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

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

• Format: ePub

Produktbeschreibung

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.

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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.