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)

Continuous inferencing on the Arduino Nano

As you can guess, the application deployment differs on the Arduino Nano and the Raspberry Pi Pico because the devices have different hardware capabilities.

In this recipe, we will show how to implement a continuous keyword application on the Arduino Nano.

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

  • 07_kws_arduino_nano_ble33_sense.ino:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter04/ArduinoSketches/07_kws_arduino_nano_ble33_sense.ino

Getting ready

The application on the Arduino Nano will be based on the nano_ble33_sense_microphone_continuous.cpp example provided by Edge Impulse, which implements a real-time KWS application. Before changing the code, we want to examine how this example works to get ready for the recipe.

Learning how a real-time KWS application works

A real-time KWS application—for example, the one used in the smart assistant—...