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 microcombs. This approach is programmable and scalable and is capable of reaching ultrahigh 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 109 operations per second, or GigaOPS, 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.

中文翻译：

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带有Kerr微梳的Giga-OP / s速度的光子单感知器，用于可扩展的光学神经网络

光学人工神经网络（ONN）具有超高的计算速度和能源效率的巨大潜力。我们报告了一种使用集成的Kerr光学微梳的新颖方法。这种方法是可编程的和可扩展的，并且能够达到超高速。我们通过将突触映射到49个波长上以实现每秒11.9 x 109次操作或GigaOPS的操作速度（每次操作8位），相当于95.2吉比特/秒（Gbps），展示了基本的神经元ONN，即单个神经元感知器）。我们在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％以上的准确性。