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.
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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.
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Chapter 1. Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design EfficiencyChapter 2. Study of Physical Processes Analysis and Phenomena of Insights of Trapping in the Performance Degradation in AlGaN/GaN HEMTsChapter 3. Framework for Design and Performance Evaluation of Memory using MemristorChapter 4. Innovative Design and Optimization of High-Power Amplifiers: A Comparative Study with GaN HEMT and CMOS TechnologiesChapter 5. Exploring FPGA Architecture Designs for Matrix Multiplication in Machine LearningChapter 6. Silicon Chip Design and TestingChapter 7. A Novel Deep Learning Approach for Early Brain Tumour DetectionChapter 8. TCAD Augmented Machine Learning for the Prediction of Device Behavior and Failure AnalysisChapter 9. Opportunities and Challenges for ML-Based FPGA Backend FlowChapter 10. Role of Machine Learning Applications in VLSI DesignChapter 11. Application of Artificial Intelligence/Machine Learning in VLSI DesignChapter 12. FinFET-Based 9T SRAM for Enhanced Performance in AI/ML ApplicationsChapter 13. Power Consumption and SNM Analysis of 6T and 7T SRAM using 90nm TechnologyChapter 14. Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and UtilisationChapter 15. VLSI Realization of Smart Systems using Blockchain and Fog Computing
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Produktdetaljer

ISBN
9781032774282
Publisert
2025-03-31
Utgiver
Vendor
CRC Press
Høyde
234 mm
Bredde
156 mm
Aldersnivå
G, U, P, 01, 05, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
254

Om bidragsyterne

Dr. Abhishek Narayan Tripathi is currently an Assistant Professor in the Department
of Micro and Nanoelectronics, School of Electronics Engineering, Vellore Institute
of Technology, 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 Engineering, Vellore Institute of Technology,
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, School of Electronics Engineering, Vellore Institute of Technology, Vellore,
Tamil Nadu, India. His research interests are in the areas of cooperative communication,
stochastic geometry, modelling of wireless networks, heterogeneous networks, millimetre
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, SR University, Warangal, India. He has more than
6 years of academic/research experience in teaching at the UG and PG levels. He
received his Ph.D. in Microelectronics and VLSI Design from the 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/
analogue domain, ML, and Internet of Things.

Prof. Ghanshyam Singh received a Ph.D. 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, millimetre/
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.