Machine Learning in VLSI Computer-Aided Design

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 pr ediction, 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 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 本书为读者提供了在超大规模集成电路（VLSI）的计算机辅助设计（CAD）环境中使用机器学习框架，方法，算法和技术的最新信息。覆盖范围包括光刻，物理设计，产量预测，硅后性能分析，可靠性和故障分析，功率和热分析，模拟设计，逻辑综合，验证和神经形态设计中使用的各种机器学习方法。 提供有关VLSI CAD机器学习的最新信息，用于器件建模，布局验证，良率预测，硅后验证和可靠性; 讨论在模拟和数字合成的背景下机器学习技术的使用; 演示如何将VLSI CAD目标制定为机器学习问题，并提供对其有效解决方案的综合处理; 讨论了收集数据的成本和预测精度之间的权衡，并提供了使用先前数据来降低模拟和数字VLSI设计的设计，测试和验证中的数据收集成本的方法。 从前言 随着半导体行业迎合认知系统和边缘智能的不断膨胀，本书可以作为我们的认知结构和方法与硬件架构和技术之间存在的渗透的预兆和例子。支持他们，另一方面......当我们从计算时代过渡到认知时代时，我们应该记住VLSI CAD的成功故事，并认真寻求隐形手的帮助，以便我们未来的认知系统习惯于设计更强大的认知系统。本书非常符合从计算到认知的这种持续过渡，我非常高兴地向所有积极参与这一令人兴奋的转变的人推荐它。 Ruchir Puri博士，IBM研究员，IBM Watson首席技术官兼首席架构师，IBM T. J. Watson研究中心 ediction, 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 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 本书为读者提供了在超大规模集成电路（VLSI）的计算机辅助设计（CAD）环境中使用机器学习框架，方法，算法和技术的最新信息。覆盖范围包括光刻，物理设计，产量预测，硅后性能分析，可靠性和故障分析，功率和热分析，模拟设计，逻辑综合，验证和神经形态设计中使用的各种机器学习方法。 提供有关VLSI CAD机器学习的最新信息，用于器件建模，布局验证，良率预测，硅后验证和可靠性; 讨论在模拟和数字合成的背景下机器学习技术的使用; 演示如何将VLSI CAD目标制定为机器学习问题，并提供对其有效解决方案的综合处理; 讨论了收集数据的成本和预测精度之间的权衡，并提供了使用先前数据来降低模拟和数字VLSI设计的设计，测试和验证中的数据收集成本的方法。 从前言 随着半导体行业迎合认知系统和边缘智能的不断膨胀，本书可以作为我们的认知结构和方法与硬件架构和技术之间存在的渗透的预兆和例子。支持他们，另一方面......当我们从计算时代过渡到认知时代时，我们应该记住VLSI CAD的成功故事，并认真寻求隐形手的帮助，以便我们未来的认知系统习惯于设计更强大的认知系统。本书非常符合从计算到认知的这种持续过渡，我非常高兴地向所有积极参与这一令人兴奋的转变的人推荐它。 Ruchir Puri博士，IBM研究员，IBM Watson首席技术官兼首席架构师，IBM T. J. Watson研究中心