Mr. Jianmin Xiong Profile

Jianmin Xiong

at Ocean College Zhejiang Univ

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Proceedings Article | 24 November 2021 Paper

Advances and progress of diffractive deep neural networks

Jianmin Xiong, Zejun Zhang, Jing Xu

Proceedings Volume 12069, 120690V (2021) https://doi.org/10.1117/12.2606596

KEYWORDS: Diffraction, Neural networks, Light wave propagation, Free space optics, Optical computing, Machine learning

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In recent years, artificial intelligence has achieved unprecedented development, and deep learning, represented by neural networks, plays an important role. After the emergence of large-scale pre-trained models with trillions of parameters, the model performance is significantly improved while the burden of computational resources and energy consumption of hardware devices are also increased simultaneously, thus limiting its application in more practical scenarios. Compared with neural networks implemented based on electronic devices, those implemented based on optical devices are called optical neural networks, which have unique properties to overcome the dilemma above. One of the most representative works of optical neural networks these years is the diffractive deep neural network (D²NN). In this paper, the research progress of D²NNs is summarized in four aspects: basic theory, further analysis, improvement, and application. Besides, it is analyzed that the common defect of D²NNs from simulation to physical fabrication, and corresponding theoretical improvement method is also proposed. Meanwhile, to further reduce the impact due to the gap between simulation and physical implementation, and to enhance the robustness of the model, the D²NN training method based on generative adversarial network (GAN) is proposed. The D²NN combines optical transmission with deep learning to achieve complex pattern recognition tasks in the optical domain at the speed of light. It is believed that under continuous research, the D²NN can play a greater role in optical communications and other fields.

Proceedings Article | 12 March 2021 Paper

Advances and marine applications of optical neural network

Jianmin Xiong, Zejun Zhang, Jing Xu

Proceedings Volume 11763, 117631O (2021) https://doi.org/10.1117/12.2586296

KEYWORDS: Optical computing, Optoelectronic devices, Neural networks

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With the development of artificial intelligence technology, such as artificial neural networks, the increasing demand for computing drives the upgrading of computing accelerators. It’s known that the semiconductor process is approaching physical limits and the Von Neumann architecture of storage-computing separation affects the computing efficiency, which both lead to the gradual failure of electronic devices to meet application requirements. Optical neural networks (ONNs) can take full advantage of high speed, high bandwidth, high parallelism, and low power consumption of optical transmission to overcome the deficiencies of electronic devices. In this paper, we summarize and analyze previous researches on optical neural networks according to different physical implementations. And we conclude that most studies apply the characteristics of special materials to realize the dense matrix multiplication and nonlinear activation function of ONNs. Less research focuses on the nonlinear characteristics inherent in the optical signal transmission to realize important components of traditional neural networks. ONNs show great potentials in analog computing and information processing, such as marine in-situ imaging and optical receiver of underwater optical communication. And ONN is possible to be a new generation of neural network accelerator. But the large-scale application of ONNs requires more studies in optical implementation of nonlinear activation function and loss function, and accuracy improvement of optical computing.

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