Generating routing-driven power distribution networks with machine-learning technique

Wen Hsiang Chang, Li De Chen, Chien Hsueh Lin, Szu Pang Mu, Chia-Tso Chao, Cheng Hong Tsai, Yen Chih Chiu

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and EM constraints. This paper presents a design flow to generate a PDN that can result in minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop and EM constraints based on a given cell placement. The design flow relies on a machine-learning model to quickly predict the total wire length of global route associated with a given PDN configuration in order to speed up the search process. The experimental results based on various 28nm industrial block designs have demonstrated the accuracy of the learned model for predicting the routing cost and the effectiveness of the proposed framework for reducing the routing cost of the final PDN.

原文English
主出版物標題ISPD 2016 - Proceedings of the 2016 International Symposium on Physical Design
發行者Association for Computing Machinery
頁面145-152
頁數8
ISBN(電子)9781450340397
DOIs
出版狀態Published - 3 4月 2016
事件2016 International Symposium on Physical Design, ISPD 2016 - Santa Rosa, United States
持續時間: 3 4月 20166 4月 2016

出版系列

名字Proceedings of the International Symposium on Physical Design
03-06-April-2016

Conference

Conference2016 International Symposium on Physical Design, ISPD 2016
國家/地區United States
城市Santa Rosa
期間3/04/166/04/16

指紋

深入研究「Generating routing-driven power distribution networks with machine-learning technique」主題。共同形成了獨特的指紋。

引用此