VLSI and Hardware Implementations using Modern Machine Learning Methods (eBook, ePUB)

Redaktion: Saini, Sandeep; Sinha, G. R.; Lata, Kusum

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 328
• Erscheinungstermin: 30. Dezember 2021
• Englisch
• ISBN-13: 9781000523843
• Artikelnr.: 62946112

Inhaltsangabe

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.