Paperback. 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-learningbased 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 designDiscusses hardware implementation and device modeling pertaining to machine learning algorithmsExplores machine learning for various VLSI architectures and reconfigurable computingIllustrates the latest techniques for device size and feature optimizationHighlights the latest case studies and reviews of the methods used for hardware implementationThis book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems. This book aims to provide the latest machine learning based methods, algorithms, architectures, and frameworks designed for VLSI design with focus on digital, analog and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: VLSI and Hardware Implementations using Modern Machine Learning Methods (Paperback)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 10: 1032061723
ISBN 13: 9781032061726
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

This book aims to provide the latest machine learning based methods, algorithms, architectures, and frameworks designed for VLSI design with focus on digital, analog and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas.

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