second photonic convolutional accelerator for deep learning optical
neural networks
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-FLOPS
(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.