Tiny Machine Learning

Next-Generation Artificial Intelligence

Someet Singh, Deepika Ghai, Suman Lata Tripathi, Navjot Kaur

• Publisher: Wiley-Scrivener
• Edition: 1
• Year: 2026
• Topic: Computer Science
• Print ISBN:
  9781394392803
• Subtopic: Artificial Intelligence (AI) & Semantics
• eBook ISBN:
  9781394392810
• Index: Computer Science

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.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Tiny Machine Learning Basic Concepts
• 2 Understanding the Basics of Machine Learning and Tiny Machine Learning
• 3 Embedded Systems and Internet of Things Foundations
• 4 Hardware Platforms for Tiny Machine Learning: A Comprehensive Review
• 5 A Sequential Approach to Realize Tiny Machine Learning in Hardware Platform
• 6 Hardware Platforms for Tiny Machine Learning: Enabling Efficient Edge Intelligence
• 7 Software Framework for Tiny Machine Learning
• 8 Analysis of Data Preprocessing Techniques for Tiny Machine Learning Applications
• 9 Model Training for Tiny Machine Learning
• 10 Model Optimization and Quantization for Efficient Neural Networks
• 11 Analysis of Model Compression Techniques for Tiny Machine Learning
• 12 Efficient Framework for Deployment of Lightweight Deep Learning Models on Simulation-Based Edge Devices
• 13 Tiny Machine Learning for Smart Campus and Educational Applications: Enhancing Learning with Edge Artificial Intelligence
• 14 Power Management in Tiny Machine Learning
• 15 Security and Privacy-Preserving Tiny Machine Learning: Challenges, Threats, and Mitigation Strategies
• 16 Urgent Response Accessory: Transforming Wearable Safety with Tiny Machine Learning
• 17 Low-Power Tiny Machine Learning-Based Animal Sound Classification Using the Sound Classification of Animal Voice
• 18 Hands-On Project and Case Studies: Project to Implement Tiny Machine Learning
• 19 Adoption of Tiny Machine Learning for Industrial Predictive Maintenance Solutions
• 20 Future Directions in Tiny Machine Learning: Emerging Trends and Innovations
• Index
• Also of Interest
• End User License Agreement