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TinyML Cookbook - Second Edition

You're reading from  TinyML Cookbook - Second Edition

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781837637362
Pages 664 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Gian Marco Iodice Gian Marco Iodice
Profile icon Gian Marco Iodice
LinkedIn
Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
See other products by Gian Marco Iodice

Table of Contents (16) Chapters

Preface 1. Getting Ready to Unlock ML on Microcontrollers 2. Unleashing Your Creativity with Microcontrollers 3. Building a Weather Station with TensorFlow Lite for Microcontrollers 4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands 5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1 6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2 7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico 8. Classifying Desk Objects with TensorFlow and the Arduino Nano 9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico 10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU 11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM 12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico 13. Conclusion
14. Other Books You May Enjoy
15. Index

Summary

In this opening chapter, we have presented the ingredients to build low-power ML applications on microcontrollers. Initially, we uncovered the factors that make tinyML particularly appealing (cost, energy, and privacy) and motivated our choice to use microcontrollers as target devices.

We delved into the core components of this technology, giving a quick recap of ML and providing an overview of the essential features of microcontrollers necessary for the following chapters. After introducing microcontrollers and their unique features, we presented the leading software tools and frameworks used in this book to bring ML to microcontrollers: the Arduino IDE, TensorFlow, and Edge Impulse.

Finally, we built a pre-built sketch in the Arduino IDE to blink the on-board LED on the Arduino Nano, Raspberry Pi Pico, and SparkFun Artemis Nano.

In the following chapter, we will start our practical tinyML journey by exploring how to craft microcontroller applications from the very basics.

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TinyML Cookbook - Second Edition
Published in: Nov 2023 Publisher: Packt ISBN-13: 9781837637362
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Authors (1)

Profile icon Gian Marco Iodice
LinkedIn
Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
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See other products by Gian Marco Iodice

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