Machine Learning in VLSI Computer-Aided Design

✍ Scribed by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 697
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Front Matter ....Pages i-xxii
A Preliminary Taxonomy for Machine Learning in VLSI CAD (Duane S. Boning, Ibrahim (Abe) M. Elfadel, Xin Li)....Pages 1-16
Front Matter ....Pages 17-17
Machine Learning for Compact Lithographic Process Models (J. P. Shiely)....Pages 19-68
Machine Learning for Mask Synthesis (Seongbo Shim, Suhyeong Choi, Youngsoo Shin)....Pages 69-93
Machine Learning in Physical Verification, Mask Synthesis, and Physical Design (Yibo Lin, David Z. Pan)....Pages 95-115
Front Matter ....Pages 117-117
Gaussian Process-Based Wafer-Level Correlation Modeling and Its Applications (Constantinos Xanthopoulos, Ke Huang, Ali Ahmadi, Nathan Kupp, John Carulli, Amit Nahar et al.)....Pages 119-173
Machine Learning Approaches for IC Manufacturing Yield Enhancement (Hongge Chen, Duane S. Boning)....Pages 175-199
Efficient Process Variation Characterization by Virtual Probe (Jun Tao, Wangyang Zhang, Xin Li, Frank Liu, Emrah Acar, Rob A. Rutenbar et al.)....Pages 201-231
Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement (Jinjun Xiong, Yada Zhu, Jingrui He)....Pages 233-263
Machine Learning-Based Aging Analysis (Arunkumar Vijayan, Krishnendu Chakrabarty, Mehdi B. Tahoori)....Pages 265-289
Front Matter ....Pages 291-291
Extreme Statistics in Memories (Amith Singhee)....Pages 293-322
Fast Statistical Analysis Using Machine Learning (Rouwaida Kanj, Rajiv V. Joshi, Lama Shaer, Ali Chehab, Maria Malik)....Pages 323-348
Fast Statistical Analysis of Rare Circuit Failure Events (Jun Tao, Shupeng Sun, Xin Li, Hongzhou Liu, Kangsheng Luo, Ben Gu et al.)....Pages 349-373
Learning from Limited Data in VLSI CAD (Li-C. Wang)....Pages 375-399
Front Matter ....Pages 401-401
Large-Scale Circuit Performance Modeling by Bayesian Model Fusion (Jun Tao, Fa Wang, Paolo Cachecho, Wangyang Zhang, Shupeng Sun, Xin Li et al.)....Pages 403-422
Sparse Relevance Kernel Machine-Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits (Honghuang Lin, Asad Khan, Peng Li)....Pages 423-447
SiLVR: Projection Pursuit for Response Surface Modeling (Amith Singhee)....Pages 449-503
Machine Learning-Based System Optimization and Uncertainty Quantification for Integrated Systems (Hakki M. Torun, Mourad Larbi, Madhavan Swaminathan)....Pages 505-536
Front Matter ....Pages 537-537
SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors (Matthew M. Ziegler, Hung-Yi Liu, George Gristede, Bruce Owens, Ricardo Nigaglioni, Jihye Kwon et al.)....Pages 539-570
Multicore Power and Thermal Proxies Using Least-Angle Regression (Rupesh Raj Karn, Ibrahim (Abe) M. Elfadel)....Pages 571-608
A Comparative Study of Assertion Mining Algorithms in GoldMine (Shobha Vasudevan, Lingyi Liu, Samuel Hertz)....Pages 609-645
Energy-Efficient Design of Advanced Machine Learning Hardware (Muhammad Abdullah Hanif, Rehan Hafiz, Muhammad Usama Javed, Semeen Rehman, Muhammad Shafique)....Pages 647-678
Back Matter ....Pages 679-694

✦ Subjects

Engineering; Circuits and Systems; Processor Architectures; Logic Design