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.
Training and pattern recognition by an opto-magnetic neural network
Note: This paper is part of the APL Special Collection on Neuromorphic Computing: From Quantum Materials to Emergent Connectivity.
A. Chakravarty, J. H. Mentink, S. Semin, Th. Rasing; Training and pattern recognition by an opto-magnetic neural network. Appl. Phys. Lett. 10 January 2022; 120 (2): 022403. https://doi.org/10.1063/5.0073280
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