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Machine Learning in VLSI Computer-Aided Design

Author: Ibrahim (Abe) M. Elfadel

Publisher: Springer

Published: 2019-03-15

Total Pages: 694

ISBN-13: 3030046664

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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

Book Synopsis Machine Learning in VLSI Computer-Aided Design by : Ibrahim (Abe) M. Elfadel

Download or read book Machine Learning in VLSI Computer-Aided Design written by Ibrahim (Abe) M. Elfadel and published by Springer. This book was released on 2019-03-15 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Machine Learning in VLSI Computer-aided Design

Author: Ibrahim (Abe) M. Elfadel

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9783030046675

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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.

Book Synopsis Machine Learning in VLSI Computer-aided Design by : Ibrahim (Abe) M. Elfadel

Download or read book Machine Learning in VLSI Computer-aided Design written by Ibrahim (Abe) M. Elfadel and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

VLSI and Hardware Implementations using Modern Machine Learning Methods

Author: Sandeep Saini

Publisher: CRC Press

Published: 2021-12-30

Total Pages: 329

ISBN-13: 1000523810

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Provides the details of state-of-the-art machine learning methods used in VLSI Design. Discusses hardware implementation and device modeling pertaining to machine learning algorithms. Explores machine learning for various VLSI architectures and reconfigurable computing. Illustrate latest techniques for device size and feature optimization. Highlight latest case studies and reviews of the methods used for hardware implementation.

Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini

VLSI and Hardware Implementations using Modern Machine Learning Methods

Author: Sandeep Saini

Publisher: CRC Press

Published: 2021-12-31

Total Pages: 292

ISBN-13: 1000523845

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Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini

Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

Mobile Computing and Sustainable Informatics

Author: Subarna Shakya

Publisher: Springer Nature

Published: 2021-07-22

Total Pages: 875

ISBN-13: 9811618666

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This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2021) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during 29–30 January 2021. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices, to personalized, embedded and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models and advanced informatics that supports the symbiosis of mobile computing and sustainable informatics.

Book Synopsis Mobile Computing and Sustainable Informatics by : Subarna Shakya

Download or read book Mobile Computing and Sustainable Informatics written by Subarna Shakya and published by Springer Nature. This book was released on 2021-07-22 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2021) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during 29–30 January 2021. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices, to personalized, embedded and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models and advanced informatics that supports the symbiosis of mobile computing and sustainable informatics.

VLSI CAD Tools and Applications

Author: Wolfgang Fichtner

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 555

ISBN-13: 1461319854

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The summer school on VLSf GAD Tools and Applications was held from July 21 through August 1, 1986 at Beatenberg in the beautiful Bernese Oberland in Switzerland. The meeting was given under the auspices of IFIP WG 10. 6 VLSI, and it was sponsored by the Swiss Federal Institute of Technology Zurich, Switzerland. Eighty-one professionals were invited to participate in the summer school, including 18 lecturers. The 81 participants came from the following countries: Australia (1), Denmark (1), Federal Republic of Germany (12), France (3), Italy (4), Norway (1), South Korea (1), Sweden (5), United Kingdom (1), United States of America (13), and Switzerland (39). Our goal in the planning for the summer school was to introduce the audience into the realities of CAD tools and their applications to VLSI design. This book contains articles by all 18 invited speakers that lectured at the summer school. The reader should realize that it was not intended to publish a textbook. However, the chapters in this book are more or less self-contained treatments of the particular subjects. Chapters 1 and 2 give a broad introduction to VLSI Design. Simulation tools and their algorithmic foundations are treated in Chapters 3 to 5 and 17. Chapters 6 to 9 provide an excellent treatment of modern layout tools. The use of CAD tools and trends in the design of 32-bit microprocessors are the topics of Chapters 10 through 16. Important aspects in VLSI testing and testing strategies are given in Chapters 18 and 19.

Book Synopsis VLSI CAD Tools and Applications by : Wolfgang Fichtner

Download or read book VLSI CAD Tools and Applications written by Wolfgang Fichtner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: The summer school on VLSf GAD Tools and Applications was held from July 21 through August 1, 1986 at Beatenberg in the beautiful Bernese Oberland in Switzerland. The meeting was given under the auspices of IFIP WG 10. 6 VLSI, and it was sponsored by the Swiss Federal Institute of Technology Zurich, Switzerland. Eighty-one professionals were invited to participate in the summer school, including 18 lecturers. The 81 participants came from the following countries: Australia (1), Denmark (1), Federal Republic of Germany (12), France (3), Italy (4), Norway (1), South Korea (1), Sweden (5), United Kingdom (1), United States of America (13), and Switzerland (39). Our goal in the planning for the summer school was to introduce the audience into the realities of CAD tools and their applications to VLSI design. This book contains articles by all 18 invited speakers that lectured at the summer school. The reader should realize that it was not intended to publish a textbook. However, the chapters in this book are more or less self-contained treatments of the particular subjects. Chapters 1 and 2 give a broad introduction to VLSI Design. Simulation tools and their algorithmic foundations are treated in Chapters 3 to 5 and 17. Chapters 6 to 9 provide an excellent treatment of modern layout tools. The use of CAD tools and trends in the design of 32-bit microprocessors are the topics of Chapters 10 through 16. Important aspects in VLSI testing and testing strategies are given in Chapters 18 and 19.

VLSI Design of Neural Networks

Author: Ulrich Ramacher

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 346

ISBN-13: 1461539943

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The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.

Book Synopsis VLSI Design of Neural Networks by : Ulrich Ramacher

Download or read book VLSI Design of Neural Networks written by Ulrich Ramacher and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.

Opto-VLSI Devices and Circuits for Biomedical and Healthcare Applications

Author: Ankur Kumar

Publisher: CRC Press

Published: 2023-09-04

Total Pages: 250

ISBN-13: 1000932443

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The text comprehensively discusses the latest Opto-VLSI devices and circuits useful for healthcare and biomedical applications. It further emphasizes the importance of smart technologies such as artificial intelligence, machine learning, and the internet of things for the biomedical and healthcare industries. Discusses advanced concepts in the field of electro-optics devices for medical applications. Presents optimization techniques including logical effort, particle swarm optimization and genetic algorithm to design Opto-VLSI devices and circuits. Showcases the concepts of artificial intelligence and machine learning for smart medical devices and data auto-collection for distance treatment. Covers advanced Opto-VLSI devices including a field-effect transistor and optical sensors, spintronic and photonic devices. Highlights application of flexible electronics in health monitoring and artificial intelligence integration for better medical devices. The text presents the advances in the fields of optics and VLSI and their applicability in diverse areas including biomedical engineering and the healthcare sector. It covers important topics such as FET biosensors, optical biosensors and advanced optical materials. It further showcases the significance of smart technologies such as artificial intelligence, machine learning and the internet of things for the biomedical and healthcare industries. It will serve as an ideal design book for senior undergraduate, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer engineering and biomedical engineering.

Book Synopsis Opto-VLSI Devices and Circuits for Biomedical and Healthcare Applications by : Ankur Kumar

Download or read book Opto-VLSI Devices and Circuits for Biomedical and Healthcare Applications written by Ankur Kumar and published by CRC Press. This book was released on 2023-09-04 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: The text comprehensively discusses the latest Opto-VLSI devices and circuits useful for healthcare and biomedical applications. It further emphasizes the importance of smart technologies such as artificial intelligence, machine learning, and the internet of things for the biomedical and healthcare industries. Discusses advanced concepts in the field of electro-optics devices for medical applications. Presents optimization techniques including logical effort, particle swarm optimization and genetic algorithm to design Opto-VLSI devices and circuits. Showcases the concepts of artificial intelligence and machine learning for smart medical devices and data auto-collection for distance treatment. Covers advanced Opto-VLSI devices including a field-effect transistor and optical sensors, spintronic and photonic devices. Highlights application of flexible electronics in health monitoring and artificial intelligence integration for better medical devices. The text presents the advances in the fields of optics and VLSI and their applicability in diverse areas including biomedical engineering and the healthcare sector. It covers important topics such as FET biosensors, optical biosensors and advanced optical materials. It further showcases the significance of smart technologies such as artificial intelligence, machine learning and the internet of things for the biomedical and healthcare industries. It will serve as an ideal design book for senior undergraduate, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer engineering and biomedical engineering.

Automatic Programming Applied to VLSI CAD Software: A Case Study

Author: Dorothy E. Setliff

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 237

ISBN-13: 1461315514

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This book, and the research it describes, resulted from a simple observation we made sometime in 1986. Put simply, we noticed that many VLSI design tools looked "alike". That is, at least at the overall software architecture level, the algorithms and data structures required to solve problem X looked much like those required to solve problem X'. Unfortunately, this resemblance is often of little help in actually writing the software for problem X' given the software for problem X. In the VLSI CAD world, technology changes rapidly enough that design software must continually strive to keep up. And of course, VLSI design software, and engineering design software in general, is often exquisitely sensitive to some aspects of the domain (technology) in which it operates. Modest changes in functionality have an unfortunate tendency to require substantial (and time-consuming) internal software modifications. Now, observing that large engineering software systems are technology dependent is not particularly clever. However, we believe that our approach to xiv Preface dealing with this problem took an interesting new direction. We chose to investigate the extent to which automatic programming ideas cold be used to synthesize such software systems from high-level specifications. This book is one of the results of that effort.

Book Synopsis Automatic Programming Applied to VLSI CAD Software: A Case Study by : Dorothy E. Setliff

Download or read book Automatic Programming Applied to VLSI CAD Software: A Case Study written by Dorothy E. Setliff and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, and the research it describes, resulted from a simple observation we made sometime in 1986. Put simply, we noticed that many VLSI design tools looked "alike". That is, at least at the overall software architecture level, the algorithms and data structures required to solve problem X looked much like those required to solve problem X'. Unfortunately, this resemblance is often of little help in actually writing the software for problem X' given the software for problem X. In the VLSI CAD world, technology changes rapidly enough that design software must continually strive to keep up. And of course, VLSI design software, and engineering design software in general, is often exquisitely sensitive to some aspects of the domain (technology) in which it operates. Modest changes in functionality have an unfortunate tendency to require substantial (and time-consuming) internal software modifications. Now, observing that large engineering software systems are technology dependent is not particularly clever. However, we believe that our approach to xiv Preface dealing with this problem took an interesting new direction. We chose to investigate the extent to which automatic programming ideas cold be used to synthesize such software systems from high-level specifications. This book is one of the results of that effort.

Machine Learning Applications in Electronic Design Automation

Author: Haoxing Ren

Publisher: Springer Nature

Published: 2023-01-01

Total Pages: 585

ISBN-13: 303113074X

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This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.

Book Synopsis Machine Learning Applications in Electronic Design Automation by : Haoxing Ren

Download or read book Machine Learning Applications in Electronic Design Automation written by Haoxing Ren and published by Springer Nature. This book was released on 2023-01-01 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.