Advancing VLSI through Machine Learning

Innovations and Research Perspectives
Herausgeber: Tripathi, Abhishek Narayan; Tayal, Shubham; Padhy, Jagana Bihari; Singh, Indrasen; Singh, Ghanshyam

• Gebundenes Buch

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 304
• Erscheinungstermin: 14. März 2025
• Englisch
• Abmessung: 234mm x 156mm
• ISBN-13: 9781032774282
• ISBN-10: 1032774282
• Artikelnr.: 71551531

Autorenporträt

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.

Inhaltsangabe

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