{"title":"NetMap-software tool for mapping neural networks onto parallel computers","authors":"K. Przytula, V. Prasanna, W.-M. Lin","doi":"10.1109/NNSP.1992.253653","DOIUrl":null,"url":null,"abstract":"The authors present a software tool for mapping neural network computations onto mesh-connected SIMD (single instruction stream multiple data stream) computers. The tool, called NetMap, contains complete mapping programs for built-in network models and a library of elemental routing routines. For built-in models the user is required to provide only minimum information about the network. For example, for a backpropagation network with fully connected layers, it is enough to provide the number of neurons in each layer of the network. If the network has been pruned or structured in some other way, so that the layers are no longer fully interconnected, a complete connectivity matrix needs to be provided. Thus, NetMap can be used for networks of arbitrary topology.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1992.253653","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 a software tool for mapping neural network computations onto mesh-connected SIMD (single instruction stream multiple data stream) computers. The tool, called NetMap, contains complete mapping programs for built-in network models and a library of elemental routing routines. For built-in models the user is required to provide only minimum information about the network. For example, for a backpropagation network with fully connected layers, it is enough to provide the number of neurons in each layer of the network. If the network has been pruned or structured in some other way, so that the layers are no longer fully interconnected, a complete connectivity matrix needs to be provided. Thus, NetMap can be used for networks of arbitrary topology.<>
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