Learning of classification tasks with an array of uniform-mode spin Hall nano-oscillators

Abstract

Recently, a system of spintronic vortex oscillators has been experimentally trained to classify vowel sounds. In this paper, we have carried out a combination of device-level and system-level simulations to train a system of spin Hall nano oscillators (SHNOs) of smaller size (25X lower in area compared to those vortex oscillators) for such data classification tasks. Magnetic moments precess in an uniform mode as opposed to the vortex mode in our oscillators. We have trained our system to classify inputs in various popular machine learning data sets like Fisher's Iris data set of flowers, Wisconsin Breast Cancer (WBC) data set, and MNIST data set of handwritten digits. We have employed a new technique for input dimensionality reduction here so that the clustering/target synchronization pattern changes based on the nature of the data in the different data sets. Our demonstration of learning in a system of such small SHNOs for a wide range of data sets is promising for scaling up the oscillator-based neuromorphic system for complex data classification tasks.