Training and pattern recognition by an opto-magnetic neural network

Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and engineering, the realization of artificial neural networks, which com...

Full description

Saved in:
Bibliographic Details
Published in:Applied physics letters 2022-01, Vol.120 (2)
Main Authors: Chakravarty, A., Mentink, J. H., Semin, S., Rasing, Th
Format: Article
Language:English
Subjects:
Applied physics
Artificial neural networks
Energy dissipation
Magnetic materials
Neural networks
Neuromorphic computing
Optical communication
Pattern recognition
Synapses
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and engineering, the realization of artificial neural networks, which combine high operating speeds with fast and low-energy adaptability, remains a challenge. Here, we demonstrate an opto-magnetic neural network capable of learning and classification of digitized 3 × 3 characters exploiting local storage in the magnetic material. Using picosecond laser pulses, we find that micrometer sized synapses absorb well below 100 picojoule per synapse per laser pulse, with favorable scaling to smaller spatial dimensions. We, thus, managed to combine the speed and low-dissipation of optical networks with the low-energy adaptability and non-volatility of magnetism, providing a promising approach to fast and energy-efficient neuromorphic computing.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0073280