Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, a...

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Autores principales:	Hoda Sadeghzadeh, Somayyeh Koohi, Ali Fele Paranj
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Optical computing optical nonlinearity optical pooling photonic neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/08b80d65cf98449796c0da259028ecc4
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id	oai:doaj.org-article:08b80d65cf98449796c0da259028ecc4
record_format	dspace
spelling	oai:doaj.org-article:08b80d65cf98449796c0da259028ecc42021-11-09T00:00:44ZFree-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations2169-353610.1109/ACCESS.2021.3123230https://doaj.org/article/08b80d65cf98449796c0da259028ecc42021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585470/https://doaj.org/toc/2169-3536Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, as well as two various optical pooling operations, namely optical average pooling and optical motion pooling, by means of 4f optical correlators. Proposing these optical building blocks not only speed up the neural networks due to negligible optical processing latency, but also facilitate the concatenation of optical convolutional layers with no optoelectrical conversions in-between, as the significant bottlenecks of implementing photonic CNNs. Furthermore, the proposed optical motion pooling layer increases the translation invariance property of CNNs, avoiding the inclusion of all corresponding translated images for the training procedure, and hence, increases the training speed of the neural network. The classification accuracy of the proposed optical convolutional layer is evaluated as the first layer of a customized version of AlexNet architecture, named as OP-AlexNet, for classification of Kaggle Cats and Dog challenge, CIFAR-10, and MNIST datasets, as 83.76%, 72.82%, and 99.25%, respectively, by using optical average pooling.Hoda SadeghzadehSomayyeh KoohiAli Fele ParanjIEEEarticleOptical computingoptical nonlinearityoptical poolingphotonic neural networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146533-146549 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Optical computing optical nonlinearity optical pooling photonic neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Optical computing optical nonlinearity optical pooling photonic neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hoda Sadeghzadeh Somayyeh Koohi Ali Fele Paranj Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
description	Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, as well as two various optical pooling operations, namely optical average pooling and optical motion pooling, by means of 4f optical correlators. Proposing these optical building blocks not only speed up the neural networks due to negligible optical processing latency, but also facilitate the concatenation of optical convolutional layers with no optoelectrical conversions in-between, as the significant bottlenecks of implementing photonic CNNs. Furthermore, the proposed optical motion pooling layer increases the translation invariance property of CNNs, avoiding the inclusion of all corresponding translated images for the training procedure, and hence, increases the training speed of the neural network. The classification accuracy of the proposed optical convolutional layer is evaluated as the first layer of a customized version of AlexNet architecture, named as OP-AlexNet, for classification of Kaggle Cats and Dog challenge, CIFAR-10, and MNIST datasets, as 83.76%, 72.82%, and 99.25%, respectively, by using optical average pooling.
format	article
author	Hoda Sadeghzadeh Somayyeh Koohi Ali Fele Paranj
author_facet	Hoda Sadeghzadeh Somayyeh Koohi Ali Fele Paranj
author_sort	Hoda Sadeghzadeh
title	Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
title_short	Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
title_full	Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
title_fullStr	Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
title_full_unstemmed	Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
title_sort	free-space optical neural network based on optical nonlinearity and pooling operations
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/08b80d65cf98449796c0da259028ecc4
work_keys_str_mv	AT hodasadeghzadeh freespaceopticalneuralnetworkbasedonopticalnonlinearityandpoolingoperations AT somayyehkoohi freespaceopticalneuralnetworkbasedonopticalnonlinearityandpoolingoperations AT alifeleparanj freespaceopticalneuralnetworkbasedonopticalnonlinearityandpoolingoperations
_version_	1718441358131200000

Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

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