Pattern recognition using spiking antiferromagnetic neurons
- URL: http://arxiv.org/abs/2308.09071v2
- Date: Mon, 4 Mar 2024 17:29:17 GMT
- Title: Pattern recognition using spiking antiferromagnetic neurons
- Authors: Hannah Bradley (1), Steven Louis (2), Andrei Slavin (1), and Vasyl
Tyberkevych (1) ((1) Department of Physics, Oakland University, (2)
Department of Electrical Engineering, Oakland University)
- Abstract summary: 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.
- Score: 1.0243212430977688
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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