Summary
In this opening chapter, we have presented the ingredients to build low-power ML applications on microcontrollers. Initially, we uncovered the factors that make tinyML particularly appealing (cost, energy, and privacy) and motivated our choice to use microcontrollers as target devices.
We delved into the core components of this technology, giving a quick recap of ML and providing an overview of the essential features of microcontrollers necessary for the following chapters. After introducing microcontrollers and their unique features, we presented the leading software tools and frameworks used in this book to bring ML to microcontrollers: the Arduino IDE, TensorFlow, and Edge Impulse.
Finally, we built a pre-built sketch in the Arduino IDE to blink the on-board LED on the Arduino Nano, Raspberry Pi Pico, and SparkFun Artemis Nano.
In the following chapter, we will start our practical tinyML journey by exploring how to craft microcontroller applications from the very basics.
