Abstract
We report ultrahigh bandwidth applications of Kerr microcombs at data
rates beyond 10 Terabits/s. Optical neural networks can dramatically
accelerate the computing speed to overcome the inherent bandwidth
bottleneck of electronics. At the same time, digital signal processing
has become central to many fields, from coherent optical
telecommunications where it is used to compensate signal impairments, to
image processing, important for observational astronomy, medical
diagnosis, autonomous driving, big data and particularly artificial
intelligence. Digital signal processing had traditionally been performed
electronically, but new applications, particularly those involving real
time video image processing, are creating unprecedented demand for
ultrahigh performance, including bandwidth and reduced energy
consumption. We use a new and powerful class of micro-comb called
soliton crystals that exhibit robust operation and stable generation as
well as a high intrinsic efficiency with a low spacing of 48.9 GHz. We
demonstrate a universal optical vector convolutional accelerator
operating at 11 Tera-OPS/s (TOPS) on 250,000 pixel images for 10 kernels
simultaneously — enough for facial image recognition. We 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. Finally, we demonstrate a photonic
digital signal processor operating at 18 Tb/s and use it to process
multiple simultaneous video signals in real-time. The system processes
400,000 video signals concurrently, performing 34 functions
simultaneously that are key to object edge detection, edge enhancement
and motion blur. As compared with spatial-light devices used for image
processing, our system is not only ultra-high speed but highly
reconfigurable and programable, able to perform many different functions
without any change to the physical hardware. Our approach, based on an
integrated Kerr soliton crystal microcomb, opens up new avenues for
ultrafast robotic vision and machine learning.