VLSI and Hardware Implementations using Modern Machine Learning Methods
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.

Machine Learning in VLSI Computer-Aided Design
Machine Learning in VLSI Computer-Aided Design

Author: Ibrahim (Abe) M. Elfadel

Publisher: Springer

Published: 2019-03-15

Total Pages: 697

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

FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks

Author: Amos R. Omondi

Publisher: Springer Science & Business Media

Published: 2006-10-04

Total Pages: 365

ISBN-13: 0387284877

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During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

Counterfeit Integrated Circuits
Counterfeit Integrated Circuits

Author: Mark (Mohammad) Tehranipoor

Publisher: Springer

Published: 2015-02-12

Total Pages: 282

ISBN-13: 3319118242

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This timely and exhaustive study offers a much-needed examination of the scope and consequences of the electronic counterfeit trade. The authors describe a variety of shortcomings and vulnerabilities in the electronic component supply chain, which can result in counterfeit integrated circuits (ICs). Not only does this book provide an assessment of the current counterfeiting problems facing both the public and private sectors, it also offers practical, real-world solutions for combatting this substantial threat. · Helps beginners and practitioners in the field by providing a comprehensive background on the counterfeiting problem; · Presents innovative taxonomies for counterfeit types, test methods, and counterfeit defects, which allows for a detailed analysis of counterfeiting and its mitigation; · Provides step-by-step solutions for detecting different types of counterfeit ICs; · Offers pragmatic and practice-oriented, realistic solutions to counterfeit IC detection and avoidance, for industry and government.

Introduction to Machine Learning
Introduction to Machine Learning

Author: Ethem Alpaydin

Publisher: MIT Press

Published: 2014-08-22

Total Pages: 639

ISBN-13: 0262028182

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Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Digital Integrated Circuit Design
Digital Integrated Circuit Design

Author: Hubert Kaeslin

Publisher: Cambridge University Press

Published: 2008-04-28

Total Pages: 878

ISBN-13: 0521882672

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This practical, tool-independent guide to designing digital circuits takes a unique, top-down approach, reflecting the nature of the design process in industry. Starting with architecture design, the book comprehensively explains the why and how of digital circuit design, using the physics designers need to know, and no more.

Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks

Author: Vivienne Sze

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 254

ISBN-13: 3031017668

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This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Digital VLSI Systems Design
Digital VLSI Systems Design

Author: Seetharaman Ramachandran

Publisher: Springer Science & Business Media

Published: 2007-06-14

Total Pages: 708

ISBN-13: 1402058292

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This book provides step-by-step guidance on how to design VLSI systems using Verilog. It shows the way to design systems that are device, vendor and technology independent. Coverage presents new material and theory as well as synthesis of recent work with complete Project Designs using industry standard CAD tools and FPGA boards. The reader is taken step by step through different designs, from implementing a single digital gate to a massive design consuming well over 100,000 gates. All the design codes developed in this book are Register Transfer Level (RTL) compliant and can be readily used or amended to suit new projects.