@inproceedings{1026255001704551a4bc655d2941f638,
title = "Generating routing-driven power distribution networks with machine-learning technique",
abstract = "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.",
author = "Chang, {Wen Hsiang} and Chen, {Li De} and Lin, {Chien Hsueh} and Mu, {Szu Pang} and Chia-Tso Chao and Tsai, {Cheng Hong} and Chiu, {Yen Chih}",
year = "2016",
month = apr,
day = "3",
doi = "10.1145/2872334.2872353",
language = "English",
series = "Proceedings of the International Symposium on Physical Design",
publisher = "Association for Computing Machinery",
pages = "145--152",
booktitle = "ISPD 2016 - Proceedings of the 2016 International Symposium on Physical Design",
note = "2016 International Symposium on Physical Design, ISPD 2016 ; Conference date: 03-04-2016 Through 06-04-2016",
}