{"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":"76 1","pages":"149-152 vol.5"},"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.