Machine Learning Techniques For Vlsi Chip Design


Machine Learning Techniques For Vlsi Chip Design
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Machine Learning Techniques For Vlsi Chip Design


Machine Learning Techniques For Vlsi Chip Design
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Author : Abhishek Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2023-06-26

Machine Learning Techniques For Vlsi Chip Design written by Abhishek Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Computers categories.


MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.



Machine Learning In Vlsi Computer Aided Design


Machine Learning In Vlsi Computer Aided Design
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Author : Ibrahim (Abe) M. Elfadel
language : en
Publisher: Springer
Release Date : 2019-03-15

Machine Learning In Vlsi Computer Aided Design written by Ibrahim (Abe) M. Elfadel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Technology & Engineering categories.


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


Vlsi And Hardware Implementations Using Modern Machine Learning Methods
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Author : Sandeep Saini
language : en
Publisher: CRC Press
Release Date : 2021-12-31

Vlsi And Hardware Implementations Using Modern Machine Learning Methods written by Sandeep Saini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-31 with Technology & Engineering categories.


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.



Machine Learning In Vlsi Computer Aided Design


Machine Learning In Vlsi Computer Aided Design
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Author : Ibrahim (Abe) M. Elfadel
language : en
Publisher:
Release Date : 2019

Machine Learning In Vlsi Computer Aided Design written by Ibrahim (Abe) M. Elfadel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Integrated circuits categories.


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 Applications In Electronic Design Automation


Machine Learning Applications In Electronic Design Automation
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Author : Haoxing Ren
language : en
Publisher: Springer Nature
Release Date : 2023-01-01

Machine Learning Applications In Electronic Design Automation written by Haoxing Ren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Technology & Engineering categories.


​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.



Handbook Of Vlsi Chip Design And Expert Systems


Handbook Of Vlsi Chip Design And Expert Systems
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Author : A. F. Schwarz
language : en
Publisher:
Release Date : 1993

Handbook Of Vlsi Chip Design And Expert Systems written by A. F. Schwarz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.


Offers a conceptual and methodological understanding of chip design, and of the fundamental principles in the computer-aided design of VLSI circuits and systems (CADCAS). The text covers where, why and how expert systems are used in subtasks of CADCAS, and in the integrated chip design system.



Handbook Of Vlsi Chip Design And Expert Systems


Handbook Of Vlsi Chip Design And Expert Systems
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Author : A. F. Schwarz
language : en
Publisher: Academic Press
Release Date : 2014-05-10

Handbook Of Vlsi Chip Design And Expert Systems written by A. F. Schwarz and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Technology & Engineering categories.


Handbook of VLSI Chip Design and Expert Systems provides information pertinent to the fundamental aspects of expert systems, which provides a knowledge-based approach to problem solving. This book discusses the use of expert systems in every possible subtask of VLSI chip design as well as in the interrelations between the subtasks. Organized into nine chapters, this book begins with an overview of design automation, which can be identified as Computer-Aided Design of Circuits and Systems (CADCAS). This text then presents the progress in artificial intelligence, with emphasis on expert systems. Other chapters consider the impact of design automation, which exploits the basic capabilities of computers to perform complex calculations and to handle huge amounts of data with a high speed and accuracy. This book discusses as well the characterization of microprocessors. The final chapter deals with interactive I/O devices. This book is a valuable resource for system design experts, circuit analysts and designers, logic designers, device engineers, technologists, and application-specific designers.



Machine Learning Support For Fault Diagnosis Of System On Chip


Machine Learning Support For Fault Diagnosis Of System On Chip
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Author : Patrick Girard
language : en
Publisher: Springer Nature
Release Date : 2023-03-13

Machine Learning Support For Fault Diagnosis Of System On Chip written by Patrick Girard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Technology & Engineering categories.


This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.



An Artificial Intelligence Approach To Vlsi Design


An Artificial Intelligence Approach To Vlsi Design
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Author : Thaddeus J. Kowalski
language : en
Publisher: Springer International Series in Engineering and Computer Science
Release Date : 1985-05-31

An Artificial Intelligence Approach To Vlsi Design written by Thaddeus J. Kowalski and has been published by Springer International Series in Engineering and Computer Science this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-05-31 with Computers categories.




Vlsi For Neural Networks And Artificial Intelligence


Vlsi For Neural Networks And Artificial Intelligence
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Author : Jose G. Delgado-Frias
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29

Vlsi For Neural Networks And Artificial Intelligence written by Jose G. Delgado-Frias and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Computers categories.


Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.