1st Edition

VLSI and Hardware Implementations using Modern Machine Learning Methods

Edited By Sandeep Saini, Kusum Lata, G.R. Sinha Copyright 2022
    328 Pages 119 B/W Illustrations
    by CRC Press

    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.


    • 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.

    Chapter No. Chapter Title Authors
    1 VLSI and Hardware Implementation Using Machine Learning Methods: A Systematic Literature Review Kusum Lata, Sandeep Saini, G. R. Sinha
    2 Machine Learning for Testing of VLSI Circuit Abhishek Choubey, Shruti Bhargava Choubey
    3  Online Checkers to Detect Hardware Trojans in AES Hardware Accelerators Sree Ranjani Rajendran, Rajat Subhra Chakraborty
    4 Machine Learning Methods for Hardware Security Soma Saha, Bodhisatwa Mazumdar
    5 Application Driven Fault Identification in NoC Designs Ankur Gogoi and Bibhas Ghoshal
    6 Online Test Derived from Binary Neural Network for Critical Autonomous Automotive Hardware Dr. Philemon Daniel
    7 Applications of Machine Learning in VLSI Design Sneh Saurabh, Pranav Jain, Madhvi Agarwal, and OVS Shashank Ram
    8 An Overview of High-Performance Computing Techniques Applied to Image Processing Giulliano Paes Carnielli, Rangel Arthur, Ana Carolina Borges Monteiro, Reinaldo Padilha França, and Yuzo Iano
    9 Machine Learning Algorithms for Semiconductor Device Modeling Yogendra Gupta, Niketa Sharma, Ashish Sharma, Harish Sharma
    10 Securing IoT-Based Microservices Using Artificial Intelligence Sushant Kumar and Saurabh Mukherjee
    11 Applications of the Approximate Computing on ML Architecture Kattekola Naresh and Shubhankar Majumdar
    12 Hardware Realization of Reinforcement Learning Algorithms for Edge Devices Shaik Mohammed Waseem and Subir Kumar Roy
    13 Deep Learning Techniques for Side-Channel Analysis Varsha Satheesh Kumar, S. Dillibabu Shanmugam, and Dr. N. Sarat Chandra Babu
    14 Machine Learning in Hardware Security of IoT Nodes T Lavanya and K Rajalakshmi
    15 Integrated Photonics for Artificial Intelligence Applications Ankur Saharia, Kamal Kishor Choure, Nitesh Mudgal, Rahul Pandey, Dinesh Bhatia, Manish Tiwari, Ghanshyam Singh


    Sandeep Saini