Xu, X.; Tan, M.; Moss, D. Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks. Preprints2020, 2020110233. https://doi.org/10.20944/preprints202011.0233.v1
APA Style
Xu, X., Tan, M., & Moss, D. (2020). Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks. Preprints. https://doi.org/10.20944/preprints202011.0233.v1
Chicago/Turabian Style
Xu, X., Mengxi Tan and David Moss. 2020 "Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks" Preprints. https://doi.org/10.20944/preprints202011.0233.v1
Abstract
Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
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