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- W3163292767 abstract "Abstract Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 10 9 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (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." @default.
- W3163292767 created "2021-05-24" @default.
- W3163292767 creator A5015900507 @default.
- W3163292767 creator A5090231085 @default.
- W3163292767 creator A5091314474 @default.
- W3163292767 date "2021-04-23" @default.
- W3163292767 modified "2023-09-25" @default.
- W3163292767 title "Photonic perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks" @default.
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- W3163292767 doi "https://doi.org/10.21203/rs.3.rs-453033/v1" @default.
- W3163292767 hasPublicationYear "2021" @default.
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