Miniature on-chip diffractive optical neural network design

Fu, Tingzhao; Liu, Wencan; Huang, Yuyao; Sun, Run Cang; Huang, Honghao; Yang, Sigang; Chen, Hongwei

doi:10.1364/cleo_at.2023.jw2a.135

Cleo 2023 2023

DOI: 10.1364/cleo_at.2023.jw2a.135

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Miniature on-chip diffractive optical neural network design

Tingzhao Fu

Wencan Liu

Yuyao Huang

et al.

Abstract: A miniature on-chip diffractive optical neural network (DONN) with a footprint of 1.35 × 10 – 3mm2 is designed through the particle swarm optimization (PSO) algorithm, yielding an accuracy of 93.3% in simulation on the Iris plants dataset.

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Optical neural networks: progress and challenges

Fu,

Zhang,

Sun

et al. 2024

Light Sci Appl

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Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.

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Optical neural networks: progress and challenges

Fu,

Zhang,

Sun

et al. 2024

Light Sci Appl

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show abstract

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Miniature on-chip diffractive optical neural network design

Abstract: A miniature on-chip diffractive optical neural network (DONN) with a footprint of 1.35 × 10 – 3mm2 is designed through the particle swarm optimization (PSO) algorithm, yielding an accuracy of 93.3% in simulation on the Iris plants dataset.

Cited by 1 publication

References 4 publications

Optical neural networks: progress and challenges

Optical neural networks: progress and challenges

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