Optical Convolutional Neural Networks: Methodology and Advances (Invited)

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Abstract

As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application.

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Meng, X., Shi, N., Li, G., Li, W., Zhu, N., & Li, M. (2023, July 1). Optical Convolutional Neural Networks: Methodology and Advances (Invited). Applied Sciences (Switzerland). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app13137523

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