用于高速、可扩展光学神经网络的孤子晶体Kerr微梳

Xingyuan Xu, M. Tan, D. Moss
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引用次数: 0

摘要

光学人工神经网络(ONNs)在超高计算速度和能源效率方面具有巨大的潜力。我们报告了一种基于集成Kerr微梳的ONN新方法,该方法是可编程的,高度可扩展的,能够达到超高速,展示了ONN的构建模块,一个单神经元感知器,通过将突触映射到49个波长,以每OP 8位或95.2 Gbps的速度实现11.9千兆ops的单单元吞吐量。我们在手写数字识别和癌细胞检测上测试了感知器,分别达到了90%和85%以上的准确率。通过使用现成的电信技术将感知器扩展到深度学习网络,我们可以实现矩阵乘法的高吞吐量操作,用于实时大规模数据处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s
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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