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Metaplastic Spintronics Synapses

Project description

Magneto-ionic metaplastic synapses

Artificial neural networks are algorithms designed to mimic the human brain and recognise patterns. Despite their great potential, current artificial neural networks are suffering a massive shortcoming: unlike humans, they are unable to learn multiple tasks sequentially. Funded by the European Innovation Council, the METASPIN project aims to develop radically new low-power artificial synapse technology based on spintronics and ionics that will prevent catastrophic forgetting. The envisioned neuromorphic hardware will use voltage-controlled ionics to control magnetic properties in nanodevices, known as magneto-ionics, to support artificial synaptic metaplasticity. Inspired by the human brain, the proposed concept assigns a hidden value to artificial synapse states to encode their importance. This will render it easier or harder to reconfigure the synaptic state upon learning a new task, giving a hierarchy to previously learned information, thereby preventing catastrophic forgetting.

Objective

In METASPIN we envision a radically new low-power artificial synapse technology based on spintronics nanodevices that will prevent catastrophic forgetting, i.e. the loss of memory of previously learned tasks upon learning new ones, a major flaw currently faced by all artificial intelligence applications.
We will develop a new class of neuromorphic hardware that will use magneto-ionics to support synaptic metaplasticity, i.e. a feature inspired by the human brain based on assigning a hidden value to the states of artificial synapses to encode how important each state is. This will make it easier or harder to reconfigure the synaptic state upon learning a new task, giving a hierarchy to previously learned information and thus preventing catastrophic forgetting. The synaptic states will be given by the two magnetisation orientations in ferromagnets with perpendicular magnetic anisotropy, and by ferro/antiferromagnetic order in materials where the two phases coexist. In all cases, magneto-ionic gating will be used to locally modulate intrinsic magnetic properties to assign hidden states to each synaptic state. The magneto-ionic hidden states will translate into a modulation of the switching probability between synaptic states, introducing the metaplasticity functionality. In parallel, we will develop ANNs learning schemes, adapted to our device physics and inspired by biological synaptic activity, that can learn with mitigated catastrophic forgetting. The ultimate goal of this project is to integrate this advanced synaptic technology and learning algorithms into an ANN demonstrator to test multitask learning on proof-of-concept tasks inspired by medical AI, and assess the impact of metaplasticity in catastrophic forgetting.

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HORIZON-EIC - HORIZON EIC Grants

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Call for proposal

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(opens in new window) HORIZON-EIC-2022-PATHFINDEROPEN-01

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Coordinator

UNIVERSITE PARIS-SACLAY
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 620 145,00
Address
BATIMENT BREGUET - 3 RUE JOLIOT CURIE
91190 GIF-SUR-YVETTE
France

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Region
Ile-de-France Ile-de-France Essonne
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 789 750,00

Participants (7)

Partners (1)

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