首页|Conversion of a single-layer ANN to photonic SNN for pattern recognition

Conversion of a single-layer ANN to photonic SNN for pattern recognition

扫码查看
This work presents a complete conversion scheme for photonic spiking neural networks(SNNs).We verified that the output of an artificial neural network(ANN)trained with the simulated optical activation function can be directly converted into the spike rate of a photonic spiking neuron model.To reveal the feasibility of hardware implementation,we considered the effects of different bit precisions of data and weight,noise level,and bias current mismatch on the converted results.The proposed scheme was evaluated using the Deterding vowel,IRIS,TIDIGITS,and MNIST datasets for pattern recognition,and achieved mean accuracies of 95.80%,98.67%,96.19%,and 92.33%,respectively.The proposed scheme can convert an ANN into a photonic SNN with almost no precision loss,and the performance was comparable to that of an ANN trained with the rectified linear unit function.The proposed scheme can enable the high-performance implementation of photonic SNNs.

photonic SNNconversionoptical computingpattern recognitionartificial neural network

Yanan HAN、Shuiying XIANG、Tianrui ZHANG、Yahui ZHANG、Xingxing GUO、Yuechun SHI

展开 >

State Key Laboratory of Integrated Service Networks,State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology,Xidian University,Xi'an 710071,China

Yongjiang Laboratory,Ningbo 315202,China

National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Outstanding Youth Science Fund Project of National Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities

2021YFB28019002021YFB28019012021YFB28019022021YFB2801904619741776167411962022062JB210114

2024

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子:0.715
ISSN:1674-733X
年,卷(期):2024.67(1)
  • 45