Machine Learning in VLSI Computer-Aided Design - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in VLSI Computer-Aided Design write by Ibrahim (Abe) M. Elfadel. This book was released on 2019-03-15. Machine Learning in VLSI Computer-Aided Design available in PDF, EPUB and Kindle. 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
Machine Learning in VLSI Computer-aided Design - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in VLSI Computer-aided Design write by Ibrahim (Abe) M. Elfadel. This book was released on 2019. Machine Learning in VLSI Computer-aided Design available in PDF, EPUB and Kindle. 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 for VLSI Computer Aided Design
Machine Learning for VLSI Computer Aided Design - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for VLSI Computer Aided Design write by Mohamed Baker Alawieh. This book was released on 2020. Machine Learning for VLSI Computer Aided Design available in PDF, EPUB and Kindle. Consumer electronics have become an integral part of people’s life putting at their disposal immense computational power that provides numerous applications. This has been enabled by the ceaseless down scaling of integrated circuit (IC) technologies which keeps pushing the performance boundary. Such scaling continues to drive, as a byproduct, an up scale in the challenges associated with circuit design and manufacturability. Among the major challenges facing modern IC Computer Aided Design (CAD) are those related to manufacturing and yield which are manifested through: (1) expensive modeling and simulation (e.g. large and complex designs); (2) entangled design and manufacturability (e.g., yield sensitive to design patterns); and (3) strict design constraints (e.g., high yield). While these challenges associated with retaining the robustness of modern designs continue to exacerbate, Very Large-Scale Integration (VLSI) CAD is becoming more critical, yet more challenging. Parallel to these developments are the recent advancements in Machine Learning (ML) which have altered the perception of computing. This dissertation attempts to address the aforementioned challenges in VLSI CAD through machine learning techniques. Our research includes efficient analog modeling, learning-assisted physical design and yield analysis, and model adaptation schemes tailored to the ever-changing IC environment. With aggressive scaling, process variation manifests itself among the most prominent factors limiting the yield of analog and mixed-signal (AMS) circuits. In modern ICs, the expensive simulation cost is one of the challenges facing accurate modeling of this variation. Our study develops a novel semi-supervised learning framework for AMS design modeling that is capable of significantly reducing the modeling cost. In addition, a new perspective towards incorporating sparsity in the modeling task is proposed. At the lithography stage, resolution enhancement techniques in general, and Sub Resolution Assist Feature (SRAF) insertion in particular, have become indispensable given the ever shrinking feature size. While different approaches have been proposed for SRAF insertion, the trade-off between efficiency and accuracy is still the governing principle. To address this, we recast the SRAF insertion process as an image translation task and propose a deep learning-based approach for efficient SRAF insertion. Besides, with complex designs, challenges at the physical design stage have exacerbated. Therefore, across-layers information sharing has become imperative for timely design closure. Particularly, in modern Field Programmable Gate Array (FPGA) place and route flows, leveraging routing congestion information during placement has demonstrated imperative benefit. Our study develops a new congestion prediction approach for large-scale FPGA designs that achieves superior prediction accuracy. Moreover, during fabrication, a critical first step towards improving production yield is to identify the underlying factors that contribute most to yield loss. And for that, wafer map defect analysis is a key. We present a novel wafer map defect pattern classification framework using confidence-aware deep selective learning. The use of ML for CAD tasks has the promise of delivering better performance and efficiency. However, one of the main characteristics of the field is that it is evolving with a fast rate of change. Therefore, by the time enough data is available to train accurate models under a given environment, changes start to occur. In this sense, the frequent restarts limit the returns on developing ML models. To address this, we develop a framework for the fast migration of classification models across different environments. Our approaches are validated with extensive experiments where they proved capable of advancing the VLSI CAD flow
VLSI and Hardware Implementations using Modern Machine Learning Methods
VLSI and Hardware Implementations using Modern Machine Learning Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook VLSI and Hardware Implementations using Modern Machine Learning Methods write by Sandeep Saini. This book was released on 2021-12-30. VLSI and Hardware Implementations using Modern Machine Learning Methods available in PDF, EPUB and Kindle. 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.
VLSI Physical Design: From Graph Partitioning to Timing Closure
VLSI Physical Design: From Graph Partitioning to Timing Closure - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook VLSI Physical Design: From Graph Partitioning to Timing Closure write by Andrew B. Kahng. This book was released on 2022-06-14. VLSI Physical Design: From Graph Partitioning to Timing Closure available in PDF, EPUB and Kindle. The complexity of modern chip design requires extensive use of specialized software throughout the process. To achieve the best results, a user of this software needs a high-level understanding of the underlying mathematical models and algorithms. In addition, a developer of such software must have a keen understanding of relevant computer science aspects, including algorithmic performance bottlenecks and how various algorithms operate and interact. This book introduces and compares the fundamental algorithms that are used during the IC physical design phase, wherein a geometric chip layout is produced starting from an abstract circuit design. This updated second edition includes recent advancements in the state-of-the-art of physical design, and builds upon foundational coverage of essential and fundamental techniques. Numerous examples and tasks with solutions increase the clarity of presentation and facilitate deeper understanding. A comprehensive set of slides is available on the Internet for each chapter, simplifying use of the book in instructional settings. “This improved, second edition of the book will continue to serve the EDA and design community well. It is a foundational text and reference for the next generation of professionals who will be called on to continue the advancement of our chip design tools and design the most advanced micro-electronics.” Dr. Leon Stok, Vice President, Electronic Design Automation, IBM Systems Group “This is the book I wish I had when I taught EDA in the past, and the one I’m using from now on.” Dr. Louis K. Scheffer, Howard Hughes Medical Institute “I would happily use this book when teaching Physical Design. I know of no other work that’s as comprehensive and up-to-date, with algorithmic focus and clear pseudocode for the key algorithms. The book is beautifully designed!” Prof. John P. Hayes, University of Michigan “The entire field of electronic design automation owes the authors a great debt for providing a single coherent source on physical design that is clear and tutorial in nature, while providing details on key state-of-the-art topics such as timing closure.” Prof. Kurt Keutzer, University of California, Berkeley “An excellent balance of the basics and more advanced concepts, presented by top experts in the field.” Prof. Sachin Sapatnekar, University of Minnesota
