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020 _a9783030046668
_9978-3-030-04666-8
024 7 _a10.1007/978-3-030-04666-8
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
_2bisacsh
072 7 _aTJFC
_2thema
082 0 4 _a621.3815
_223
245 1 0 _aMachine Learning in VLSI Computer-Aided Design
_h[electronic resource] /
_cedited by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXXII, 694 p. 341 illus., 275 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter1: 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.
520 _aThis 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.
650 0 _aElectronic circuits.
_919581
650 0 _aMicroprocessors.
_940776
650 0 _aComputer architecture.
_93513
650 0 _aLogic design.
_93686
650 1 4 _aElectronic Circuits and Systems.
_940777
650 2 4 _aProcessor Architectures.
_940778
650 2 4 _aLogic Design.
_93686
700 1 _aElfadel, Ibrahim (Abe) M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_940779
700 1 _aBoning, Duane S.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_940780
700 1 _aLi, Xin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_940781
710 2 _aSpringerLink (Online service)
_940782
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030046651
776 0 8 _iPrinted edition:
_z9783030046675
856 4 0 _uhttps://doi.org/10.1007/978-3-030-04666-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76809
_d76809