Preliminary Application of Deep Learning to Design Space Exploration

Kallol Roy, Hakki Torun Mert, Madhavan Swaminathan

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

7 Scopus citations

Abstract

The increasing trend towards higher performance electronic systems has led to various challenges in design space exploration. Often times, interconnects in such systems are analyzed using CPU intensive fullwave EM simulations, making parameter sweeps in a very large design space impractical. In this paper, we propose using Deep Neural Networks (DNN) as a solution to cover the large design space using their generalization capability, i.e., predicting outside the range of training data. We show the performance of the proposed method on predicting the frequency dependent RLGC matrices of a multi-conductor microstrip transmission line.

Original languageEnglish (US)
Title of host publicationIEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538665923
DOIs
StatePublished - Jul 2 2018
Event2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018 - Chandigarh, India
Duration: Dec 16 2018Dec 18 2018

Publication series

NameIEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018

Conference

Conference2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018
Country/TerritoryIndia
CityChandigarh
Period12/16/1812/18/18

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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