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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.

Table of Contents

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