Advancing VLSI through Machine Learning: Innovations and Research Perspectives

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.

Table of Contents:
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