Deep Learning on Microcontrollers

TinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you.

This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers. The book will then help you build an Air gesture digit recognition system using the Arduino Nano RP2040 board and an AI project for recognizing keywords using the Syntiant TinyML board. Lastly, the book summarizes the concepts covered and provides a brief introduction to topics such as zero-shot learning, one-shot learning, federated learning, and MLOps.

By the end of the book, you will be able to develop and deploy end-to-end Tiny ML solutions with ease.

KEY FEATURES

* Deploy machine learning models on edge devices with ease.
* Leverage pre-built AI models and deploy them without writing any code.
* Create smart and efficient IoT solutions with TinyML.

WHAT YOU WILL LEARN

* Learn how to build a Keyword recognition system using the Syntiant TinyML board.
* Learn how to build an air gesture digit recognition system using the Arduino Nano RP2040.
* Learn how to test and deploy models on Edge Impulse and Arduino IDE.
* Get tips to enhance system-level performance.
* Explore different real-world use cases of TinyML across various industries.

WHO THIS BOOK IS FOR

The book is for IoT developers, System engineers, Software engineers, Hardware engineers, and professionals who are interested in integrating AI into their work. This book is a valuable resource for Engineering undergraduates who are interested in learning about microcontrollers and IoT devices but may not know where to begin.

1. Introduction to AI
2. Traditional ML Lifecycle
3. TinyML Hardware and Software Platforms
4. End-to-End TinyML Deployment Phases
5. Real World Use Cases
6. Practical Experiments with TinyML
7. Advance Implementation with TinyML Board
8. Continuous Improvement
9. Conclusion

Atul Krishna Gupta has held many positions as Research & Development Executive in companies such as Syntiant, Macom, Inphi (now Marvell) and Gennum (now Semtech). He has over 25 years of experience in delivering all aspects of systems from IC design to software support. He has made contributions to various forums such as IEEE, SMPTE and OIF. Two technical Emmy Awards were granted to two companies for the technical work he led in the past. He was awarded with the Employee of the year award and Excellence in R&D award at Gennum. Atul holds over 20 patents. Currently, his research interests are in the field of Battery Management Systems (BMS) where he is finding ways to use AI to make Electrical Vehicles (EV) safer and last longer. He has received his B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur, India and MS degree in Electrical and Computer Engineering from University of Calgary, Canada.

Dr. Sivaprasad Nandyala worked in Eaton Research Labs as Lead Engineer (Data Science) at Eaton India Innovation Center, Pune, India. Prior to Eaton, he worked in companies like Tata Elxsi, Wipro Technologies, Analog Devices & Ikanos Communications in multiple technology areas. Dr. Nandyala has over 35+ research publications, 1 patent grant and 6 patents under review. He obtained his Ph.D. in Speech Processing from NIT Warangal, India. He was an ERASMUS MUNDUS scholarship holder from the European government for his Postdoctoral Research at Politecnico di Milano (POLIMI), Italy.