Optical artificial neural networks (ONNs), analog computing hardware tailored for machine learning, have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN, a single neuron perceptron, by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets, handwritten-digit recognition and cancer-cell detection, achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

中文翻译：

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基于孤子晶体克尔微梳的单光子感知器，用于高速、可扩展的光学神经网络

光学人工神经网络 (ONN) 是为机器学习量身定制的模拟计算硬件，在超高计算速度和能源效率方面具有巨大潜力。我们提出了一种基于集成 Kerr 微梳源的 ONN 架构新方法，该方法可编程、高度可扩展且能够达到超高速。我们通过将突触映射到微梳的 49 个波长上，以每 FLOP 8 位实现 11.9 Giga-FLOPS 的高单单元吞吐量（对应于 95.2 Gbps），通过实验证明了 ONN（单个神经元感知器）的构建块。我们在简单的标准基准数据集、手写数字识别和癌细胞检测上测试感知器，分别达到 90% 和 85% 以上的准确率。这种性能是相干集成微梳源创纪录的小波长间隔 (49GHz) 的直接结果，这为神经形态光学带来了前所未有的波长数量。最后，我们提出了一种使用相同的单个微梳设备和标准的现成电信技术将感知器扩展到深度学习网络的方法，用于涉及全矩阵乘法的高吞吐量操作，例如实时大规模用于无人驾驶车辆和飞机跟踪的数据处理。