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VLSI and Hardware Implementations using Modern Machine Learning Methods

TaschenbuchKartoniert, Paperback
312 Seiten
Englisch
Taylor & Francis Ltderscheint am07.10.2024
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.mehr
Verfügbare Formate
BuchGebunden
EUR162,50
TaschenbuchKartoniert, Paperback
EUR58,50
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EUR62,49
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EUR62,49

Produkt

KlappentextThis 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.
Details
ISBN/GTIN978-1-032-06172-6
ProduktartTaschenbuch
EinbandartKartoniert, Paperback
Erscheinungsjahr2024
Erscheinungsdatum07.10.2024
Seiten312 Seiten
SpracheEnglisch
MasseBreite 156 mm, Höhe 234 mm
Artikel-Nr.17327205

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Autor