Spintronics for image recognition: performance benchmarking via ultrafast data-driven simulations

no code implementations • 10 Aug 2023 • Anatole Moureaux, Chloé Chopin, Simon de Wergifosse, Laurent Jacques, Flavio Abreu Araujo

We present a demonstration of image classification using an echo-state network (ESN) relying on a single simulated spintronic nanostructure known as the vortex-based spin-torque oscillator (STVO) delayed in time.

Benchmarking Classification +1

Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach

no code implementations • 18 Jul 2023 • Anatole Moureaux, Simon de Wergifosse, Chloé Chopin, Flavio Abreu Araujo

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS).

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

1 code implementation • 23 Jul 2021 • Xing Chen, Flavio Abreu Araujo, Mathieu Riou, Jacob Torrejon, Dafiné Ravelosona, Wang Kang, Weisheng Zhao, Julie Grollier, Damien Querlioz

Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them.