Kuramoto-model-based data classification using the synchronization dynamics of uniform-mode spin Hall nano-oscillators

Abstract

Oscillator-based data-classification schemes have been proposed recently using the Kuramoto model, which tries to capture the synchronization behavior of coupled oscillators without considering the underlying physics of the oscillation and the coupling. In this paper, we propose the hardware implementation of a Kuramoto-model-based data-classification scheme through an array of dipole-coupled uniform-mode spin Hall nano-oscillators (SHNOs). Using micromagnetic simulations, which capture the underlying physics of operation of the SHNOs, we first study the variation of synchronization range between two uniform-mode SHNOs as a function of the physical distance between them. Thus we correlate the coupling constant in the Kuramoto model with the dipole-coupling strength between two SHNOs, which our micromagnetic simulation takes into account. Next, we generate the synchronization map for the two-input–two-output dipole-coupled uniform-mode SHNO system through micromagnetics and show that it matches with the one predicted by the Kuramoto model. Thus, we demonstrate here that the synchronization behavior of SHNOs obtained from micromagnetics-based modeling is consistent with that obtained from the Kuramoto model, which ignores the underlying physics of the SHNOs. This suggests that the Kuramoto-model-based data classification scheme can indeed be implemented physically on an array of SHNOs. To verify our claim, we show, through micromagnetic simulation, binary classification of data from a popular machine-learning data set (Fisher’s Iris data set) using an array of uniform-mode SHNOs.