On-chip 4F-system based on concave mirrors for optical neural networks

Jun Dai; Xiaowen Dong; Chong Li; Jian-Jun He

doi:10.1117/12.2687410

27 November 2023 On-chip 4F-system based on concave mirrors for optical neural networks

Jun Dai, Xiaowen Dong, Chong Li, Jian-Jun He

Proceedings Volume 12768, Holography, Diffractive Optics, and Applications XIII; 1276826 (2023) https://doi.org/10.1117/12.2687410
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Optical neural networks (ONNs) have the potential for accelerating the inference of AI models, since ONNs have the advantage of high compute speed, and high parallelism. Integrated diffractive optical network for implementing parallel Fourier transforms has been proved efficient and is promising for large scale ONNs. We propose a novel on-chip Fourier transform implementation based on etched concave mirrors, enabling the construction of a photonic integrated 4F system to perform the convolution computation in the Convolutional Neural Networks (CNNs). One of the input vectors is encoded in a modulator array at the object plane, and the Fourier transform of the other vector is encoded in another modulator array at the spectrum plane. We simulated the computing process by the diffractive propagation of the optical field from the object plane to the image plane according to the Kirchhoff's diffraction formula. Finally, we used our simulation system to replace the traditional convolution layers in the electronic system to implement CNNs on three different datasets, Iris- Flower, MNIST and Fashion-MNIST, and obtained 96.67%, 95.6% and 89.4% classification accuracies, respectively, demonstrating comparable performance with the electronic counterpart.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Dai, Xiaowen Dong, Chong Li, and Jian-Jun He "On-chip 4F-system based on concave mirrors for optical neural networks", Proc. SPIE 12768, Holography, Diffractive Optics, and Applications XIII, 1276826 (27 November 2023); https://doi.org/10.1117/12.2687410

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