Bridge the gap between advanced algorithms and hardware innovation with this essential book, which details how machine learning is being used to overcome challenges in nanoelectronics while laying the critical groundwork for the future of neuromorphic computing hardware.
New techniques for obtaining insights from enormous amounts of data and efficiently acquiring smaller data sets are provided by recent developments in machine learning. Researchers in nanoscience and nanoelectronics are experimenting with these tools to tackle challenges across many fields. Nanoscience and nanoelectronics not only advance machine learning but also lay the groundwork for neuromorphic computing hardware to broaden machine learning algorithm implementation. This book is a collection of possibilities for machine learning in nanoelectronics, semiconductor devices, and based circuits. With an easy-to-understand approach, this book explores the latest in machine learning in nanoelectronics materials and nanoscale devices through insights and analysis of recent developments in nanoelectronics.
Table of Contents:
Preface xiii
1 Introduction to Machine Learning in Nanoelectronics 1
Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary
1.1 Introduction 2
1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 4
1.3 Machine Learning in Nanoscale Device Simulation 11
1.4 Process Optimization in Semiconductor Manufacturing 21
1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 25
1.6 Future Directions and Challenges 29
1.7 Conclusion 31
2 Machine Learning to Explore Opportunities in Quantum 43
Jyoti Khandelwal
2.1 Introduction to Quantum Opportunities 44
2.2 Understanding Quantum Data 46
2.3 Machine Learning Techniques for Quantum Applications 49
2.4 Case Studies and Applications 57
2.5 Tools and Frameworks for Implementation 60
2.6 Challenges and Opportunities in QML 63
2.7 Conclusion 63
3 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67
Anshu Srivastava and Shakun Srivastava
3.1 Introduction 69
3.2 Predictive Modelling and Machine Learning's Application in Cancer Diagnostics 69
3.3 Customized Medical Care 72
3.4 Result and Future Perspective 77
4 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89
Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni
4.1 Introduction 90
4.2 Methodology 94
4.3 Simulation Results 96
4.4 Conclusion 104
5 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113
Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik
5.1 Introduction 114
5.2 Computational Complexities of Convolutional Neural Networks 115
5.3 Evolution of CNN Accelerators 119
5.4 Model Compression Approaches 121
5.5 Hardware Optimization Techniques 124
5.6 Design Space Exploration 129
5.7 Hardware Platforms for Implementing CNNs 134
5.8 Sparse Neural Networks 141
5.9 Future Scope and Summary 145
6 Flexible Energy Storage Devices 155
Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur
6.1 Introduction 155
6.2 Energy Storage 159
6.3 Criteria for a Device to Store Energy 167
6.4 Need of Flexible Energy Storage Devices 169
6.5 Different Structures That are Being Used in Flexible Energy Storage 172
6.6 Emergence of Micro-Supercapacitors 179
6.7 Materials for Energy Storage Devices 1806.8 Electrode Materials 180
6.9 Comparison Sheet of Different Materials 187
7 VLSI Design for AI Applications 197
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram
7.1 Introduction 198
7.2 Specialized Neural Networks Accelerators 201
7.3 Memory Hierarchy Optimization 204
7.4 High Speed Interconnects 208
7.5 Power Optimization 211
7.6 Scalability 213
7.7 Key Components of VLSI Design for AI 214
7.8 Accelerating Chip Design Using ML 217
7.9 Future Trends in VLSI Design for AI 219
7.10 Industrial Application of VLSI Design 221
8 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231
Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta
8.1 Introduction 232
8.2 Adiabatic Charging Principle 232
8.3 Adiabatic Logic Family 234
8.4 Comparative Simulation Results 236
8.5 Key Challenges 236
8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic 240
9 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities 257
Arun Raj and Durbadal Mandal
9.1 Introduction 258
9.2 Antenna Design Equations 260
9.3 Design and Simulation 262
9.4 Conclusions 292
10 Layout Dependent Effects 307
Kirti and Deepti Kakkar
10.1 Overview of Layout Considerations 308
10.2 Analog Layout Techniques 312
10.3 Effects of Layout in Deep Nanoscale CMOS 320
10.4 Mismatch of Devices 326
11 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343
Anuraj V. and Dhandapani Vaithiyanathan
11.1 Introduction 344
11.2 Digital Filtering Techniques 345
11.3 Hardware Architecture 347
11.4 Simulation Setup and Results Analysis 356
11.5 Summary 359
12 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363
Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik
12.1 Introduction 364
12.2 Neural Network Architectures 365
12.3 Deep Learning Algorithms for Medical Images 373
12.4 Recent Trends in Hardware Architectures of DNN 386
12.5 Challenges and Opportunities 393
12.6 Summary 396
13 Integration with IoT for Smart Homes 409
Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj
13.1 Introduction 410
13.2 Sensors for Smart Homes 413
13.3 Connectivity Protocols for IoT Smart Homes 419
13.4 Smart Appliances for Smart Homes 422
13.5 Voice Assistants 424
13.6 Security and Surveillance 426
13.7 Home Healthcare System 427
13.8 User Interfaces and Experiences 430
13.9 Sustainability and Smart Homes 433
13.10 Future Trends in Smart Home IoT 435
13.11 Conclusions 437
References 438
About the Editors 449
Index 451
About the Author :
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.
