  Convolutional neural networks (CNNs), inspired by biological visual cortex
systems, are a powerful category of artificial neural networks that can extract
the hierarchical features of raw data to greatly reduce the network parametric
complexity and enhance the predicting accuracy. They are of significant
interest for machine learning tasks such as computer vision, speech
recognition, playing board games and medical diagnosis. Optical neural networks
offer the promise of dramatically accelerating computing speed to overcome the
inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal
optical vector convolutional accelerator operating beyond 10 TeraOPS (TOPS:
operations per second), generating convolutions of images of 250,000 pixels
with 8 bit resolution for 10 kernels simultaneously, enough for facial image
recognition. We then use the same hardware to sequentially form a deep optical
CNN with ten output neurons, achieving successful recognition of full 10 digits
with 900 pixel handwritten digit images with 88% accuracy. Our results are
based on simultaneously interleaving temporal, wavelength and spatial
dimensions enabled by an integrated microcomb source. This approach is scalable
and trainable to much more complex networks for demanding applications such as
unmanned vehicle and real time video recognition.
