Neural Networks for Transient Modeling of Circuits: Invited Paper

Jie Xiong, Alan S. Yang, Maxim Raginsky, Elyse Rosenbaum

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

Abstract

Theoretical analyses as well as case studies have established that behavioral models based on a recurrent neural network (RNN) are suitable for transient modeling of nonlinear circuits. After training, an RNN model can be implemented in Verilog-A and evaluated by a SPICE-type circuit simulator. This paper describes hurdles that have prevented wide-scale adoption of the RNN as an IP-obscuring behavioral model for circuits and presents recent advances. A new stability constraint is formulated and demonstrated to guide model training and improve performance. Augmented RNNs that can accurately capture aging effects and represent process variations are presented.

Original languageEnglish (US)
Title of host publication2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665431668
DOIs
StatePublished - Aug 30 2021
Externally publishedYes
Event3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021 - Raleigh, United States
Duration: Aug 30 2021Sep 3 2021

Publication series

Name2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021

Conference

Conference3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021
Country/TerritoryUnited States
CityRaleigh
Period8/30/219/3/21

Keywords

  • circuit simulation
  • neural ODE
  • recurrent neural network
  • transient models

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Hardware and Architecture

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