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 and Hardware Implementations using Modern Machine Learning Methods
RRP:
$333.00
Description
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.
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Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction...
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device...
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