A MZI-based optical neural network for image classification

• Autorzy: Zhang Ye , Zhang Saining , Zhang Danni , Wang Ruiting , Su Yanmei , Yi Junkai , Wang Pengfei , Luo Guangzhen , Zhou Xuliang , Pan Jiaoqing

Identyfikatory

DOI

10.37190/oa240108

• Języki publikacji: EN

Abstrakty

EN

In recent years, with the expansion of information, artificial intelligence technology has been developed and used in various fields. Among them, optical neural network provides a new type of special neural network accelerator chip solution, which has the advantages of high speed, high bandwidth, and low power consumption. In this paper, we construct an optical neural network based on Mach–Zehnder interferometer. The experimental results on the image classification of MNIST handwritten digitals show that the optical neural network has high accuracy, fast convergence and good scalability.

Słowa kluczowe

EN

optical neural network; image classification; Mach-Zehnder interferometer

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2024
• Tom: Vol. 54, nr 1
• Strony: 97--104
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Zhang Ye

• School of Automation, Beijing Information Science and Technology University, Beijing, China

autor

Zhang Saining

• School of Computer Science Technology, Beijing Institute of Technology, Beijing, China

autor

Zhang Danni

• School of Automation, Beijing Information Science and Technology University, Beijing, China
• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

autor

Wang Ruiting

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Su Yanmei

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

autor

Yi Junkai

• School of Automation, Beijing Information Science and Technology University, Beijing, China

autor

Wang Pengfei

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Luo Guangzhen

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Zhou Xuliang

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

autor

Pan Jiaoqing

• Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
• College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).