Neuromorphic computing using wavelength-division multiplexing

TL;DR

This paper reviews recent advances in wavelength-division multiplexing optical neural networks, highlighting integrated microcombs and demonstrating high-speed image processing, while discussing current challenges and future prospects.

Contribution

It provides a comprehensive review of WDM-based ONNs with integrated microcombs and presents experimental results on high-speed optical convolution for image processing.

Findings

Achieved 11 Tera operations per second in optical convolution accelerators.

Demonstrated the use of integrated microcombs in WDM-based ONNs.

Discussed open challenges for future development of optical neural networks.

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 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.