Tiny Machine Learning

Next-Generation Artificial Intelligence
1. Auflage 2026
ISBN: 978-1-394-39280-3
Verlag: John Wiley & Sons Inc

Buch, Englisch, 576 Seiten

This comprehensive guide provides the foundational tools, optimization techniques, and practical case studies you need to engineer autonomous edge systems across healthcare, industrial automation, and IoT.

Deploying machine learning algorithms on devices with limited resources, such as microcontrollers and low-power edge devices, is known as TinyML (tiny machine learning). These devices are typically limited by memory, power, and processing capabilities, yet TinyML allows real-time processing and decision-making at the edge without relying on cloud computing. Despite these challenges, TinyML is becoming essential for edge applications that require fast, efficient, and autonomous operations.

TinyML is revolutionizing industries by enabling sophisticated machine learning capabilities on hardware with limited resources. As edge computing and the Internet of Things continue to expand, TinyML forms the backbone of smarter, more efficient applications in sectors such as healthcare, industrial automation, and environmental monitoring.

This book provides a comprehensive guide to this rapidly evolving field. It presents the concepts, methods, algorithms, and tools of TinyML, covering foundational principles, hardware and software platforms, optimization techniques, and real-world case studies. The book is tailored for practitioners, researchers, and enthusiasts eager to understand and leverage the power of TinyML.

Autoren/Hrsg.

Singh, Someet

Ghai, Deepika

Tripathi, Suman Lata

Kaur, Navjot

Inhaltsverzeichnis

Preface xxix

1 Tiny Machine Learning Basic Concepts 1
Sandhya Avasthi, Suman Lata Tripathi, Khushboo Jain and Divya Upadhyay

1.1 Introduction 2
1.2 Main Components of Tiny Machine Learning 5
1.3 Tiny Machine Learning Technology 8
1.4 Tiny Machine Learning Frameworks 13
1.5 Applications of Tiny Machine Learning 13
1.6 Issues and Challenges in Tiny Machine Learning 17
1.7 Conclusion 19

2 Understanding the Basics of Machine Learning and Tiny Machine Learning 23
Amandeep Kaur, Ramandeep Sandhu, Gaganpreet Kaur and Deepika Ghai

2.1 Introduction 24
2.2 Role of Machine Learning in Key Business Sectors 28
2.3 Basics of Tiny Machine Learning 32
2.4 Applications of Tiny Machine Learning in Business Sectors 37
2.5 Tiny Machine Learning-Driven Models for Intelligent Customer Interaction 39
2.6 Security and Privacy Considerations 42
2.7 Conclusion 45

3 Embedded Systems and Internet of Things Foundations 51
Ruby Singh, Sandeep Chouhan and Deepika Ghai

3.1 Introduction 52
3.2 Architecture of Embedded Systems 60
3.3 Internet of Things: Concepts and Ecosystem 62
3.4 Embedded Systems in the Internet of Things Context 64
3.5 Related Work 66
3.6 Design Methodologies 69
3.7 Analytical Insights 72
3.8 Case Studies and Applications 76
3.9 Challenges and Future Trends 79
3.10 Conclusion 81

4 Hardware Platforms for Tiny Machine Learning: A Comprehensive Review 87
Sonal Trivedi and Amitava Pal

4.1 Introduction 87
4.2 Literature Review 89
4.3 Brief Overview of Fraud Detection 90
4.4 Deep Learning 93
4.5 Data and Preprocessing in Fraud Detection for Tiny Machine Learning 94
4.6 Performance Metrics for Fraud Detection Models 100
4.7 Comparative Analysis of Hardware Platforms for Tiny Machine Learning 103
4.8 Future Directions in Tiny Machine Learning 106
4.9 Conclusion 110

5 A Sequential Approach to Realize Tiny Machine Learning in Hardware Platform 115
Abhinav Karan

5.1 Introduction 115
5.2 Literature Survey 118
5.3 Tiny Machine Learning Hardware Requirements 119
5.4 Popular Hardware Platforms for Tiny Machine Learning 123
5.5 Tiny Machine Learning Software Framework and Hardware Compatibility 126
5.6 Implementation Approaches and Best Practices 129
5.7 Application-Specific Hardware Considerations 135
5.8 Future Trends in Tiny Machine Learning Hardware 138
5.9 Conclusion 141

6 Hardware Platforms for Tiny Machine Learning: Enabling Efficient Edge Intelligence 145
Srinivas Talasila, Vijaya Kumar Gurrala, Ch. V. S. Satyamurthy, D. Karthik Reddy, Ch. Sri Karthik, B. Sathvika and K. Geetanjali

6.1 Introduction 146
6.2 Hardware Platforms for Tiny Machine Learning 150
6.3 Deployment and Workflow 154
6.4 Software Ecosystem for Tiny Machine Learning 155
6.5 Benchmarking and Performance Metrics in Tiny Machine Learning 157
6.6 Security, Privacy, and Federated Learning in Tiny Machine Learning 160
6.7 Neuromorphic Computing and the Future of Tiny Machine Learning 163
6.8 Future Trends in Tiny Machine Learning 165
6.9 Conclusion 166

7 Software Framework for Tiny Machine Learning 169
Devanshi Srivastava, Adarsh Kumar Arya, Gauri Shukla, Bhalchandra Shingan and Murali Pujari

7.1 Introduction 170
7.2 Tiny Machine Learning Software Development 172
7.3 Popular Tiny Machine Learning Software Frameworks 175
7.4 Key Features of Tiny Machine Learning Software Frameworks 179
7.5 Comparison of Tiny Machine Learning Frameworks 182
7.6 Best Practices for Tiny Machine Learning Software Development 185
7.7 Future Trends in Tiny Machine Learning Software Frameworks 189
7.8 Conclusion 192

8 Analysis of Data Preprocessing Techniques for Tiny Machine Learning Applications 197
Ameya K. Naik, Kiran V. Ajetrao and Nitin S. Nagori

8.1 Introduction 198
8.2 Related Work 199
8.3 Structure of a Tiny Machine Learning Model 201
8.4 Preprocessing for Tiny Machine Learning 203
8.5 Experimentations and Discussions 210
8.6 Conclusions and Scope for Further Research 221

9 Model Training for Tiny Machine Learning 225
R. Saranya and T.R. Nagajothi

9.1 Introduction 226
9.2 Need for Optimized Model Training 231
9.3 Advantages and Disadvantages of Tiny Machine Learning 236
9.4 Related Work 240
9.5 Methodology 246
9.6 Applications 251
9.7 Conclusion 255

10 Model Optimization and Quantization for Efficient Neural Networks 259
Abhishek Kumar, Harpreet Singh Bedi and Tanishk Singhal

10.1 Introduction 259
10.2 The Modified National Institute of Standards and Technology Dataset and Data Processing 260
10.3 Building the Baseline Convolutional Neural Network Model 261
10.4 Model Pruning for Reducing Complexity 262
10.5 Knowledge Distillation for Efficient Learning 265
10.6 Model Quantization for Reduced Precision Computing 267
10.7 Hardware Acceleration for Optimized Inference 269
10.8 Benchmarking and Deployment Strategies 272
10.9 Conclusion 275

11 Analysis of Model Compression Techniques for Tiny Machine Learning 279
Kiran V. Ajetrao, Ameya K. Naik and Nitin S. Nagori

11.1 Introduction 280
11.2 Tiny Machine Learning 281
11.3 Case Study 1: Model Compression Techniques for Memory-Constrained Devices 285
11.4 Case Study 2: Efficient Image Classifier Compression for Embedded Deployment 293
11.5 Results and Discussions 298
11.6 Conclusion 303

12 Efficient Framework for Deployment of Lightweight Deep Learning Models on Simulation-Based Edge Devices 307
Arwinder Dhillon and Amandeep Kaur

12.1 Introduction 307
12.2 Edge Computing Motivation 309
12.3 Types of Edge Devices 310
12.4 Background 311
12.5 Proposed Work 313
12.6 Dataset Collection 315
12.7 Experimental Setup and Results 317
12.8 Conclusion 320

13 Tiny Machine Learning for Smart Campus and Educational Applications: Enhancing Learning with Edge Artificial Intelligence 323
Reshma Ajetrao and Swapna Kadam

13.1 Introduction 323
13.2 Objectives and Scope 325
13.3 Understanding Tiny Machine Learning: Definition, Evolution, and Key Concepts 325
13.4 Smart Campus and Education 4.0 328
13.5 Tiny Machine Learning Applications in Education 331
13.6 Technical Implementation Framework 332
13.7 Challenges and Limitations 335
13.8 Case Studies and Examples 337
13.9 Future Trends and Research Directions 340
13.10 Conclusion 342

14 Power Management in Tiny Machine Learning 347
Chitra Ravi and Anita Patrot

14.1 Introduction 348
14.2 Power Management in Tiny Machine Learning Systems 351
14.3 Challenges in Power-Constrained Environments 352
14.4 Energy Harvesting Techniques for Tiny Machine Learning Devices 354
14.5 Efficient Power Management Strategies 355
14.6 Power Consumption and Profiling for Tiny Machine Learning Systems 357
14.7 Hardware Power Usage in Tiny Machine Learning Systems 357
14.8 Software Power Consumption in Tiny Machine Learning Systems 358
14.9 Power Profiling Techniques within Tiny Machine Learning Systems 359
14.10 Energy-Efficient Tiny Machine Learning Models 360
14.11 Edge Impulse and Other Development Platforms 364
14.12 Tiny Machine Learning Compilers and Energy Profiling Tools 364
14.13 Power Optimization Strategies in Tiny Machine Learning Systems 365
14.14 Software-Level Optimization for TinyML 368
14.15 Power-Aware Operating Systems and Middleware 377
14.16 Power-Efficient Tiny Machine Learning Applications – Case Studies 381
14.17 Future Prospects of Power Management in Tiny Machine Learning Systems 384
14.18 Conclusion 386

15 Security and Privacy-Preserving Tiny Machine Learning: Challenges, Threats, and Mitigation Strategies 389
Sofia Khan

15.1 Introduction 389
15.2 Overview of Tiny Machine Learning Architecture 391
15.3 Security Challenges in Tiny Machine Learning 394
15.4 Privacy Challenges and Mitigation Strategies 398
15.5 Secure Tiny Machine Learning Deployment Architecture 403
15.6 Research Gaps and Future Directions 407
15.7 Conclusion 410

16 Urgent Response Accessory: Transforming Wearable Safety with Tiny Machine Learning 413
Mohammed Al Libaan Kazi, Khushi Motwani and Mithila Chavan

16.1 Introduction 414
16.2 Motivation and Challenges 414
16.3 Research Objectives 415
16.4 Related Work 415
16.5 Research Gap 417
16.6 Methodology 417
16.7 Applications and Evaluation 429
16.8 Conclusion and Future Directions 432

17 Low-Power Tiny Machine Learning-Based Animal Sound Classification Using the Sound Classification of Animal Voice 439
Swathi Gowroju, S. Sowjanya Chintalapati, S. Sreeja and D. Nagasri

17.1 Introduction 440
17.2 Related Work 441
17.3 Proposed System 444
17.4 Results and Discussions 450
17.5 Conclusion 457

18 Hands-On Project and Case Studies: Project to Implement Tiny Machine Learning 461
Navneet Kaur, Manjot Kaur and Chirag Sharma

18.1 Introduction 462
18.2 Evaluation, Co-Design, and Tools 464
18.3 Case Study 1: Predictive Maintenance Using Tiny Machine Learning in Industrial Motors 467
18.4 Case Study 2: Smart Irrigation Using Tiny Machine Learning and Soil Moisture Sensing 469
18.5 Case Study 3: Crop Disease Detection Using Tiny Machine Learning and Embedded Vision 471
18.6 Scalability and Future Work 473
18.7 Conclusion 476

19 Adoption of Tiny Machine Learning for Industrial Predictive Maintenance Solutions 479
Manorama Patnaik and Tannisha Kundu

19.1 Introduction 480
19.2 Predictive Maintenance and Tiny Machine Learning 481
19.3 Tiny Machine Learning Architecture for Industrial Predictive Maintenance 486
19.4 Case Studies and Applications in Tiny Machine Learning Applications 488
19.5 Challenges and Limitations 492
19.6 Future Directions 492
19.7 Conclusion 493

20 Future Directions in Tiny Machine Learning: Emerging Trends and Innovations 499
P. Lokeshkiran and C. Mathankumar

20.1 Introduction 500
20.2 Importance of Tiny Machine Learning in Real-Time Edge Computing 501
20.3 Key Constraints and Challenges in Tiny Machine Learning 502
20.4 Emerging Trends in Tiny Machine Learning 503
20.5 Future Innovations and Applications of Tiny Machine Learning 508
20.6 Challenges and Research Directions in Tiny Machine Learning 511
20.7 Conclusion and Future Research 516
20.8 Key Findings and Recommendations on Tiny Machine Learning 519
20.9 Conclusion 520

References 520
Index 523

Über Autor(innen)

Someet Singh, PhD, is an Associate Professor and Coordinator of Academic Operations at Lovely Professional University, Phagwara, Punjab. His contributions include multiple patents and book chapters and more than 17 research publications in prestigious journals and conferences. He specializes in the Internet of Things, mechatronics, and machine learning.

Deepika Ghai, PhD, is an Assistant Professor at Lovely Professional University with more than seven years of experience in academia. She has published more than 50 research papers in refereed journals and conferences, as well as a number of edited books. Her areas of expertise include signal and image processing, biomedical signal and image processing, and VLSI signal processing.

Suman Lata Tripathi, PhD, is a Professor at Lovely Professional University with more than 22 years of experience in academia and research. She has published more than 152 research papers in refereed journals, conference proceedings, and e-books, as well as more than 30 books, 20 Indian patents, and four copyrights. Her research focuses on microelectronics device modeling and characterization, low-power VLSI circuit design, and VLSI design testing.

Navjot Kaur is an Assistant Professor in the Department of System Programming at Lovely Professional University, Phagwara. With more than 15 years of experience in academia and industry, she specializes in image processing, system programming, and advanced computational methodologies. Her professional career spans roles as a software developer, lecturer, Head of Department, and mentor, guiding numerous capstone projects and theses.