None Ye Zhang, None Saining Zhang, None Danni Zhang, None Yanmei Su, None Junkai Yi, None Pengfei Wang, None Ruiting Wang, None Guangzhen Luo, None Xuliang Zhou, None Jiaoqing Pan
{"title":"Noise quantization simulation analysis of optical convolutional networks","authors":"None Ye Zhang, None Saining Zhang, None Danni Zhang, None Yanmei Su, None Junkai Yi, None Pengfei Wang, None Ruiting Wang, None Guangzhen Luo, None Xuliang Zhou, None Jiaoqing Pan","doi":"10.37190/oa230311","DOIUrl":null,"url":null,"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.","PeriodicalId":19589,"journal":{"name":"Optica Applicata","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optica Applicata","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37190/oa230311","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Acoustooptics, atmospheric and ocean optics, atomic and molecular optics, coherence and statistical optics, biooptics, colorimetry, diffraction and gratings, ellipsometry and polarimetry, fiber optics and optical communication, Fourier optics, holography, integrated optics, lasers and their applications, light detectors, light and electron beams, light sources, liquid crystals, medical optics, metamaterials, microoptics, nonlinear optics, optical and electron microscopy, optical computing, optical design and fabrication, optical imaging, optical instrumentation, optical materials, optical measurements, optical modulation, optical properties of solids and thin films, optical sensing, optical systems and their elements, optical trapping, optometry, photoelasticity, photonic crystals, photonic crystal fibers, photonic devices, physical optics, quantum optics, slow and fast light, spectroscopy, storage and processing of optical information, ultrafast optics.
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