Using CAN FD for Remote Hardware Debugging of Cortex-M Devices

Today’s projects and systems get more and more complex. Many systems include multiple MCUs, connected with a field bus or network, for example CAN. For example there can be up to 70 CAN nodes in modern cars. Such larger and connected systems are a challenge for debugging.

Traditional hardware debugging requires a hardware debug probe, connected with a dedicated SWD/JTAG debug cable to the target device. This needs dedicated pins on the target device plus physical access to the device itself. In many cases, this is not possible in the final product. The hardware debug probes, cables, pins and high speed signals are costly. And worse they can introduce new problems and are prone to interference.

If there is a field bus like CAN connecting all the MCUs, why not use it for hardware debugging? Hardware debugging meaning programming the FLASH memory, halt the MCU, inspect the memory and registers, and step through the code?

Cortex-M Hardware Debugging over CAN

Yes, we can! With the help of a rather unknown hardware feature on ARM Cortex-M devices. We can use the ARM DebugMonitor Interrupt to control and debug the target system. As we would use a JTAG/SWD connection. Instead, we use the CAN bus :-).

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Static Analysis with SonarQube and VS Code

SonarQube from Sonar is a free static analysis tool for VS Code. It is able to analyze the source code and find issues. SonarQube does a similar job as other static analysis tools like CppCheck for VS Code.

SonarQube in VS Code
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Using Semihosting and File I/O in DevContainers

With Optimizing Embedded Development with VS Code and DevContainer I showed the benefits of using development containers. And with Remote Debugging with DevContainer and VS Code I explained ways use hardware debugging using that concept.

One topic is still open: how to use semihosting with file I/O using development container? The challenge here is that we need to work with two different file systems.

DevContainer Development with File I/O and Semihosting

In this article I show how semihosting file I/O can be used with DevContainer.

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Sharing Standalone NXP SDK Projects in VS Code

The NXP SDK is git based which is great. If I create a project with VS code, it references the SDK cloned locally.

Standard NXP SDK Project in VS Code

A standalone project structure is needed if you want to easily share a project with your team. It’s also necessary for sharing inside a classroom environment. This article shows how to use an NXP SDK project in standalone mode.

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Optimizing Embedded Development with VS Code and DevContainer

Embedded System have a lifetime of 10 or 15 years. During that time the software and tools has to be maintained. This can be very challenging. Who knows if that compiler or tool used is still available in 10 years from now? Additionally installing and configuring the tool chain and environment for a new team member is difficult. Even worse: using a different host operating system for the cross development can produce different results or introduce issues.

VS Code running with DevContainer
VS Code running with DevContainer

One solution for all these problems is to use Docker images and containers. I can pack all the necessary tools and software into a virtual environment and container. But developing inside a container comes with many challenges. In this article I’ll show how Visual Studio Code or VS Code makes working with containers very easy. In this article I show how easy it is to use modern development tools and methodologies for embedded development.

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GitLab Automated CI/CD Embedded Multi-Project Building using Docker

In CI/CD for Embedded with VS Code, Docker and GitHub Actions, I used GitHub to build a pipeline. This setup supports continuous integration within a CI/CD environment.

This time, let’s do a similar thing. But instead of GitHub, I use GitLab with VS Code. And I use it for a project where three different MCUs are used: the Raspberry Pi Pico-W, an Espressif ESP32 plus the NXP K22FX512 on the Sumo robot:

Raspberry Pi Pico-W, Espressif ESP32 and Robot with NXP K22FX512
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Processor Expert Components: 24-Dec-2024 Release

The year 2024 is coming to its end, time for a Year-End-Processor-Expert-Component-Release.

Processor Expert Components in Eclipse (KDS)

Also, this is now the 10th anniversary of the releases on Sourceforge, starting with https://mcuoneclipse.com/2014/10/21/mcuoneclipse-releases-on-sourceforge/ back in 2014.

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Tutorial: GNU Coverage with C++ for Embedded Applications

In an earlier article I explained how to generate GNU coverage information, for an embedded application written in C.

In this article, I show the steps and configuration needed to use GNU gcov targeting an embedded application with C++.

GNU gcov Coverage with C++ Application
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How to Use GNU Coverage (gcov) in a Freestanding Environment for Embedded Systems

The GNU Coverage (gcov) is a source code analysis tool, and is a standard utility in the GNU gcc suite. It works great in a hosted environment (e.g. Linux or Windows), where you have plenty of resources and a file system. But the gcov tools is relevant and usable for restricted embedded systems too. I have used it for years with the help of debug probes and file I/O semihosting. But semihosting does not come for free, depends on a library with support for constructors and destructors, plus relies on file I/O.

Fortunately, there is a way to use gcov without debugger, semihosting, file I/O and special system initialization: using a freestanding environment:

gcov in freestanding environment with embedded target

This article explains how to collect coverage information using a data stream for example over UART or USB-CDC. Key benefits are less code side, no need for a debugger or on-target file system, improved performance, better automation and flexible data collection.

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