Review
Version 1
Preserved in Portico This version is not peer-reviewed
Neuromorphic Computing Based on Wavelength-Division Multiplexing
Version 1
: Received: 11 June 2022 / Approved: 13 June 2022 / Online: 13 June 2022 (09:58:01 CEST)
How to cite: Moss, D. Neuromorphic Computing Based on Wavelength-Division Multiplexing. Preprints 2022, 2022060179. https://doi.org/10.20944/preprints202206.0179.v1 Moss, D. Neuromorphic Computing Based on Wavelength-Division Multiplexing. Preprints 2022, 2022060179. https://doi.org/10.20944/preprints202206.0179.v1
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.
Keywords
Optical neural networks; neuromorphic processor; microcomb; convolutional accelerator
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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