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MAG.NET

Financiado

Magnetic neural Network for predictive maintenance

Golana Computing is a new start-up company, spin-off from Spintec-CNRS, exploiting a recent scientific and technological breakthrough in the design and fabrication of bio-mimicking magnetic neurons. Breakthrough: Studying new mag... ver más

30/04/2026

Golana

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

GOLANA COMPUTING No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-03-30

HORIZON-EIC-2022-TRANSITIONCHALLENGES-01: EIC Transition Challenge: Green dig... Expected Impact:In the medium term, the expected outcome is the commercial emergence of new class of...

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Golana

2.50M€ | Lider

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Duración del proyecto: 37 meses Fecha Inicio: 2023-03-30
Fecha Fin: 2026-04-30

Líder del proyecto

GOLANA COMPUTING No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 2M€

Descripción del proyecto Golana Computing is a new start-up company, spin-off from Spintec-CNRS, exploiting a recent scientific and technological breakthrough in the design and fabrication of bio-mimicking magnetic neurons. Breakthrough: Studying new magnetization reversal schemes, we have inadvertently discovered that domain wall depinning from geometrical traps imitates in many regards the spiking of biological neurons. Based on this, we designed, fabricated and tested magnetic neurons able to complete bio-mimicking tasks. Our magnetic neural network performs speech recognition and speaker identification in real-time, without any prior feature extraction. The audio is simply transformed into spikes by a mechanism inspired from the mammalian ear. Our Goal is to develop a technology and fabricate a prototype that extends this unique ability to other types of analog signals, and apply it for predictive maintenance in the manufacturing industry. The present solutions based on mainstream artificial intelligence (AI) struggle, because the problems at hand are too fragmented: the training data is too scarce and the model engineering relies on very specific expert knowledge. Our Solution, frugal in terms of data and resources, based on a task-agnostic generic device, is able to identify unusual patterns in the analog signals. Our bio-mimicking approach should imitate the ability of human technicians, which assess the state of their machines by the sound. On the long term, our technology could be adapted for a variety of AI applications requiring low energy consumption or full privacy. The EIC Transition call corresponds exactly to our present needs: accelerate the development and the market readiness of our technology. Moreover, we address explicitly the requirements for Green Digital Devices. By working on the edge, our device reduces the energy and resources required for data transfer and, by imitating the biological neurons, it also reduces the energy required for the computation.

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