Book Image

The Insider's Guide to Arm Cortex-M Development

By : Zachary Lasiuk, Pareena Verma, Jason Andrews
Book Image

The Insider's Guide to Arm Cortex-M Development

By: Zachary Lasiuk, Pareena Verma, Jason Andrews

Overview of this book

Cortex-M has been around since 2004, so why a new book now? With new microcontrollers based on the Cortex-M55 and Cortex-M85 being introduced this year, Cortex-M continues to expand. New software concepts, such as standardized software reuse, have emerged alongside new topics including security and machine learning. Development methodologies have also significantly advanced, with more embedded development taking place in the cloud and increased levels of automation. Due to these advances, a single engineer can no longer understand an entire project and requires new skills to be successful. This book provides a unique view of how to navigate and apply the latest concepts in microcontroller development. The book is split into two parts. First, you’ll be guided through how to select the ideal set of hardware, software, and tools for your specific project. Next, you’ll explore how to implement essential topics for modern embedded developers. Throughout the book, there are examples for you to learn by working with real Cortex-M devices with all software available on GitHub. You will gain experience with the small Cortex-M0+, the powerful Cortex-M55, and more Cortex-M processors. By the end of this book, you’ll be able to practically apply modern Cortex-M software development concepts.
Table of Contents (15 chapters)
Part 1: Get Set Up
Part 2: Sharpen Your Skills

Investigating ML frameworks and libraries

The list of frameworks and libraries that help bring ML to edge devices is large and constantly evolving. This section will highlight the top three most common ML frameworks and libraries for Cortex-M ML at the time of writing this book. We will cover the following:

  • TensorFlow Lite for Microcontrollers
  • TinyML

TensorFlow Lite for Microcontrollers

The first framework to discuss is TensorFlow Lite for Microcontrollers (TFlite Micro). TensorFlow Lite is a framework that is designed for mobile devices, microcontrollers, and other edge devices where a small memory footprint and optimized performance are important. TFlite Micro is a C++ framework for ML inference that works well on Cortex-M microcontrollers. It is an optimized version of TensorFlow for embedded C/C++ applications. It is designed to run on 32-bit microcontrollers and results in very small binary files. It also doesn’t require an operating system...