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TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
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TinyML Cookbook

TinyML Cookbook

4.9 (11)
By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)
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Building and running the TFLu application on QEMU

The skeleton of our Zephyr project is ready, so we just need to finalize our application to classify our input test image.

In this recipe, we will see how to build the TFLu application and run the program on the emulated Arm Cortex-M3-based microcontroller.

The following C files contain the code referred to in this recipe:

  • main.c, main_functions.cc, and main_functions.h:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter07/ZephyrProject/CIFAR10

Getting ready

Most of the ingredients required for developing this recipe are related to TFLu and have already been discussed in earlier chapters, such as Chapter 3, Building a Weather Station with TensorFlow Lite for Microcontrollers, or Chapter 5, Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano. However, there is one small detail of TFLu that has a big impact on the program memory usage that we haven&apos...

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