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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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Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano

Computer vision is what made convolutional neural networks hugely popular. Without this deep learning algorithm, tasks such as object recognition, scene understanding, and pose estimation would be really challenging. Nowadays, many modern camera applications are powered by machine learning (ML), and we just need to take the smartphone to see them in action. Computer vision also finds space in microcontrollers, although with limitations given the reduced onboard memory.

In this chapter, we will see the benefit of adding sight to our tiny devices by recognizing indoor environments with the OV7670 camera module in conjunction with the Arduino Nano 33 BLE Sense board.

In the first part, we will learn how to acquire images from the OV7670 camera module. We will then focus on the model design, applying transfer learning with the Keras API to recognize kitchens and bathrooms. Finally...

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