Neuromorphic Computing Based on Wavelength-Division Multiplexing

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 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.

Automated summary

Optical neural networks (ONNs) based on wavelength-division multiplexing (WDM) techniques offer high bandwidth and analog architecture for enhanced computing power and energy efficiency. Integrated microcombs have been used to implement ONNs, with successful applications such as optical convolution accelerators for human image processing at 11 Tera operations per second. However, challenges and limitations of ONNs still need to be addressed for future applications.