VLSI and hardware implementations using modern machine learning methods
- Boca Raton : CRC Press , 2022
- xv, 312 p. ; ill. 24 cm
Includes bibliographical references and index.
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. 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. It contains chapters on 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.
9781032061719
Machine learning VLSI Integrated circuits construction Data processing Approximate computing Binary convolutional neural network Circuit feature analysis Digital image processing Field programmable gate arrays(FPGAs) Hardware Trojans Look-up table(LuT) Photonic Neural network Reinforcement learning Supervised learning Threshold implementation Hardware security