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

Machine Learning

Author (1):

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

Deploying the model on the Raspberry Pi Pico

The deployment of the quantized CIFAR-10 model on the Arduino Nano showcased TVM’s capability to generate code to run the model inference on this specific platform.

In this recipe, we will discover how we can use TVM to generate code for the Raspberry Pi Pico.

Getting ready

As we have seen in the previous chapter, the tvm.micro.generate_project() function is responsible for generating the Arduino project. Among the list of input arguments, this function requires the Arduino board name. However, what is the purpose of this information?

The board name is not used during the code generation phase because the code is already generated when calling the tvm.micro.generate_project() function. Instead, this information is required because TVM offers commands that allow building and flashing the application directly on the Arduino board from Python. Since we are not employing these commands to compile and upload the Arduino...