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

• URL: http://arxiv.org/abs/2307.09262v1
• Date: Tue, 18 Jul 2023 13:47:57 GMT
• Title: Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach
• Authors: Anatole Moureaux, Simon de Wergifosse, Chlo\'e Chopin and Flavio Abreu Araujo
• Abstract summary: We develop a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.

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