Invertible Neural Networks for Inverse Design of CTLE in High-speed Channels

Majid Ahadi Dolatsara, Huan Yu, Jose Ale Hejase, Wiren Dale Becker, Madhavan Swaminathan

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

7 Scopus citations

Abstract

Designing CTLE of high-speed channels can be complicated and time consuming. To alleviate this issue, this paper investigates the invertible neural networks (INNs) for inverse design of the CTLE. In this approach, a desired eye height and eye width is given, and the algorithm finds the corresponding peaking frequency and gain value of the CTLE. INN is a special type of neural networks that can be traversed in both forward and reverse directions. An advantage of this network is producing distribution of the input variables based on the desired output. This feature enables the algorithm to provide multiple solutions when a multi-modal distribution is produced. Thus, the user can choose the appropriate solution based on other constraints. A numerical example for inverse design of CTLE of a SerDes channel is provided, which results in moderate accuracy. However, other variations of the example show that the accuracy is case dependent which implies improvements on the algorithm is needed.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194127
DOIs
StatePublished - Dec 14 2020
Event2020 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2020 - Virtual, Shenzhen, China
Duration: Dec 14 2020Dec 16 2020

Publication series

NameIEEE Electrical Design of Advanced Packaging and Systems Symposium
Volume2020-December
ISSN (Print)2151-1225
ISSN (Electronic)2151-1233

Conference

Conference2020 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2020
Country/TerritoryChina
CityVirtual, Shenzhen
Period12/14/2012/16/20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Automotive Engineering
  • General Computer Science

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