Verilog-A compatible recurrent neural network model for transient circuit simulation

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

Abstract

This paper presents a method for data-driven behavioral modeling of electronic circuits using recurrent neural networks (RNNs). The RNN structure is adapted based on known characteristics of the system being modeled. The discrete-time RNN is transformed to a continuous-time model and then implemented in Verilog-A for compatibility with general-purpose circuit simulators.

Original languageEnglish (US)
Title of host publication2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9781467364836
DOIs
StatePublished - Jul 2 2017
Event26th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017 - San Jose, United States
Duration: Oct 15 2017Oct 18 2017

Publication series

Name2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
Volume2018-January

Other

Other26th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
Country/TerritoryUnited States
CitySan Jose
Period10/15/1710/18/17

Keywords

  • Behavioral models
  • Circuit simulation
  • Recurrent neural network
  • Verilog-A

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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