  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 TeraFLOPS
(floating point 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.
