Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach
This work addresses the computational cost challenge in designing STVO-based neuromorphic computing devices, offering a promising incremental improvement for the field of neuromorphic spintronics.
The study tackled the problem of simulating spin-torque vortex nano-oscillators (STVOs) by developing an analytical model combining the Thiele equation approach with micromagnetic simulation data, resulting in a 9 orders of magnitude acceleration in simulations while maintaining accuracy.
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.