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TinyML Cookbook

You're reading from TinyML Cookbook Combine machine learning with microcontrollers to solve real-world problems

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781837637362

Length 664 pages

Edition 2nd Edition

Languages

Python

Tools

Combine

Concepts

Machine Learning

Author (1):

Gian Marco Iodice

View More author details

Table of Contents (16) Chapters

Preface

1. Getting Ready to Unlock ML on Microcontrollers FREE CHAPTER

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

Deploying the model with TVM using the AoT executor on the host machine

In this recipe, we will employ TVM for the first time to convert the pre-trained CIFAR-10 model into generic C code. Instead of deploying this code on a microcontroller, we will leverage the host-driven executor through Python. This simple approach will allow us to focus more on the essential TVM components needed for generating code.

The rest of the chapter is locked

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Authors (1)

Gian Marco Iodice

Gian Marco Iodice

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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