Embed-AI: Towards hardware frugality

François BERRY,
Professor,
françois.berry@uca.fr

DESCRIPTION

The Embed-AI project is part of a broader effort to design artificial intelligence systems that are both resource-efficient and high-performing. It is built around two complementary innovations. On one side, it relies on a decentralized network of small smart cameras, or motes, equipped with low-resolution sensors and onboard analog neural processors. These devices collaborate to process data directly at the source, minimizing the need for data transmission. This approach helps preserve privacy, reduces overall energy consumption, and enhances the robustness of the system.

On the other side, to overcome the inherent computational and energy limitations of such lightweight devices, the project is developing a specialized analog ASIC chip. This chip is designed to optimize distributed neural network operations and can handle complex tasks, such as matrix–vector multiplications, with extremely low power consumption.

By bringing these two elements together, Embed-AI paves the way for a sustainable, decentralized, and scalable model of AI. The system makes it possible to envision robust edge computing applications that process large amounts of data locally while keeping material and energy requirements to a minimum.

ACTIVITIES

Each call for position will be posted in websites and distributed though mailing lists. Open positions will be advertised with clear project descriptions. Individuals from under-represented categories will be encouraged to apply. There will be a pre-selection based on the evaluation of CVs. The short-listed candidates will be interviewed by the appointing PI. After that, the PI will report to the RTB. Gender equality policies will be applied in case of candidates with very similar qualifications.

For this chair, 4 positions are opened: 3PhD thesis and 1 postdoc.

CHAIR PRESENTATION

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