{"title":"O(n)深度-2的前馈神经网络二进制加法","authors":"S. Vassiliadis, K. Bertels, G. Pechanek","doi":"10.1109/ICNN.1994.374487","DOIUrl":null,"url":null,"abstract":"In this paper we investigate the reduction of the size of depth-2 feedforward neural networks performing binary addition and related functions. We suggest that 2-1 binary n-bit addition and some related functions can be computed in a depth-2 network of size O(n) with maximum fan-in of 2n+1. Furthermore, we show, if both input polarities are available, that the comparison can be computed in a depth-1 network of size O(1) also with maximum fan-in of 2n+1.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"O(n) depth-2 binary addition with feedforward neural nets\",\"authors\":\"S. Vassiliadis, K. Bertels, G. Pechanek\",\"doi\":\"10.1109/ICNN.1994.374487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we investigate the reduction of the size of depth-2 feedforward neural networks performing binary addition and related functions. We suggest that 2-1 binary n-bit addition and some related functions can be computed in a depth-2 network of size O(n) with maximum fan-in of 2n+1. Furthermore, we show, if both input polarities are available, that the comparison can be computed in a depth-1 network of size O(1) also with maximum fan-in of 2n+1.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374487\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
O(n) depth-2 binary addition with feedforward neural nets
In this paper we investigate the reduction of the size of depth-2 feedforward neural networks performing binary addition and related functions. We suggest that 2-1 binary n-bit addition and some related functions can be computed in a depth-2 network of size O(n) with maximum fan-in of 2n+1. Furthermore, we show, if both input polarities are available, that the comparison can be computed in a depth-1 network of size O(1) also with maximum fan-in of 2n+1.<>
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