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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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Converting a NumPy image to a C-byte array

Our application will be running on a virtual platform with no access to a camera module. Therefore, we need to supply a valid test input image into our application to check whether the model works as expected.

In this recipe, we will get an image from the test dataset that must return a correct classification for the ship class. The sample will then be converted to an int8_t C array and saved as an input.h file.

The following Colab file (refer to the Converting a NumPy image to a C-byte array section) contains the code referred to in this recipe:

  • prepare_model.ipynb:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter07/ColabNotebooks/prepare_model.ipynb

Getting ready

To get ready for this recipe, we just need to know how to prepare the C file containing the input test image. The structure of this file is quite simple and reported in the following figure:

Figure 7.7 –...

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