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Machine Learning in VLSI Computer-Aided Design

  • Ibrahim (Abe) M. Elfadel
  • Duane S. Boning
  • Xin Li

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Duane S. Boning, Ibrahim (Abe) M. Elfadel, Xin Li
    Pages 1-16
  3. Part I

    1. Front Matter
      Pages 17-17
    2. Seongbo Shim, Suhyeong Choi, Youngsoo Shin
      Pages 69-93
  4. Part II

    1. Front Matter
      Pages 117-117
    2. Constantinos Xanthopoulos, Ke Huang, Ali Ahmadi, Nathan Kupp, John Carulli, Amit Nahar et al.
      Pages 119-173
    3. Hongge Chen, Duane S. Boning
      Pages 175-199
    4. Jun Tao, Wangyang Zhang, Xin Li, Frank Liu, Emrah Acar, Rob A. Rutenbar et al.
      Pages 201-231
    5. Arunkumar Vijayan, Krishnendu Chakrabarty, Mehdi B. Tahoori
      Pages 265-289
  5. Part III

    1. Front Matter
      Pages 291-291
    2. Amith Singhee
      Pages 293-322
    3. Rouwaida Kanj, Rajiv V. Joshi, Lama Shaer, Ali Chehab, Maria Malik
      Pages 323-348
    4. Jun Tao, Shupeng Sun, Xin Li, Hongzhou Liu, Kangsheng Luo, Ben Gu et al.
      Pages 349-373
    5. Li-C. Wang
      Pages 375-399
  6. Part IV

    1. Front Matter
      Pages 401-401
    2. Jun Tao, Fa Wang, Paolo Cachecho, Wangyang Zhang, Shupeng Sun, Xin Li et al.
      Pages 403-422
    3. Hakki M. Torun, Mourad Larbi, Madhavan Swaminathan
      Pages 505-536
  7. Part V

    1. Front Matter
      Pages 537-537
    2. Matthew M. Ziegler, Hung-Yi Liu, George Gristede, Bruce Owens, Ricardo Nigaglioni, Jihye Kwon et al.
      Pages 539-570
    3. Rupesh Raj Karn, Ibrahim (Abe) M. Elfadel
      Pages 571-608
    4. Shobha Vasudevan, Lingyi Liu, Samuel Hertz
      Pages 609-645
    5. Muhammad Abdullah Hanif, Rehan Hafiz, Muhammad Usama Javed, Semeen Rehman, Muhammad Shafique
      Pages 647-678
  8. Back Matter
    Pages 679-694

About this book

Introduction

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

  • Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability;
  • Discusses the use of machine learning techniques in the context of analog and digital synthesis;
  • Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions;
  • Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs.

From the Foreword

As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other….As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation.

Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Keywords

VLSI Design VLSI Verification VLSI Testing VLSI Analog Circuits CMOS VLSI Design

Editors and affiliations

  • Ibrahim (Abe) M. Elfadel
    • 1
  • Duane S. Boning
    • 2
  • Xin Li
    • 3
  1. 1.Department of Electrical and Computer Engineering and Center for Cyber Physical SystemsKhalifa UniversityAbu DhabiUnited Arab Emirates
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-04666-8
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-04665-1
  • Online ISBN 978-3-030-04666-8
  • Buy this book on publisher's site