Abstraction Refinement for Large Scale Model Checking

  • Chao Wang
  • Gary D. Hachtel
  • Fabio Somenzi

Part of the Series on Integrated Circuits and Systems book series (ICIR)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Pages 1-10
  3. Pages 41-54
  4. Pages 55-83
  5. Pages 153-156
  6. Back Matter
    Pages 157-179

About this book

Introduction

Abstraction Refinement for Large Scale Model Checking summarizes recent research on abstraction techniques for model checking large digital systems.  Considering both the size of today's digital systems and the capacity of state-of-the-art verification algorithms, abstraction is the only viable solution for the successful application of model checking techniques to industrial-scale designs.  This book describes recent research developments in automatic abstraction refinement techniques.  The authors address the main challenge in abstraction refinement, i.e., the ability to efficiently reach or come close to the optimum abstraction (the smallest abstract model that proves or refutes the given property).  A suite of fully automatic abstraction techniques are proposed to improve the overall computation efficiency.  The suite of algorithms presented in this book has demonstrated significant improvement over the prior art; some of them have already been adopted by the EDA companies in their commercial/in-house verification tools.

Abstraction Refinement for Large Scale Model Checking will be of interest to EDA researchers and tool developers, verification engineers, as well as people who are in the general areas of computer science and want to know the state-of-the-art of formal verification.

Keywords

Abstraction Refinement Hachtel Integrated Circuits Large Scale Model Checking Somenzi Wang algorithms model

Authors and affiliations

  • Chao Wang
    • 1
  • Gary D. Hachtel
    • 2
  • Fabio Somenzi
    • 2
  1. 1.NEC Laboratories AmericaPrincetonUSA
  2. 2.Department of Electrical/Computer EngineeringUniversity of ColoradoBoulderUSA

Bibliographic information

  • DOI https://doi.org/10.1007/0-387-34600-7
  • Copyright Information Springer Science+Business Media, LLC 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Engineering
  • Print ISBN 978-0-387-34155-2
  • Online ISBN 978-0-387-34600-7
  • Series Print ISSN 1558-9412
  • About this book