Ferroelectric-based Accelerators for Computationally Hard Problems

Mohammad Khairul Bashar, Jaykumar Vaidya, R. S. Surya Kanthi, Chonghan Lee, Feng Shi, Vijaykrishnan Narayanan, Nikhil Shukla

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

Abstract

Solving hard combinatorial optimization problems such as graph coloring efficiently continues to be an outstanding challenge for computing. Traditional digital computers typically entail an exponential increase in computing resources as the problem sizes increase. This makes larger problems of practical relevance intractable to compute, with subsequently adverse implications for a broad spectrum of ever-more relevant practical applications ranging from machine learning to electronic device automation (EDA). Here, we examine how analog coupled oscillators can enable area and energy-efficient methods to accelerate such problems. Further, we discuss how the implementation of such non-Boolean platforms can take advantage of emerging technologies such as scalable ferroelectrics.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2021 - Proceedings of the 2021 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages485-489
Number of pages5
ISBN (Electronic)9781450383936
DOIs
StatePublished - Jun 22 2021
Event31st Great Lakes Symposium on VLSI, GLSVLSI 2021 - Virtual, Online, United States
Duration: Jun 22 2021Jun 25 2021

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference31st Great Lakes Symposium on VLSI, GLSVLSI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/22/216/25/21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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