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)
1
Part 1: Get Set Up
5
Part 2: Sharpen Your Skills

Understanding the ML application life cycle

The life cycle to create, maintain, and update an ML application can be visualized in a few different ways. The following diagram shows a high-level view of this ML life cycle for a typical ML project being deployed on edge devices. Take a minute to look through the listed steps before we explain each section:

Figure 6.1 – Typical ML life cycle for embedded projects

The steps at the top all pertain to data. Gathering it and preparing it for use in ML algorithms is a non-trivial task and is often the source of competitive advantage for companies (as opposed to the ML algorithms themselves). Commonly, there is a dedicated resource—or resources—for preparing data at companies, referred to here as data scientists. Once the data is prepared, the data is used to create and train an ML model. This is commonly performed by an embedded software developer and—potentially—an ML developer that...