  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.
