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Full Description
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.
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.
