{"title":"Neural networks in FPGAs","authors":"A. Omondi, J. Rajapakse","doi":"10.1109/ICONIP.2002.1198202","DOIUrl":null,"url":null,"abstract":"As FPGAs have increasingly become denser and faster, they are being utilized for many applications, including the implementation of neural networks. Ideally, FPGA implementations, being directly in hardware and having parallelism, will have performance advantages over software on conventional machines. But there is a great deal to be done to make the most of FPGAs and to prove their worth in implementing neural networks, especially in view of past failures in the implementation of neurocomputers. This paper looks at some of the relevant issues.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICONIP.2002.1198202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31

Abstract

As FPGAs have increasingly become denser and faster, they are being utilized for many applications, including the implementation of neural networks. Ideally, FPGA implementations, being directly in hardware and having parallelism, will have performance advantages over software on conventional machines. But there is a great deal to be done to make the most of FPGAs and to prove their worth in implementing neural networks, especially in view of past failures in the implementation of neurocomputers. This paper looks at some of the relevant issues.
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fpga中的神经网络
随着fpga变得越来越密集和快速,它们被用于许多应用,包括神经网络的实现。理想情况下,FPGA实现直接位于硬件中并具有并行性,将比传统机器上的软件具有性能优势。但是,要充分利用fpga并证明其在实现神经网络方面的价值,特别是考虑到过去在实现神经计算机方面的失败,还有很多工作要做。本文着眼于一些相关问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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