Optical artificial neural networks (ONNs)—analog computing hardware tailored for machine learning—have significant potential for achieving ultra‐high computing speed and energy efficiency. A new approach to architectures for ONNs based on integrated Kerr microcomb sources that is programmable, highly scalable, and capable of reaching ultra‐high speeds is proposed here. The building block of the ONN—a single neuron perceptron—is experimentally demonstrated that reaches a high single‐unit throughput speed of 11.9 Giga‐FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps, achieved by mapping synapses onto 49 wavelengths of a microcomb. The perceptron is tested 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 low wavelength spacing (49 GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, an approach to scaling the perceptron to a deep learning network is proposed using the same single microcomb 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 vehicles and aircraft tracking.

中文翻译：

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基于Kerr微梳的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）是专为机器学习量身定制的模拟计算硬件，具有实现超高计算速度和能源效率的巨大潜力。本文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。通过实验证明，通过将突触映射到微梳的49个波长上，ONN的构造块（单个神经元感知器）可以以每FLOP 8位达到11.9 Giga-FLOPS的高单单元吞吐速度，相当于95.2 Gbps。 。感知器在简单的标准基准数据集上进行了测试-手写数字识别和癌细胞检测-分别达到90％和85％以上的准确性。这种性能是相干集成微梳光源创纪录的低波长间隔（49 GHz）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，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，例如实时海量数据处理用于无人驾驶车辆和飞机跟踪。