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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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).

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - 1 Jan 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period26/05/1929/05/19

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