Tiny Machine Learning: Design Principles and Applications

On-Device Training Techniques and Ethical Considerations

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development.

Overview of Tiny Machine Learning

In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML).

Contributors and Innovations

Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design.

TinyML Paradigms and Applications

Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML.

Devices, Tools, and Techniques

Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML.

Real-Time and Environmental Applications

The book also describes TinyML techniques for real-time and environmental applications.

Security, Privacy, and Power Consumption

Additional topics covered in the book include:

Security and Privacy

A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes.

Power Consumption and Memory

Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML.

Research and Practical Applications

Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis.

Intended Audience

Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.