More effective power network prototyping by analytical and centroid learning

Yu Hsiang Chuang, Chang Tzu Lin, Hung-Ming Chen, Chi Han Lee, Ting Sheng Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Recently a prior work has been proposed to improve the power distribution network (PDN) design with some practical methodologies. However, we found that such approach will cause redundant resources, resulting in the waste of the metal application. In this paper, we present a more effective design flow to automatically generate a PDN verified by the commercial tool without IR-Drop violation. We propose an analytical model and consider the different types of macros to determine the total metal width of PDN. Moreover, the optimization is based on a centroid learning method from unsupervised learning to consolidate PDN. Our work has experimented on real designs in 65 nm process, 0.18 um generic process, and 40 nm process. The results show that our framework can satisfy the given IR-Drop constraints and simultaneously save lots of metal resource (means no overdesign).

原文English
主出版物標題2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728103976
DOIs
出版狀態Published - 1 一月 2019
事件2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
持續時間: 26 五月 201929 五月 2019

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2019-May
ISSN(列印)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
國家Japan
城市Sapporo
期間26/05/1929/05/19

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