@misc{Zhang_Ye_Noise_2023,
 author={Zhang, Ye and Zhang, Saining and Zhang, Danni and Su, Yanmei and Wang, Pengfei and Yi, Junkai and Wang, Ruiting and Luo, Guangzhen and Zhou, Xuliang and Pan, Jiaoqing},
 contributor={Urbańczyk, Wacław. Redakcja},
 identifier={DOI: 10.37190/oa230311},
 year={2023},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 description={Optica Applicata, Vol. 53, 2023, nr 3, s. 483-493},
 description={Optica Applicata is an international journal, published in a non-periodical form in the years 1971-1973 and quarterly since 1973. From the beginning of the year 2008, Optica Applicata is an Open Access journal available online via the Internet, with free access to the full text of articles serving the best interests of the scientific community. The journal is abstracted and indexed in: Chemical Abstracts, Compendex, Current Contents, Inspec, Referativnyj Zhurnal, SCI Expanded, Scopus, Ulrich’s Periodicals Directory},
 description={http://opticaapplicata.pwr.edu.pl/},
 language={eng},
 abstract={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.},
 type={artykuł},
 title={Noise quantization simulation analysis of optical convolutional networks},
 keywords={optyka, optical neural network, convolutional neural network, quantization, noise},
}