroula,

Kaiser, Martin,

Knauss, Eric,

Kucza, Nils,

Mika, Kevin,

Casimiro, Antonio,

Marcus, Carina,

Tigges, Lennart,

Latosinski, Grzegorz,

Mao, Yufei,

Felber, Pascal,

Tassemeier, Marco,

Porrmann, Florian,

Hagemeyer, Jens,

Hans-Martin Heyn,

Salomonsson, Hans,

Gugala, Karol,

Bessani, Alysson,

Pasin, Marcelo,

Ödman, Daniel,

Eriksson, Olof,

Qararyah, Fareed,

Carvalho, Tiago,

Trancoso, Pedro,

Meierhöfer, Franz,

Brunnegard, Oliver,

Porrmann, Mario,

Griessl, René,

Ask, Andreas,

Muhammad Waqar Azhar

2023

Overview

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.

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Publisher

Cornell University Library, arXiv.org

Subject

Algorithms

/ Artificial intelligence

/ Computation

/ Energy efficiency

/ Hardware

/ Industrial applications

/ Machine learning