{"title":"cnn大邻域克隆模板的混合逻辑和模拟实现","authors":"R. Akbari-Dilmaghani","doi":"10.1109/CNNA.1998.685387","DOIUrl":null,"url":null,"abstract":"An approach to the implementation of large neighbourhood (r>1) cloning templates in cellular neural networks (CNNs) is presented while the number of interconnections and the circuit complexity are preserved at a level which is practical for existing VLSI technology. The proposed method employs mixed pass transistors logic and analog circuitry to implement r>1 CNNs. Simulation results are presented to confirm the viability of the proposed methods.","PeriodicalId":171485,"journal":{"name":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Mixed logic and analog implementation of large neighbourhood cloning templates for CNNs\",\"authors\":\"R. Akbari-Dilmaghani\",\"doi\":\"10.1109/CNNA.1998.685387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An approach to the implementation of large neighbourhood (r>1) cloning templates in cellular neural networks (CNNs) is presented while the number of interconnections and the circuit complexity are preserved at a level which is practical for existing VLSI technology. The proposed method employs mixed pass transistors logic and analog circuitry to implement r>1 CNNs. Simulation results are presented to confirm the viability of the proposed methods.\",\"PeriodicalId\":171485,\"journal\":{\"name\":\"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1998.685387\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1998.685387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mixed logic and analog implementation of large neighbourhood cloning templates for CNNs
An approach to the implementation of large neighbourhood (r>1) cloning templates in cellular neural networks (CNNs) is presented while the number of interconnections and the circuit complexity are preserved at a level which is practical for existing VLSI technology. The proposed method employs mixed pass transistors logic and analog circuitry to implement r>1 CNNs. Simulation results are presented to confirm the viability of the proposed methods.