{"title":"状态流和状态扫描CNN架构","authors":"L. Spaanenburg, S. Malki","doi":"10.1109/CNNA.2010.5430342","DOIUrl":null,"url":null,"abstract":"The Cellular Neural Network is an obvious candidate for multi-core realization. For reason of its seemingly simple architecture, it is therefore the ideal candidate to evaluate techniques for multi-core technology mapping. In this paper it is studied how a CNN implementation can be unrolled in space or in time to fit the specific characteristics of a multi-core platform. It illustrates that this is a crucial step that sets the basic performance of a multi-core realization.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"State-flow and state-scan CNN architectures\",\"authors\":\"L. Spaanenburg, S. Malki\",\"doi\":\"10.1109/CNNA.2010.5430342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Cellular Neural Network is an obvious candidate for multi-core realization. For reason of its seemingly simple architecture, it is therefore the ideal candidate to evaluate techniques for multi-core technology mapping. In this paper it is studied how a CNN implementation can be unrolled in space or in time to fit the specific characteristics of a multi-core platform. It illustrates that this is a crucial step that sets the basic performance of a multi-core realization.\",\"PeriodicalId\":336891,\"journal\":{\"name\":\"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.2010.5430342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.2010.5430342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Cellular Neural Network is an obvious candidate for multi-core realization. For reason of its seemingly simple architecture, it is therefore the ideal candidate to evaluate techniques for multi-core technology mapping. In this paper it is studied how a CNN implementation can be unrolled in space or in time to fit the specific characteristics of a multi-core platform. It illustrates that this is a crucial step that sets the basic performance of a multi-core realization.