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

Computing the FFT magnitude with fixed-point arithmetic using the CMSIS-DSP library

In the previous chapter, we discovered that the Raspberry Pi Pico has enough memory to handle the data pipeline to extract the MFCCs, using floating-point arithmetic. However, this data format does not offer the best computational efficiency for our desired target platform.

In this recipe, we will uncover why floating-point arithmetic is inefficient on the Raspberry Pi Pico and propose the 16-bit fixed-point (Q15) arithmetic as a more practical alternative. To provide a hands-on understanding of Q15, we will guide you through calculating the FFT magnitude, using this data type with the CMSIS-DSP Python library in the Colab notebook. This approach will simplify the transition of the code to the Raspberry Pi Pico in subsequent chapters.