Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This stud...

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Main Authors: Toshihiro Sakurai, Tomoyoshi Ito, Tomoyoshi Shimobaba
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Photonics
Subjects:
Online Access:https://www.mdpi.com/2304-6732/11/2/145
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author Toshihiro Sakurai
Tomoyoshi Ito
Tomoyoshi Shimobaba
author_facet Toshihiro Sakurai
Tomoyoshi Ito
Tomoyoshi Shimobaba
author_sort Toshihiro Sakurai
collection DOAJ
description Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.
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spelling doaj.art-bff37e8fcb354e8d8fdb419c14015d612024-02-23T15:31:39ZengMDPI AGPhotonics2304-67322024-02-0111214510.3390/photonics11020145Diffractive Deep-Neural-Network-Based Classifier for Holographic MemoryToshihiro Sakurai0Tomoyoshi Ito1Tomoyoshi Shimobaba2Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, JapanGraduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, JapanGraduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, JapanHolographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.https://www.mdpi.com/2304-6732/11/2/145holographic memoryholographic data storageneural networkdiffractive neural network
spellingShingle Toshihiro Sakurai
Tomoyoshi Ito
Tomoyoshi Shimobaba
Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
Photonics
holographic memory
holographic data storage
neural network
diffractive neural network
title Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
title_full Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
title_fullStr Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
title_full_unstemmed Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
title_short Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
title_sort diffractive deep neural network based classifier for holographic memory
topic holographic memory
holographic data storage
neural network
diffractive neural network
url https://www.mdpi.com/2304-6732/11/2/145
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