Machine Learning in Nanoelectronics

Ashish Maurya, PhD is an Assistant Professor in the Electronics and Communication Engineering Department and Assistant Dean of Research and Development at the Kanpur Institute of Technology. He has published nine journal articles and seven international conference proceedings. His current research interests include machine learning in semiconductor physics, nanoelectronics, and emerging semiconductor materials and their applications in various analog and digital circuits. Mandeep Singh is a Professor in the Electronics and Communication Engineering Department at the Indian Institute of Information Technology. He has published three books, five book chapters, and various research papers in international journals. His areas of research include semiconductor device modeling, memory design, and low-power VLSI design. Balwinder Raj, PhD is an Associate Professor at the National Institute of Technology Jalandhar. He has authored and co-authored ten books, 15 book chapters, and more than 150 research papers in peer-reviewed national and international journals and conferences. His areas of interest include classical and non-classical nanoscale semiconductor device modeling, nanoelectronics, FinFET-based memory design, and low-power VLSI design.

• Preface xiii1 Introduction to Machine Learning in Nanoelectronics 1Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary1.1 Introduction 21.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 41.3 Machine Learning in Nanoscale Device Simulation 111.4 Process Optimization in Semiconductor Manufacturing 211.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 251.6 Future Directions and Challenges 291.7 Conclusion 312 Machine Learning to Explore Opportunities in Quantum 43Jyoti Khandelwal2.1 Introduction to Quantum Opportunities 442.2 Understanding Quantum Data 462.3 Machine Learning Techniques for Quantum Applications 492.4 Case Studies and Applications 572.5 Tools and Frameworks for Implementation 602.6 Challenges and Opportunities in QML 632.7 Conclusion 633 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67Anshu Srivastava and Shakun Srivastava3.1 Introduction 693.2 Predictive Modelling and Machine Learning's Application in Cancer Diagnostics 693.3 Customized Medical Care 723.4 Result and Future Perspective 774 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni4.1 Introduction 904.2 Methodology 944.3 Simulation Results 964.4 Conclusion 1045 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik5.1 Introduction 1145.2 Computational Complexities of Convolutional Neural Networks 1155.3 Evolution of CNN Accelerators 1195.4 Model Compression Approaches 1215.5 Hardware Optimization Techniques 1245.6 Design Space Exploration 1295.7 Hardware Platforms for Implementing CNNs 1345.8 Sparse Neural Networks 1415.9 Future Scope and Summary 1456 Flexible Energy Storage Devices 155Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur6.1 Introduction 1556.2 Energy Storage 1596.3 Criteria for a Device to Store Energy 1676.4 Need of Flexible Energy Storage Devices 1696.5 Different Structures That are Being Used in Flexible Energy Storage 1726.6 Emergence of Micro-Supercapacitors 1796.7 Materials for Energy Storage Devices 1806.8 Electrode Materials 1806.9 Comparison Sheet of Different Materials 1877 VLSI Design for AI Applications 197Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram7.1 Introduction 1987.2 Specialized Neural Networks Accelerators 2017.3 Memory Hierarchy Optimization 2047.4 High Speed Interconnects 2087.5 Power Optimization 2117.6 Scalability 2137.7 Key Components of VLSI Design for AI 2147.8 Accelerating Chip Design Using ML 2177.9 Future Trends in VLSI Design for AI 2197.10 Industrial Application of VLSI Design 2218 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta8.1 Introduction 2328.2 Adiabatic Charging Principle 2328.3 Adiabatic Logic Family 2348.4 Comparative Simulation Results 2368.5 Key Challenges 2368.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic 2409 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities 257Arun Raj and Durbadal Mandal9.1 Introduction 2589.2 Antenna Design Equations 2609.3 Design and Simulation 2629.4 Conclusions 29210 Layout Dependent Effects 307Kirti and Deepti Kakkar10.1 Overview of Layout Considerations 30810.2 Analog Layout Techniques 31210.3 Effects of Layout in Deep Nanoscale CMOS 32010.4 Mismatch of Devices 32611 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343Anuraj V. and Dhandapani Vaithiyanathan11.1 Introduction 34411.2 Digital Filtering Techniques 34511.3 Hardware Architecture 34711.4 Simulation Setup and Results Analysis 35611.5 Summary 35912 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik12.1 Introduction 36412.2 Neural Network Architectures 36512.3 Deep Learning Algorithms for Medical Images 37312.4 Recent Trends in Hardware Architectures of DNN 38612.5 Challenges and Opportunities 39312.6 Summary 39613 Integration with IoT for Smart Homes 409Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj13.1 Introduction 41013.2 Sensors for Smart Homes 41313.3 Connectivity Protocols for IoT Smart Homes 41913.4 Smart Appliances for Smart Homes 42213.5 Voice Assistants 42413.6 Security and Surveillance 42613.7 Home Healthcare System 42713.8 User Interfaces and Experiences 43013.9 Sustainability and Smart Homes 43313.10 Future Trends in Smart Home IoT 43513.11 Conclusions 437References 438About the Editors 449Index 451