{"title":"Optical inner-product implementations for multi-layer BAM with 2-dimensional patterns","authors":"Hyuek-Jae Lee, Soo-Young Lee, C. Park, S. Shin","doi":"10.1109/IJCNN.1991.170675","DOIUrl":null,"url":null,"abstract":"The authors present an optical inner-product architecture for MBAM (multi-layer bidirectional associative memory) with two-dimensional input and output patterns. The proposed architecture utilizes compact solid modules for single-layer feedforward networks, which may be cascaded for MBAM. Instead of analog interconnection weights the inner-product scheme stores input and output patterns. For binary input and output patterns this inner-product scheme requires binary spatial light modulators only, and is scalable to very large-size implementations. Unlike optical neural networks for one-dimensional patterns, multifocus holograms and lenslet arrays become essential components in these modules. The performance of the MBAM was demonstrated by an electrooptic inner-product implementation for the exclusive-OR problem.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The authors present an optical inner-product architecture for MBAM (multi-layer bidirectional associative memory) with two-dimensional input and output patterns. The proposed architecture utilizes compact solid modules for single-layer feedforward networks, which may be cascaded for MBAM. Instead of analog interconnection weights the inner-product scheme stores input and output patterns. For binary input and output patterns this inner-product scheme requires binary spatial light modulators only, and is scalable to very large-size implementations. Unlike optical neural networks for one-dimensional patterns, multifocus holograms and lenslet arrays become essential components in these modules. The performance of the MBAM was demonstrated by an electrooptic inner-product implementation for the exclusive-OR problem.<>