Metaplastic Spintronics Synapses

Introduction

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.

Neuromorphic Hardware Development

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.

Mechanism of Synaptic States

The synaptic states will be given by:

1. The two magnetisation orientations in ferromagnets with perpendicular magnetic anisotropy.
2. 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.

Modulation of Switching Probability

The magneto-ionic hidden states will translate into a modulation of the switching probability between synaptic states, introducing the metaplasticity functionality.

Learning Schemes Development

In parallel, we will develop artificial neural networks (ANNs) learning schemes, adapted to our device physics and inspired by biological synaptic activity, that can learn with mitigated catastrophic forgetting.

Project Goals

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.