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710	2		_aSpringerLink (Online service) _9106996
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008			190315s2019 gw a o |||| 0|eng d
020			_a9783030046668
024	7		_a10.1007/978-3-030-04666-8 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTK7874.75 _b2019 EB
245	0	0	_aMachine Learning in VLSI Computer-Aided Design _cedited by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XXII, 694 páginas) _b341 ilustraciones, 275 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
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	3		_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.
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _9670141 _aCircuitos integrados VLSI
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aBoning, Duane S. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aElfadel, Ibrahim (Abe) M. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _997072
700	1		_aLi, Xin. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030046651
776	0	8	_iPrinted edition: _z9783030046675
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-04666-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI