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Digit recognition based on programmable nanophotonic processor
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Paper Abstract

Artificial neural networks are computational models enlightened by biological neural networks, playing a significant role in image recognition, language translation and computer vision fields, etc. In this paper, we propose a fully optical neural network based on programmable nanophotonic processor (PNP) to realize digit recognition. The architecture includes 4 layers cascaded Mach–Zehnder interferometers (MZIs), which could theoretically execute matrix functions corresponding to a two-layer fully connected ANN with four inputs. We simulate cascaded MZIs and adjust phase shifters to match weight matrices calculated by ANN in computer beforehand. The accuracy of 4-class handwritten digits in ONN is 80.29% due to the compressed input data. The accuracy of 10-class digits could achieve 99.23% when the input node merely increases to 36. The results demonstrate the handwritten digits could be recognized effectively through PNP in ONN and the construction of PNP could be extended for more complex recognition systems.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390D (6 September 2019); doi: 10.1117/12.2527960
Show Author Affiliations
Yuan Chen, Zhejiang Univ. (China)
Yi Huang, Zhejiang Univ. (China)
Jinlei Zhang, Zhejiang Univ. (China)
Zhentao Qin, Zhejiang Univ. (China)
Zhenrong Zheng, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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