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)

Tuning model performance with EON Tuner

Developing the most efficient ML pipeline for a given application is always challenging. One way to do this is through iterative experiments. For example, we can evaluate how some target metrics (latency, memory, and accuracy) change depending on the input feature generation and the model architecture. However, this process is time-consuming because there are several combinations, and each one needs to be tested and evaluated. Furthermore, this approach requires familiarity with digital signal processing and NN architectures to know what to tune.

In this recipe, we will use the EON Tuner to find the best ML pipeline for the Arduino Nano.

Getting ready

EON Tuner (https://docs.edgeimpulse.com/docs/eon-tuner) is a tool for automating the discovery of the best ML-based solution for a given target platform. However, it is not just an automated ML (AutoML) tool because the processing block is also part of the optimization problem. Therefore...