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1 August 2021 A complete, parallel, and autonomous photonic neural network in a semiconductor multimode laser
Anas Skalli, Javier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, Daniel Brunner
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180406 (2021) https://doi.org/10.1117/12.2594646
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
Artificial Neural Networks (ANNs) have become a staple computing technique. Their flexibility allows them to excel in a wide range of tasks and they benefit from highly parallelized architecture by design. We experimentally demonstrate a fully parallel photonic neural network using spatially distributed modes of a large-area vertical-cavity surface-emitting laser (LA-VCSEL). All components of the ANN are fully realized in parallel hardware. We train the readout weights to perform 2 and 3-bit header recognition, XOR classification, and digital to analog conversion, and obtain low error rates for all tasks. Our system uses readily available components, is scalable to much larger sizes and to bandwidths in excess of 20 GHz.
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Anas Skalli, Javier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, and Daniel Brunner "A complete, parallel, and autonomous photonic neural network in a semiconductor multimode laser", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180406 (1 August 2021); https://doi.org/10.1117/12.2594646
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KEYWORDS
Semiconductor lasers
Neural networks
Semiconductors
Data processing
Excel
Medical diagnostics
Micromirrors
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