Book Image

TinyML Cookbook

By : Gian Marco Iodice
Book Image

TinyML Cookbook

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)

Preparing the input features for the model inference

As we know, the model's input features are the scaled and quantized temperature and humidity of the last three hours. Using this data, the ML model can forecast whether it will snow.

In this recipe, we will see how to prepare the input data to feed into our ML model. In particular, this recipe will teach us how to acquire, scale, and quantize the sensor measurements and keep them in temporal order using a circular buffer.

The following Arduino sketch contains the code referred to in this recipe:

  • 08_input_features.ino:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter03/ArduinoSketches/08_input_features.ino

Getting ready

Our application will acquire the temperature and humidity every hour to get the necessary input features for the model. However, how can we keep the last three measurements in temporal order to feed the network the correct input?

In this recipe, we will use a...