{"title":"采用VLSI构建块进行动态规划神经网络的体系结构设计","authors":"C. Chiu, M. Shanblatt","doi":"10.1109/GLSV.1991.143962","DOIUrl":null,"url":null,"abstract":"An architecture for dynamic programming neural networks is presented. The architecture is based on a building block paradigm in which the network is constructed from neuron array and weight assignment chips. Because of its simple and regular structure, the architecture is a feasible implementation for dynamic programming neural networks with current VLSI technology. Moreover, this architecture can be further extended to other Hopfield-Tank type networks.<<ETX>>","PeriodicalId":261873,"journal":{"name":"[1991] Proceedings. First Great Lakes Symposium on VLSI","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An architecture design using VLSI building blocks for dynamic programming neural networks\",\"authors\":\"C. Chiu, M. Shanblatt\",\"doi\":\"10.1109/GLSV.1991.143962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An architecture for dynamic programming neural networks is presented. The architecture is based on a building block paradigm in which the network is constructed from neuron array and weight assignment chips. Because of its simple and regular structure, the architecture is a feasible implementation for dynamic programming neural networks with current VLSI technology. Moreover, this architecture can be further extended to other Hopfield-Tank type networks.<<ETX>>\",\"PeriodicalId\":261873,\"journal\":{\"name\":\"[1991] Proceedings. First Great Lakes Symposium on VLSI\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1991] Proceedings. First Great Lakes Symposium on VLSI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLSV.1991.143962\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991] Proceedings. First Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLSV.1991.143962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An architecture design using VLSI building blocks for dynamic programming neural networks
An architecture for dynamic programming neural networks is presented. The architecture is based on a building block paradigm in which the network is constructed from neuron array and weight assignment chips. Because of its simple and regular structure, the architecture is a feasible implementation for dynamic programming neural networks with current VLSI technology. Moreover, this architecture can be further extended to other Hopfield-Tank type networks.<>