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Advancing VLSI through Machine Learning: Innovations and Research Perspectives de Abhishek Narayan Tripathi

Advancing VLSI through Machine Learning: Innovations and Research Perspectives: Materials, Devices, and Circuits

Editat de Abhishek Narayan Tripathi, Jagana Bihari Padhy, Indrasen Singh, Shubham Tayal, Ghanshyam Singh
en Limba Engleză Hardback – 11 feb 2025
Advancing VLSI through Machine Learning: Innovations and Research Perspectives explores the synergy between VLSI and Machine Learning and its applications across various domains. It investigates how Machine Learning 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 Machine Learning, 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 Machine Learning 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.
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Specificații

ISBN-13: 9781032774282
ISBN-10: 1032774282
Pagini: 304
Ilustrații: 282
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Materials, Devices, and Circuits

Locul publicării:Boca Raton, United States

Public țintă

General, Postgraduate, Professional Reference, and Undergraduate Advanced

Cuprins

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

Notă biografică

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.
 

Descriere

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.