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Convolutional neural networks (CNNs), inspired by biological visual cortexsystems, are a powerful category of artificial neural networks that can extractthe hierarchical features of raw data to greatly reduce the network parametriccomplexity and enhance the predicting accuracy. They are of significantinterest for machine learning tasks such as computer vision, speechrecognition, playing board games and medical diagnosis. Optical neural networksoffer the promise of dramatically accelerating computing speed to overcome theinherent bandwidth bottleneck of electronics. Here, we demonstrate a universaloptical vector convolutional accelerator operating beyond 10 TeraOPS (TOPS:operations per second), generating convolutions of images of 250,000 pixelswith 8 bit resolution for 10 kernels simultaneously, enough for facial imagerecognition. We then use the same hardware to sequentially form a deep opticalCNN with ten output neurons, achieving successful recognition of full 10 digitswith 900 pixel handwritten digit images with 88% accuracy. Our results arebased on simultaneously interleaving temporal, wavelength and spatialdimensions enabled by an integrated microcomb source. This approach is scalableand trainable to much more complex networks for demanding applications such asunmanned vehicle and real time video recognition.

Optical neuromorphic processing at Tera-OP/s speeds based on Kerr soliton crystal microcombs