Dapeng Zhang, G. Jullien, W. Miller, E. Swartzlander
{"title":"Arithmetic for digital neural networks","authors":"Dapeng Zhang, G. Jullien, W. Miller, E. Swartzlander","doi":"10.1109/ARITH.1991.145534","DOIUrl":null,"url":null,"abstract":"The implementation of large input digital neurons using designs based on parallel counters is described. The implementation of the design uses a two-cell library, in which each cell is implemented using switching trees which are pipelined binary trees of n-channel transistors. Results obtained from initial switching trees realized with a 3- mu m CMOS process indicate that the design is capable of being pipelined at 40 MHz sample rates, with better performance expected for more advanced technologies. It appears feasible to develop a wafer-scale implementation with 2000 neurons (each with 1000 inputs) that would perform 3*10/sup 12/ additions/s.<<ETX>>","PeriodicalId":190650,"journal":{"name":"[1991] Proceedings 10th IEEE Symposium on Computer Arithmetic","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991] Proceedings 10th IEEE Symposium on Computer Arithmetic","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ARITH.1991.145534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
The implementation of large input digital neurons using designs based on parallel counters is described. The implementation of the design uses a two-cell library, in which each cell is implemented using switching trees which are pipelined binary trees of n-channel transistors. Results obtained from initial switching trees realized with a 3- mu m CMOS process indicate that the design is capable of being pipelined at 40 MHz sample rates, with better performance expected for more advanced technologies. It appears feasible to develop a wafer-scale implementation with 2000 neurons (each with 1000 inputs) that would perform 3*10/sup 12/ additions/s.<>