2023/08/17 by Hannah Bradley, Steven Louis, Bradley, Hannah +5 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2308.09071
openalex publication_date 2023/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.