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4 October 2022 Computational metrics of an injection-locked large area semiconductor laser for neural network computing (Conference Presentation)
Anas Skalli, Xavier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, Daniel Brunner
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC1220408 (2022) https://doi.org/10.1117/12.2633381
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
Artificial neural networks (ANNs) have become a staple computing technique in many fields. Yet, they differ from classical computing hardware by taking a connectionist and parallel approach to computing and information processing. Here, we present a high performance, scalable, fully parallel, and autonomous PNN based on a large area vertical-cavity surface-emitting laser (LA-VCSEL). We implement 300+ hardware nodes and train the network to perform up to 6-bit header recognition, XOR classification and digital to analog conversion. Moreover, we investigate the impact of different physical parameters, namely, injection wavelength, injection power, and bias current on performance, and link these parameters to the general computational measures of consistency and dimensionality.
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Anas Skalli, Xavier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, and Daniel Brunner "Computational metrics of an injection-locked large area semiconductor laser for neural network computing (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC1220408 (4 October 2022); https://doi.org/10.1117/12.2633381
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KEYWORDS
Neural networks
Semiconductor lasers
Computer networks
Complex systems
Vertical cavity surface emitting lasers
Analog electronics
Artificial neural networks
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