VLSI and Hardware Implementations using Modern Machine Learning Methods

Synopsis

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.

Copyright:

2022

Book Details

Book Quality:

Publisher Quality

Book Size:

312 Pages

ISBN-13:

9781000523843

Related ISBNs:

9781032061719, 9781032061726, 9781003201038

Publisher:

CRC Press

Date of Addition:

02/06/23

Copyrighted By:

selection and editorial matter, Sandeep Saini, Kusum Lata and G.R. Sinha

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Technology

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Sandeep Saini

Edited by:

Kusum Lata

Edited by:

G.R. Sinha