Reinforcement Learning for the Optimization of Power Plane Designs in Power Delivery Networks

Seunghyup Han, Osama Waqar Bhatti, Madhavan Swaminathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper proposes a deep deterministic policy gradient (DDPG) based method to optimize the power plane in power delivery networks (PDNs). The proposed method considers the degrees of freedom of a plane design in a layer, determining the parameters for creating a power plane. The results show that the proposed method can provide an optimized power plane design even in a plane layer with a restricted region.

Original languageEnglish (US)
Title of host publicationEPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450751
DOIs
StatePublished - 2022
Event31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022 - San Jose, United States
Duration: Oct 9 2022Oct 12 2022

Publication series

NameEPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022
Country/TerritoryUnited States
CitySan Jose
Period10/9/2210/12/22

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials
  • Instrumentation

Fingerprint

Dive into the research topics of 'Reinforcement Learning for the Optimization of Power Plane Designs in Power Delivery Networks'. Together they form a unique fingerprint.

Cite this