TinyML
Revolutionizing Embedded AI for Intelligent Devices
1st Edition
Will be published approx. on 1. February 2027
Book
Hardback
384 pages
978-1-394-35137-4 (ISBN)
Description
A contemporary discussion of real-world TinyML development
In TinyML: Revolutionizing Embedded AI for Intelligent Devices, a team of distinguished researchers delivers an expert, up-to-date discussion of the practical applications of TinyML. The authors explain how to develop smart systems that learn and make decisions at the place of action on tiny, low-power devices.
Readers will learn how to create systems that act without access to high-speed internet connectivity. Beginning with explanations of the fundamentals of TinyML, the hardware it runs on, and how to collect and prepare the data required to run applications, the book goes on to discuss neural networks, model training, and the wide array of real-world applications and examples of the technologies.
Readers will also discover:
A thorough introduction to the data privacy and security problems inherent in TinyML development
Comprehensive explorations of the hands-on techniques required to develop TinyML applications
Practical discussions of the hardware and software ecosystems in which TinyML development occurs
Complete treatments of clustering, neural network architectures, and likely future developments in those areas
Perfect for data scientists and machine learning engineers, TinyML will also benefit embedded systems engineers, IoT developers, and students of computer science, electrical engineering, and robotics.
In TinyML: Revolutionizing Embedded AI for Intelligent Devices, a team of distinguished researchers delivers an expert, up-to-date discussion of the practical applications of TinyML. The authors explain how to develop smart systems that learn and make decisions at the place of action on tiny, low-power devices.
Readers will learn how to create systems that act without access to high-speed internet connectivity. Beginning with explanations of the fundamentals of TinyML, the hardware it runs on, and how to collect and prepare the data required to run applications, the book goes on to discuss neural networks, model training, and the wide array of real-world applications and examples of the technologies.
Readers will also discover:
A thorough introduction to the data privacy and security problems inherent in TinyML development
Comprehensive explorations of the hands-on techniques required to develop TinyML applications
Practical discussions of the hardware and software ecosystems in which TinyML development occurs
Complete treatments of clustering, neural network architectures, and likely future developments in those areas
Perfect for data scientists and machine learning engineers, TinyML will also benefit embedded systems engineers, IoT developers, and students of computer science, electrical engineering, and robotics.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
ISBN-13
978-1-394-35137-4 (9781394351374)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
S. Karthika is an Associate Professor in the Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering.
Balamurugan Balusamy is an Associate Dean Student in Shiv Nadar University, Delhi-NCR
Chithirai Pon Selvan has over twenty-five years' experience teaching engineering students. He's a Senior Fellow of the Higher Education Academy in the UK.
Feslin Anish Mon is a researcher specializing in trust-based security in Internet of Things networks. He works in the Department of Information Technology at the University of Technology and Applied Sciences, Ibri Branch.
Balamurugan Balusamy is an Associate Dean Student in Shiv Nadar University, Delhi-NCR
Chithirai Pon Selvan has over twenty-five years' experience teaching engineering students. He's a Senior Fellow of the Higher Education Academy in the UK.
Feslin Anish Mon is a researcher specializing in trust-based security in Internet of Things networks. He works in the Department of Information Technology at the University of Technology and Applied Sciences, Ibri Branch.
Content
Part I: Foundations of TinyML and Edge AI
Chapter 1: A Primer on TinyML: Revolutionizing Embedded AI for Intelligent Devices
Chapter 2: Hardware and Software Ecosystem for TinyML - Cross Platform Deployment of Models
Chapter 3: Smart Data Acquisition and Preprocessing for Resource-Constrained Environments
Chapter 4: AI-Centric Architectures for TinyML: Revolutionizing Edge Intelligence with Optimized Hardware Solutions
Part II: Building and Optimizing TinyML Models
Chapter 5: Clustering Distributed Edge Devices Using Scalable AI
Chapter 6: Optimization at Edge Using AI Techniques: Pruning, Quantization, and Knowledge Distillation
Chapter 7: Neural Network Architecture for Ultra-Low-Power Intelligent Edge Systems
Part III: Analyzing and Evaluating TinyML Models
Chapter 8: Analysis of Optimization Algorithms for Efficient TinyML Model Performance
Chapter 9: Evaluation Frameworks, Metrics, and Benchmarks for TinyML Applications
Part IV: TinyML Applications
Chapter 10: TinyML for Healthcare and Biomedical Applications
Chapter 11: Advancing Sustainable Intelligence through an Unified Framework at the Edge by Utilizing TinyML for Environmental Monitoring
Chapter 12: TinyML for Industrial Automation and Predictive Maintenance
Chapter 13: TinyML for Computer Vision and Image Processing
Chapter 14: Impact of Tiny Machine Learning on Embedded Systems for Advanced Image Processing
Chapter 15: TinyML for Audio and Speech Processing
Part V: Advanced Topics in TinyML
Chapter 16: Mitigation Strategies for Enhancing Security and Privacy in TinyML Systems
Chapter 17: Compliance, Data Security, Law and Ethical AI Regulation in Decentralized Approached using TinyML
Chapter 1: A Primer on TinyML: Revolutionizing Embedded AI for Intelligent Devices
Chapter 2: Hardware and Software Ecosystem for TinyML - Cross Platform Deployment of Models
Chapter 3: Smart Data Acquisition and Preprocessing for Resource-Constrained Environments
Chapter 4: AI-Centric Architectures for TinyML: Revolutionizing Edge Intelligence with Optimized Hardware Solutions
Part II: Building and Optimizing TinyML Models
Chapter 5: Clustering Distributed Edge Devices Using Scalable AI
Chapter 6: Optimization at Edge Using AI Techniques: Pruning, Quantization, and Knowledge Distillation
Chapter 7: Neural Network Architecture for Ultra-Low-Power Intelligent Edge Systems
Part III: Analyzing and Evaluating TinyML Models
Chapter 8: Analysis of Optimization Algorithms for Efficient TinyML Model Performance
Chapter 9: Evaluation Frameworks, Metrics, and Benchmarks for TinyML Applications
Part IV: TinyML Applications
Chapter 10: TinyML for Healthcare and Biomedical Applications
Chapter 11: Advancing Sustainable Intelligence through an Unified Framework at the Edge by Utilizing TinyML for Environmental Monitoring
Chapter 12: TinyML for Industrial Automation and Predictive Maintenance
Chapter 13: TinyML for Computer Vision and Image Processing
Chapter 14: Impact of Tiny Machine Learning on Embedded Systems for Advanced Image Processing
Chapter 15: TinyML for Audio and Speech Processing
Part V: Advanced Topics in TinyML
Chapter 16: Mitigation Strategies for Enhancing Security and Privacy in TinyML Systems
Chapter 17: Compliance, Data Security, Law and Ethical AI Regulation in Decentralized Approached using TinyML