High-speed link design optimization using machine learning SVR-AS method

Hanzhi Ma, Er Ping Li, Andreas C. Cangellaris, Xu Chen

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

Abstract

This paper proposes a novel and fast constrained design optimization method based on support vector regression-active subspace method. The proposed optimization method calculates a linear combination of original design parameters named active variable as a low-dimensional representation of high-dimensional design space to transform the non-linear constraint into a reduced linear constraint for optimization problems, which successfully derives a simplified and mathematically solvable equation. A complex high-speed link with 16-dimensional design parameters is utilized to verify this method and results show that the proposed method can efficiently find the optimal design structures compared to interior-point method.

Original languageEnglish (US)
Title of host publicationEPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161617
DOIs
StatePublished - Oct 2020
Event29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020 - San Jose, United States
Duration: Oct 5 2020Oct 7 2020

Publication series

NameEPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020
CountryUnited States
CitySan Jose
Period10/5/2010/7/20

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Energy Engineering and Power Technology

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