Please use this identifier to cite or link to this item:
Title: Space-efficient optical computing with an integrated chip diffractive neural network
Authors: Zhu, Hanhan
Zou, Jun
Zhang, Hengyi
Shi, Yuzhi
Luo, Shibo
Wang, N.
Cai, H.
Wan, Liangxia
Wang, Bo
Jiang, Xudong
Thompson, Jayne
Luo, Xianshu
Zhou, Xuanhe
Xiao, Limin
Huang, W.
Patrick, Lento
Gu, Mile
Kwek, Leong Chuan
Liu, Ai Qun
Keywords: Science::Physics
Issue Date: 2022
Source: Zhu, H., Zou, J., Zhang, H., Shi, Y., Luo, S., Wang, N., Cai, H., Wan, L., Wang, B., Jiang, X., Thompson, J., Luo, X., Zhou, X., Xiao, L., Huang, W., Patrick, L., Gu, M., Kwek, L. C. & Liu, A. Q. (2022). Space-efficient optical computing with an integrated chip diffractive neural network. Nature Communications, 13(1), 1044-.
Project: NRFCRP13-2014-01
Journal: Nature Communications 
Abstract: Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimental implementations need N2 units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.
ISSN: 2041-1723
DOI: 10.1038/s41467-022-28702-0
Schools: School of Physical and Mathematical Sciences 
Research Centres: Quantum Science and Engineering Centre
Quantum Hub
Rights: © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

Files in This Item:
File Description SizeFormat 
Space-efficient optical computing with an integrated chip diffractive neural network.pdf1.4 MBAdobe PDFThumbnail

Citations 10

Updated on Sep 16, 2023

Web of ScienceTM
Citations 10

Updated on Sep 18, 2023

Page view(s)

Updated on Sep 22, 2023

Download(s) 50

Updated on Sep 22, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.