Advancing VLSI through Machine Learning

Auteur

Dr. Abhishek N. Tripathi is an Assistant Professor in Micro and Nanoelectronics department, School of Electronics at VIT Vellore, Tamil Nadu, India. He holds a Ph.D. in ECE with a specialization in VLSI design and Embedded Technology from MANIT-Bhopal. His research work includes the development of methodologies for dynamic power and leakage power estimation in FPGA and ASIC-based implementations, VLSI system design, AI, deep learning, and microprocessor architecture.

Dr. Jagana Bihari Padhy is an Assistant Professor in the Department of Embedded Technology, School of Electronics at VIT Vellore, Tamil Nadu, India. He holds a Ph.D. in ECE with a specialization in Optical wireless system design from IIIT Bhubaneswar. His research work includes the development of Optical system design both in wired and wireless methodologies for the next generation of communication 5G and beyond.

Dr. Indrasen Singh is an Assistant Professor (Sr. Grade-2) in the Department of Embedded Technology under School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. His research interests are in the areas of cooperative communication, stochastic geometry, modeling of wireless networks, heterogeneous networks, millimeter wave communications, Device-to-Device communication, and 5G/6G communication.

Dr. Shubham Tayal is an Assistant Professor in the Department of Electronics and Communication Engineering at SR University, Warangal, India. He has more than 6 Years of academic/research experience of teaching at UG and PG level. He received his Ph.D in Microelectronics & VLSI Design from National Institute of Technology, Kurukshetra, M.Tech (VLSI Design) from YMCA university of Science and Technology, Faridabad and B.Tech (Electronics and Communication Engineering) from MDU, Rohtak. His research interests include simulation and modelling of Multi-gate semiconductor devices, Device-Circuit co-design in digital/analog domain, machine learning and IOT.

Prof. Ghanshyam Singh received a PhD degree in Electronics Engineering from the Indian Institute of Technology, Banaras Hindu University, Varanasi, India, in 2000. At present, he is a full Professor with the Department of Electrical and Electronics Engineering, APK Campus, University of Johannesburg, South Africa. His research and teaching interests include RF/Microwave Engineering, Millimeter/THz Wave Antennas and their Applications in Communication and Imaging, Next-Generation Communication Systems (OFDM and Cognitive Radio), and Nanophotonics. He has more than 19 years of teaching and research experience in Electromagnetic/Microwave Engineering, Wireless Communication and Nanophotonics.

Texte du rabat

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.

Contenu

Chapter 1. Foundations of VLSI and Machine Learning

• Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design Efficiency Chapter 2. Physical Processes Analysis and Phenomena: Insights into Trapping in AlGaN/GaN HEMTs

• 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

• Framework for Design and Performance Evaluation of Memory using Memristor Chapter 4. Innovative Design and Optimization of High-Power Amplifiers

• 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

• Exploring FPGA Architecture Designs for Matrix Multiplication in Machine Learning Chapter 6. Silicon Chip Design and Testing

• Silicon Chip Design and Testing Chapter 7. A Novel Deep Learning Approach for Early Brain Tumour Detection

• A Novel Deep Learning Approach for Early Brain Tumour Detection Chapter 8. TCAD Augmented Machine Learning for Prediction of Device Behavior and Failure Analysis

• TCAD Augmented Machine Learning for the Prediction of Device Behaviour and Failure Analysis Chapter 9. Opportunities and Challenges for ML-Based FPGA Backend Flow

• Opportunities and Challenges for ML-Based FPGA Backend Flow Chapter 10. Role of Machine Learning Applications in VLSI Design

• Role of Machine Learning Applications in VLSI Design Chapter 11. Application of Artificial Intelligence/Machine Learning in VLSI Design

• Application of Artificial Intelligence/Machine Learning in VLSI Design Chapter 12. FinFET-Based 9T SRAM for Enhanced Performance in AI/ML Applications

• 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

• 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

• Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and Utilisation Chapter 15. VLSI Realization of Smart Systems using Blockchain and Fog Computing

• VLSI Realization of Smart Systems using Blockchain and Fog Computing