| ISBN13: | 9781032774282 |
| ISBN10: | 1032774282 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 264 oldal |
| Méret: | 234x156 mm |
| Nyelv: | angol |
| Illusztrációk: | 141 Illustrations, black & white; 17 Halftones, black & white; 124 Line drawings, black & white; 27 Tables, black & white |
| 700 |
Advancing VLSI through Machine Learning
GBP 130.00
Kattintson ide a feliratkozáshoz
This book explores the synergy between VLSI and Machine Learning and its applications across various domains. It will investigate how Machine Learning techniques can enhance the design and testing of VLSI circuits, improve power efficiency, optimize layouts, and enable novel architectures.
This book explores the synergy between very large-scale integration (VLSI) and machine learning (ML) and its applications across various domains. It investigates how ML techniques can enhance the design and testing of VLSI circuits, improve power efficiency, optimize layouts, and enable novel architectures.
This book bridges the gap between VLSI and ML, showcasing the potential of this integration in creating innovative electronic systems, advancing computing capabilities, and paving the way for a new era of intelligent devices and technologies. Additionally, it covers how VLSI technologies can accelerate ML algorithms, enabling more efficient and powerful data processing and inference engines. It explores both hardware and software aspects, covering topics like hardware accelerators, custom hardware for specific ML tasks, and ML-driven optimization techniques for chip design and testing.
This book will be helpful for academicians, researchers, postgraduate students and those working in ML-driven VLSI.
Chapter 1. Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design Efficiency
Chapter 2. Study of Physical Processes Analysis and Phenomena of Insights of Trapping in the Performance Degradation in AlGaN/GaN HEMTs
Chapter 3. Framework for Design and Performance Evaluation of Memory using Memristor
Chapter 4. Innovative Design and Optimization of High-Power Amplifiers: A Comparative Study with GaN HEMT and CMOS Technologies
Chapter 5. Exploring FPGA Architecture Designs for Matrix Multiplication in Machine Learning
Chapter 6. Silicon Chip Design and Testing
Chapter 7. A Novel Deep Learning Approach for Early Brain Tumour Detection
Chapter 8. TCAD Augmented Machine Learning for the Prediction of Device Behavior and Failure Analysis
Chapter 9. Opportunities and Challenges for ML-Based FPGA Backend Flow
Chapter 10. Role of Machine Learning Applications in VLSI Design
Chapter 11. Application of Artificial Intelligence/Machine Learning in VLSI Design
Chapter 12. FinFET-Based 9T SRAM for Enhanced Performance in AI/ML Applications
Chapter 13. Power Consumption and SNM Analysis of 6T and 7T SRAM using 90nm Technology
Chapter 14. Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and Utilisation
Chapter 15. VLSI Realization of Smart Systems using Blockchain and Fog Computing