An algorithm for multi-layer channel routing problem using chaotic neural networks

M. Ohta
{"title":"An algorithm for multi-layer channel routing problem using chaotic neural networks","authors":"M. Ohta","doi":"10.1109/ISCAS.2000.857385","DOIUrl":null,"url":null,"abstract":"In this paper a novel algorithm for the multi-layer channel routing problem in VLSI design using a chaotic neural network (chaotic NN) is proposed. For this problem, Funabiki and Takefuji (1992) proposed a parallel algorithm using the maximum neural network. However it is often caught in a local minimum because the maximum neural network is based on the Hopfield neural network. On the other hand, the chaotic NN has the characteristic of escaping from a local minimum. A novel algorithm using the chaotic NN is proposed. In order to confirm the effectiveness of the algorithm, numerical experiments are carried out, and it is confirmed experimentally that the proposal is more effective than the Funabiki and Takefuji algorithm.","PeriodicalId":6422,"journal":{"name":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2000.857385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper a novel algorithm for the multi-layer channel routing problem in VLSI design using a chaotic neural network (chaotic NN) is proposed. For this problem, Funabiki and Takefuji (1992) proposed a parallel algorithm using the maximum neural network. However it is often caught in a local minimum because the maximum neural network is based on the Hopfield neural network. On the other hand, the chaotic NN has the characteristic of escaping from a local minimum. A novel algorithm using the chaotic NN is proposed. In order to confirm the effectiveness of the algorithm, numerical experiments are carried out, and it is confirmed experimentally that the proposal is more effective than the Funabiki and Takefuji algorithm.
基于混沌神经网络的多层信道路由算法
本文提出了一种利用混沌神经网络(chaotic neural network,简称混沌神经网络)解决VLSI设计中多层信道路由问题的新算法。对于这个问题,Funabiki和Takefuji(1992)提出了一种利用最大神经网络的并行算法。然而,由于极大值神经网络是基于Hopfield神经网络的,它经常陷入局部极小值。另一方面,混沌神经网络具有摆脱局部极小值的特性。提出了一种新的混沌神经网络算法。为了验证算法的有效性,进行了数值实验,实验证实了该算法比Funabiki和Takefuji算法更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
文献相关原料
公司名称 产品信息 采购帮参考价格
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信