Machine Learning in VLSI Computer-Aided Design
Description
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.
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Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability;
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Discusses the use of machine learning techniques in the context of analog and digital synthesis;
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Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions;
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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 thatI 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
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Persons
Ibrahim (Abe) M. Elfadel is Professor of Electrical and Computer Engineering at the Khalifa University of Science and Technology, Abu Dhabi, UAE. Since May 2014, he has been the Program Manager of TwinLab MEMS, a joint collaboration with GLOBALFOUNDRIES and the Singapore Institute of Microelectronics on micro-electromechanical systems. Between May 2013 and May 2018, he was the founding co-director of the Abu Dhabi Center of Excellence on Energy-Efficient Electronic Systems (ACE 4 S). Between November 2012 and October 2015, he was the founding co-director of Mubadala's TwinLab 3DSC, a joint research center on 3D integrated circuits with the Technical University of Dresden, Germany. He also headed the Masdar Institute Center for Microsystems (iMicro) from November 2013 until March 2016. From 1996 to 2010, he was with the corporate CAD organizations at IBM Research and the IBM Systems and Technology Group, Yorktown Heights, NY, where he was involved in the research, development, and deployment of CAD tools and methodologies for IBM's high-end microprocessors. His current research interests include IoT platform prototyping; IoT communications; energy-efficient edge and cloud computing; power and thermal management of multi-core processors; low-power, embedded digital-signal processing; 3D integration; and CAD for VLSI, MEMS, and Silicon Photonics. Dr. Elfadel is the recipient of six Invention Achievement Awards, one Outstanding Technical Achievement Award and one Research Division Award, all from IBM, for his contributions in the area of VLSI CAD. He is the inventor or co-inventor of 50 issued US patents with several more pending. In 2014, he was the co-recipient of the D.~O. Pederson Best Paper Award from the IEEE Transactions on Computer-Aided Design for Integrated Circuits and Systems. Most recently, he received (with Prof. Mohammed Ismail) the SRC Board of Director Special Award for "pioneering semiconductor research in AbuDhabi." Dr. Elfadel is the co-editor of two Springer books: "3D Stacked Chips: From Emerging Processes to Heterogeneous Systems," 2016, and "The IoT Physical Layer: Design and Implementation," 2019. From 2009 to 2013, Dr. Elfadel served as an Associate Editor of the IEEE Transactions on Computer-Aided Design. He is currently serving as Associate Editor of the IEEE Transactions on VLSI Systems and on the Editorial Board of the Microelectronics Journal (Elsevier). Dr. Elfadel has also served on the Technical Program Committees of several leading conferences, including DAC, ICCAD, ASPDAC, DATE, ICCD, ICECS, and MWSCAS. Most recently, he was the General Co-chair of the IFIP/IEEE 25 th International Conference on Very Large Scale Integration (VLSI-SoC 2017), Abu Dhabi, UAE. He received his PhD from MIT in 1993.
Duane S. Boning is the Clarence J. LeBel Professor in Electrical Engineering, and Professor of Electrical Engineering and Computer Science in theEECS Department at MIT. He is affiliated with the MIT Microsystems Technology Laboratories, and serves as MTL Associate Director for Computation and CAD. From 2004 to 2011, he served as Associate Head of the EECS Department at MIT, from 2011 through 2013 as Director/Faculty Lead of the MIT Skoltech Initiative, and from 2011 to 2018 as Director of the MIT/Masdar Institute Cooperative Program. He is currently the Engineering Faculty Co-Director of the MIT Leaders for Global Operations (LGO) program. Dr. Boning received his S.B. degrees in electrical engineering and in computer science in 1984, and his S.M. and Ph.D. degrees in electrical engineering in 1986 and 1991, respectively, all from the Massachusetts Institute of Technology. He was an NSF Fellow from 1984 to 1989, and an Intel Graduate Fellow in 1990. From 1991 to 1993 he was a Member Technical Staff at the Texas Instruments Semiconductor Process and Design Center in Dallas, Texas, where he worked on semiconductor process representation, process/device simulation tool integration, and statistical modeling and optimization. Dr. Boning is a Fellow of the IEEE, and has served as Editor in Chief for the IEEE Transactions on Semiconductor Manufacturing. He is a member of the IEEE, Electrochemical Society, Eta Kappa Nu, Tau Beta Pi, Materials Research Society, Sigma Xi, and the Association of Computing Machinery.
Xin Li received the Ph.D. degree in Electrical & Computer Engineering from Carnegie Mellon University in 2005. He is currently a Professor in the ECE Department at Duke University and is leading the Institute of Applied Physical Sciences and Engineering and the Data Science Research Center at Duke Kunshan University. His research interests include integrated circuit, signal processing and data analytics. Dr. Li is the Deputy Editor-in-Chief of IEEE TCAD. He was an Associate Editor of IEEE TCAD, IEEE TBME, ACM TODAES, IEEE D&T and IET CPS. He was the General Chair of ISVLSI and FAC. He received the NSF CAREER Award in 2012 and six Best Paper Awards from IEEE TCAD, DAC, ICCAD and ISIC. He is a Fellow of IEEE.
Content
Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD.- Chapter2: Machine Learning for Compact Lithographic Process Models.- Chapter3: Machine Learning for Mask Synthesis.- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design.- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications.- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement.- Chapter7: Efficient Process Variation Characterization by Virtual Probe.- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement.- Chapter9: Machine Learning based Aging Analysis.- Chapter10: Extreme Statistics in Memories.- Chapter11: Fast Statistical Analysis Using Machine Learning.- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events.- Chapter13: Learning from Limited Data in VLSI CAD.- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion.- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits.- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling.- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems.- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors.- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle.- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine.- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.