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 Arduino Nano

Now that we have acquired the knowledge of code generation with TVM, we are prepared to shift our attention toward deploying an actual model on physical microcontrollers.

Thus, in this recipe, we aim to deploy the quantized CIFAR-10 model on the Arduino Nano.

Getting ready

To get ready with this recipe, we need to know how to generate and structure an Arduino project.

In the previous recipe, we executed the CIFAR-10 model on the host machine through the Python host-driven interface. However, we haven’t seen any actual code generated apart from a few Python objects returned by TVM.

As mentioned earlier, when dealing with microcontrollers, the output of TVM is a TAR package that contains the C code for the TVM runtime and TVM Lib, known as MLF. The TAR file is created when we call the tvm.micro.generate_project() function and is automatically decompressed, integrating only the necessary files into the target template...