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1

Design of a photonic unitary neural network based on MZI arrays

Zhang Ye, Wang Ruiting, Zhang Yejin, Su Yanmei, Wang Pengfei, Luo Guangzhen, Zhou Xuliang, Pan Jiaoqing

Optica Applicata

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2024

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Vol. 54, nr 1

5--13

EN

In recent years, optical neural networks have attracted widespread attention, due to their advantages of high speed, high parallelism, high bandwidth, and low power consumption. Photonic unitary neural network is a kind of neural networks that utilize the principles of unitary matrices and photonics to perform computations. In this paper, we design a photonic unitary neural network based on Mach–Zehnder interferometer arrays. The results show that the network has a good performance on both triangular and circular binary classification datasets, where most of the data points are correctly classified. The accuracies achieve 97% and 95% for triangular and circular datasets, with the loss function values of 0.023 and 0.046, respectively.

2

A MZI-based optical neural network for image classification

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

Optica Applicata

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2024

|

Vol. 54, nr 1

97--104

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.

3

Noise quantization simulation analysis of optical convolutional networks

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

Optica Applicata

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2023

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Vol. 53, nr 3

483--493

EN

Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.