Pattern recognition using spiking antiferromagnetic neurons

TL;DR

This paper demonstrates a nanoscale, energy-efficient neural network using antiferromagnetic spintronic neurons trained with a spike timing algorithm to recognize patterns rapidly and accurately.

Contribution

It introduces a novel AFM neuron-based neural network trained with the SPAN algorithm for fast, high-accuracy pattern recognition at ultra-low power.

Findings

Achieved microsecond training time for pattern recognition.

Enabled multi-symbol recognition with added output layer.

Power consumption in the picojoule range.

Abstract

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.