Generating Routing-Driven Power Distribution Networks With Machine-Learning Technique

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and electro-migration (EM) constraints. This paper presents a design flow to generate a PDN that can result in near-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 28 nm 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.

Original languageEnglish
Article number7807300
Pages (from-to)1237-1250
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number8
StatePublished - 1 Aug 2017


  • Electro-migration (EM)
  • IR drop
  • machine learning
  • power grid design
  • routing cost model
  • routing-driven

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