Photonic convolutional accelerator operating at Tera-OPs speeds for
neural networks with Kerr microcombs
Abstract
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 [1-7]. 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 Tera-OPS
(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.