VLSI and Hardware Implementations using Modern Machine Learning Methods

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.

List of contents

1. VLSI and Hardware Implementation Using Machine Learning Methods: A Systematic Literature Review. 2. Machine Learning for Testing of VLSI Circuit. 3. Online Checkers to Detect Hardware Trojans in AES Hardware Accelerators. 4. Machine Learning Methods for Hardware Security. 5. Application Driven Fault Identification in NoC Designs. 6. Online Test Derived from Binary Neural Network for Critical Autonomous Automotive Hardware. 7. Applications of Machine Learning in VLSI Design. 8. An Overview of High-Performance Computing Techniques Applied to Image Processing. 9. Machine Learning Algorithms for Semiconductor Device Modeling. 10. Securing IoT-Based Microservices Using Artificial Intelligence. 11. Applications of the Approximate Computing on ML Architecture. 12. Hardware Realization of Reinforcement Learning Algorithms for Edge Devices. 13. Deep Learning Techniques for Side-Channel Analysis. 14. Machine Learning in Hardware Security of IoT Nodes. 15. Integrated Photonics for Artificial Intelligence Applications.

Summary

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.

Product details

• Assisted by: Kusum Lata (Editor), Sandeep Saini (Editor), Saini Sandeep (Editor), G.R. Sinha (Editor)
• Publisher: Taylor and Francis
• Languages: English
• Product format: Paperback / Softback
• Released: 07.10.2024
• EAN: 9781032061726
• ISBN: 978-1-032-06172-6
• No. of pages: 312
• Weight: 500 g
• Illustrations: schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; machine learning, TECHNOLOGY & ENGINEERING / Electronics / Circuits / General, TECHNOLOGY & ENGINEERING / Electrical, COMPUTERS / Machine Theory, Electrical Engineering, Mathematical theory of computation, COMPUTERS / Data Science / Machine Learning, Electronics: circuits and components, Circuits & components