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

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

doi:10.1109/EPEPS48591.2020.9231368

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 proceeding › Conference 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 language	English (US)
Title of host publication	EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728161617
DOIs	https://doi.org/10.1109/EPEPS48591.2020.9231368
State	Published - Oct 2020
Event	29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020 - San Jose, United States Duration: Oct 5 2020 → Oct 7 2020

Publication series

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

Conference

Conference	29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020
Country/Territory	United States
City	San Jose
Period	10/5/20 → 10/7/20

ASJC Scopus subject areas

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

Online availability

10.1109/EPEPS48591.2020.9231368

Library availability

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Cite this

Ma, H., Li, E. P., Cangellaris, A. C., & Chen, X. (2020). High-speed link design optimization using machine learning SVR-AS method. In EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems Article 9231368 (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEPS48591.2020.9231368

High-speed link design optimization using machine learning SVR-AS method. / Ma, Hanzhi; Li, Er Ping; Cangellaris, Andreas C. et al.
EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2020. 9231368 (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ma, H, Li, EP, Cangellaris, AC & Chen, X 2020, High-speed link design optimization using machine learning SVR-AS method. in EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems., 9231368, EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems, Institute of Electrical and Electronics Engineers Inc., 29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020, San Jose, United States, 10/5/20. https://doi.org/10.1109/EPEPS48591.2020.9231368

Ma H, Li EP, Cangellaris AC , Chen X. High-speed link design optimization using machine learning SVR-AS method. In EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc. 2020. 9231368. (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems). doi: 10.1109/EPEPS48591.2020.9231368

Ma, Hanzhi ; Li, Er Ping ; Cangellaris, Andreas C. et al. / High-speed link design optimization using machine learning SVR-AS method. EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2020. (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems).

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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.",

author = "Hanzhi Ma and Li, {Er Ping} and Cangellaris, {Andreas C.} and Xu Chen",

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N2 - 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.

AB - 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.

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

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