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

Designing and training a CIFAR-10 model for memory-constrained devices

The tight memory constraint on LM3S6965 forces us to develop a model with extremely low memory utilization. In fact, the target microcontroller has four times less memory capacity than the Arduino Nano.

Despite this challenging constraint, in this recipe, we will demonstrate the effective deployment of CIFAR-10 image classification on this microcontroller by employing the following convolutional neural network (CNN) with TensorFlow:

Figure 10.1: The model tailored for CIFAR-10 dataset image classification

As you can see from the preceding model architecture, the depthwise separable convolution (DepthSeparableConv2D) layer is the leading operator of the model. This operator, discussed in the upcoming Getting ready section, will make the model compact and accurate.