Very Efficient Deep Learning in IoT

Teaching the IoT to learn

About

Vedliot

As the Internet of Things (IoT) continues to take shape, promising widespread automation and data exchange, one of the biggest challenges is to act on the data generated. The amount of data collected is huge, the computational power required for processing is high, and the algorithms are complex. The EU-funded VEDLIoT project develops an IoT platform that uses deep learning algorithms distributed throughout the IoT continuum. The proposed new platform with innovative IoT architecture is expected to bring significant benefits to a large number of applications, including industrial robots, self-driving cars, and smart homes. The project offers an Open Call at project midterm, incorporating additional VEDLIoT-related industrial use-cases in the project, increasing the market readiness of the VEDLIoT solutions.

USE

CASES

Industrial

The industrial use case employs DL-based solutions for motor condition monitoring and for arc detection, involving different challenges concerning the usage of DL models. In the former case it is necessary to comply with a ultra-low energy budget, and in the latter it is necessary to ensure a very low false-negative error rate.

Automotive

​The automotive use-case focuses on increasing the processing efficiency DL tasks over the resources that are present in the traffic environment. This will be achieved through distribution of the processing tasks over resources such as the ego vehicle, cellular base station(s) in the close proximity,  as well as the cloud.

Home

In the home domain, VEDLIoT will consider a virtual mirror application in which it will be necessary to deploy the software and execute DNN models on different kinds of hardware, namely at the edge (t.RECS) and on a specialized embedded platform (µ.RECS).

NEWS

VEDLIoT article in HiPEACinfo71

VEDLIoT article in HiPEACinfo71

See our new article “Next-generation accelerated IoT” in HIPEACinfo71 (January 2024), the latest issue of HiPEAC magazine, under the theme "Distributed computing and Edge AI special". In this article, the VEDLIoT coordinators Carola Haumann and Jens Hagemeyer (UNIBI),...

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VEDLIoT at HiPEAC 2024, January 17-19

VEDLIoT at HiPEAC 2024, January 17-19

We are two weeks away from HiPEAC 2024 and VEDLIoT is taking part. We’ll have a booth (room Pluto) and we’re organising the 3rd edition of the Deep Learning for IoT (DL4IoT) workshop, which will take place on 19 January, from 10:00 to 13:00, in Munich (Germany). The...

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Jämes Ménétrey nomination by the Bytecode Alliance

Jämes Ménétrey nomination by the Bytecode Alliance

We end 2023 with great news! The Bytecode Alliance is nominating Jämes Ménétrey (UNINE) as a Recognised Contributor. The Bytecode Alliance is a group of industry-leading companies that collaborate to create new software foundations, building on standards such as...

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EVENTS

DL4IoT Workshop at HiPEAC 2024

DL4IoT Workshop at HiPEAC 2024

We ended VEDLIoT participation at HiPEAC 2024 on a high note with the DL4IoT workshop! Attendance at the workshop was very positive and the presentations were of high quality. We had descriptions of ongoing work on advanced machine learning techniques useful in a wide...

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Paper presentation at OPODIS’23

Paper presentation at OPODIS’23

Jämes Ménétrey (UNINE) presented the paper "A Holistic Approach for Trustworthy Distributed Systems with WebAssembly and TEEs" at the 27th Conference on Principicles of Distributed Systems (OPODIS’23) in Tokyo (Japan), from 6 to 8 December. This paper is co-authored...

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