Abstract
Optical neural networks (ONNs), or optical neuromorphic hardware
accelerators, have the potential to dramatically enhance the computing
power and energy efficiency of mainstream electronic processors, due to
their ultra-large bandwidths of up to 10’s of terahertz together with
their analog architecture that avoids the need for reading and writing
data back-and-forth. Different multiplexing techniques have been
demonstrated to demonstrate ONNs, amongst which wavelength-division
multiplexing (WDM) techniques make sufficient use of the unique
advantages of optics in terms of broad bandwidths. Here, we review
recent advances in WDM-based ONNs, focusing on methods that use
integrated microcombs to implement ONNs. We present results for human
image processing using an optical convolution accelerator operating at
11 Tera operations per second. The open challenges and limitations of
ONNs that need to be addressed for future applications are also
discussed.