  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
ultralarge bandwidths of up to 10s of terahertz together with their analog
architecture that avoids the need for reading and writing data back and forth.
Different multiplexing techniques have been employed 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.
