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This book describes machine learning-based new principles, methods of design and optimization of high-speed integrated circuits, included in one electronic system, which can exchange information between each other up to 128/256/512 Gbps speed. The efficiency of methods has been proven and is described on the examples of practical designs. This will enable readers to use them in similar electronic system designs. The author demonstrates newly developed principles and methods to accelerate communication between ICs, working in non-standard operating conditions, considering signal deviation compensation with linearity self-calibration. The observed circuit types also include but are not limited to mixed-signal, high performance heterogeneous integrated circuits as well as digital cores.
This book describes machine learning-based new principles, methods of design and optimization of high-speed integrated circuits, included in one electronic system, which can exchange information between each other up to 128/256/512 Gbps speed. The efficiency of methods has been proven and is described on the examples of practical designs. This will enable readers to use them in similar electronic system designs. The author demonstrates newly developed principles and methods to accelerate communication between ICs, working in non-standard operating conditions, considering signal deviation compensation with linearity self-calibration. The observed circuit types also include but are not limited to mixed-signal, high performance heterogeneous integrated circuits as well as digital cores.
This book introduces readers to a variety of tools for analog layout design automation. After discussing the placement and routing problem in electronic design automation (EDA), the authors overview a variety of automatic layout generation tools, as well as the most recent advances in analog layout-aware circuit sizing. The discussion includes different methods for automatic placement (a template-based Placer and an optimization-based Placer), a fully-automatic Router and an empirical-based Parasitic Extractor. The concepts and algorithms of all the modules are thoroughly described, enabling readers to reproduce the methodologies, improve the quality of their designs, or use them as starting point for a new tool. All the methods described are applied to practical examples for a 130nm design process, as well as placement and routing benchmark sets.
Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users. With up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers. It is also a very suitable text for graduate students interested in ML-based signal processing and networking. - Discusses the reasons behind the recent popularity of machine learning (ML) concepts in modern optical communication networks and the why/where/how ML can play a unique role - Presents fundamental ML techniques like artificial neural networks (ANNs), support vector machines (SVMs), K-means clustering, expectation-maximization (EM) algorithm, principal component analysis (PCA), independent component analysis (ICA), reinforcement learning, and more - Covers advanced deep learning (DL) methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) - - Individual chapters focus on ML applications in key areas of optical communications and networking
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
This book is based on the 18 tutorials presented during the 29th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on analog circuits for machine learning, current/voltage/temperature sensors, and high-speed communication via wireless, wireline, or optical links. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.
This book presents high-quality research papers presented at 4th International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering and Technology (ICSISCET 2022) held at Madhav Institute of Technology & Science (MITS), Gwalior, India, from November 19 to 20, 2022. The book extensively covers recent research in artificial intelligence (AI) that knit together nature-inspired algorithms, evolutionary computing, fuzzy systems, computational intelligence, machine learning, deep learning, etc., which is very useful while dealing with real problems due to their model-free structure, learning ability, and flexible approach. These techniques mimic human thinking and decision-making abilities to produce systems that are intelligent, efficient, cost-effective, and fast. The book provides a friendly and informative treatment of the topics which makes this book an ideal reference for both beginners and experienced researchers.
Mineral processing deals with complex particle systems with two-, three- and more phases. The modeling and understanding of these systems are a challenge for research groups and a need for the industrial sector. This Special Issue aims to present new advances, methodologies, applications, and case studies of computer-aided analysis applied to multiphase systems in mineral processing. This includes aspects such as modeling, design, operation, optimization, uncertainty analysis, among other topics. The special issue contains a review article and eleven articles that cover different methodologies of modeling, design, optimization, and analysis in problems of adsorption, leaching, flotation, and magnetic separation, among others. Consequently, the topics covered are of interest to readers from academia and industry.
The first of two volumes in the Electronic Design Automation for Integrated Circuits Handbook, Second Edition, Electronic Design Automation for IC System Design, Verification, and Testing thoroughly examines system-level design, microarchitectural design, logic verification, and testing. Chapters contributed by leading experts authoritatively discuss processor modeling and design tools, using performance metrics to select microprocessor cores for integrated circuit (IC) designs, design and verification languages, digital simulation, hardware acceleration and emulation, and much more. New to This Edition: Major updates appearing in the initial phases of the design flow, where the level of abstraction keeps rising to support more functionality with lower non-recurring engineering (NRE) costs Significant revisions reflected in the final phases of the design flow, where the complexity due to smaller and smaller geometries is compounded by the slow progress of shorter wavelength lithography New coverage of cutting-edge applications and approaches realized in the decade since publication of the previous edition—these are illustrated by new chapters on high-level synthesis, system-on-chip (SoC) block-based design, and back-annotating system-level models Offering improved depth and modernity, Electronic Design Automation for IC System Design, Verification, and Testing provides a valuable, state-of-the-art reference for electronic design automation (EDA) students, researchers, and professionals.
Despite the fact that in the digital domain, designers can take full benefits of IPs and design automation tools to synthesize and design very complex systems, the analog designers’ task is still considered as a ‘handcraft’, cumbersome and very time consuming process. Thus, tremendous efforts are being deployed to develop new design methodologies in the analog/RF and mixed-signal domains. This book collects 16 state-of-the-art contributions devoted to the topic of systematic design of analog, RF and mixed signal circuits. Divided in the two parts Methodologies and Techniques recent theories, synthesis techniques and design methodologies, as well as new sizing approaches in the field of robust analog and mixed signal design automation are presented for researchers and R/D engineers.