Space-efficient optical computing with an integrated chip diffractive neural network

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...

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Main 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
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160409
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1604092023-02-28T20:01:23Z Space-efficient optical computing with an integrated chip diffractive neural network 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 School of Physical and Mathematical Sciences Quantum Science and Engineering Centre Quantum Hub Science::Physics Artificial Intelligence Computers 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was supported by the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13-2014-01) and the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001). 2022-07-21T06:09:30Z 2022-07-21T06:09:30Z 2022 Journal Article 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-. https://dx.doi.org/10.1038/s41467-022-28702-0 2041-1723 https://hdl.handle.net/10356/160409 10.1038/s41467-022-28702-0 35210432 2-s2.0-85125348441 1 13 1044 en NRFCRP13-2014-01 MOE2017-T3-1-001 Nature Communications © 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 http://creativecommons.org/ licenses/by/4.0/. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Physics
Artificial Intelligence
Computers
spellingShingle Science::Physics
Artificial Intelligence
Computers
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
Space-efficient optical computing with an integrated chip diffractive neural network
description 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.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
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
format Article
author 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
author_sort Zhu, Hanhan
title Space-efficient optical computing with an integrated chip diffractive neural network
title_short Space-efficient optical computing with an integrated chip diffractive neural network
title_full Space-efficient optical computing with an integrated chip diffractive neural network
title_fullStr Space-efficient optical computing with an integrated chip diffractive neural network
title_full_unstemmed Space-efficient optical computing with an integrated chip diffractive neural network
title_sort space-efficient optical computing with an integrated chip diffractive neural network
publishDate 2022
url https://hdl.handle.net/10356/160409
_version_ 1759855610410565632