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

Fusing the pre-processing operators for efficient deployment

In this last recipe, we will develop a sketch to classify desk objects with the Arduino Nano. However, the ML deployment is not the only thing we must take care of. Indeed, a few additional operations must be implemented to supply the correct input image to the model.

Therefore, in this recipe, we will not just discuss model deployment but also delve into implementing a memory-efficient pre-processing pipeline, preparing the input for the model.

Getting ready

RAM usage is impacted by the variables allocated during the program execution, such as the input, output, and intermediate tensors of the ML model. However, the model is not solely responsible for memory utilization. In fact, the image acquired from the OV7670 camera needs to be pre-processed with the following operations to provide the appropriate input to the model: