The ARM Cortex-M series of processors are extremely popular in embedded devices and IoT applications. They provide a good balance of performance, power efficiency and cost. Linux on the other hand is the most popular open-source operating system. Learning how to use the ARM Cortex-M processors with Linux allows developers to leverage the benefits of both worlds.
Getting an ARM Cortex-M development board
The first step is to get a development board with an ARM Cortex-M processor. Some popular options are:
- ST Microelectronics STM32 Discovery and Nucleo boards – These come with STM32 ARM Cortex-M0, M3 or M4 processors. There are boards like STM32F4 Discovery, STM32F7 Discovery, STM32H7 Nucleo etc.
- NXP LPCXpresso boards – Have NXP LPC ARM Cortex-M0/M3 processors. For example LPCXpresso54114 board.
- Microchip SAMD21 or SAMD51 based boards – Contain Microchip ATSAMD ARM Cortex-M0+ or M4 processors. Like the Adafruit Metro M0 or M4.
- TI Launchpad boards – Low cost boards with TI ARM Cortex-M processors like MSP432P401R.
- Teensy 3.x boards – These contain NXP ARM Cortex-M4 processors.
- Raspberry Pi Pico – An RP2040 ARM Cortex-M0+ board.
These development boards allow easy prototyping and development with the ARM Cortex-M processors. They come with programmer/debugger interfaces like SWD, JTAG and bootloaders which make programming straightforward.
Toolchain for ARM Cortex-M and Linux
To build applications for ARM Cortex-M processors, you need a cross compiler toolchain. This allows compiling code on your Linux machine which can then run on the Cortex-M processor. Some options for toolchains are:
- GNU Arm Embedded Toolchain – Provided by ARM, includes GCC compiler, GDB debugger and other tools. Can be downloaded from developer.arm.com.
- Arm Compiler 6 – ARM’s commercial toolchain with advanced optimizations.
- GCC ARM Embedded – GNU toolchain targeted at ARM processors. Available from launchpad.net/gcc-arm-embedded.
Most development boards come with some pre-built toolchain recommendations. It’s best to follow their guidelines on the optimal toolchain to use.
Setting up the development environment
Once you have the hardware board and toolchain, the next step is to setup your development environment on Linux. This includes:
- Installing the toolchain binaries on your Linux system PATH.
- Getting the board vendor’s software libraries and example projects.
- Installing the SEGGER J-Link tools if your board uses that for debugging.
- Downloading the IDE/editor of your choice – like Eclipse, Visual Studio Code etc.
- Testing with a simple blinky project to verify all the tools integrate correctly.
The board vendor provides detailed instructions on how to setup the development environment. Follow their steps to detect any issues early on. Having a working environment is crucial before diving deeper.
Learning the Processor Architecture
ARM Cortex-M processors have a clean RISC architecture. Some key aspects to learn are:
- General purpose registers, control registers and different processor modes.
- ARM Thumb instruction set used by Cortex-M cores.
- Nested Vectored Interrupt Controller (NVIC) for managing interrupts.
- Memory Protection Unit (MPU) for system protection.
- Bus interfaces like AHB, APB buses to connect peripherals.
- Debugging components like Debug Access Port (DAP).
- Low power features for operating in different power modes.
The ARM reference manuals detail the complete architecture. Start with the Cortex-M4 Technical Reference Manual as that covers the core architecture. Then you can look at Cortex-M3 or M0 manuals for differences.
Programming the ARM Cortex-M Processor
With the environment setup, you can start application development on ARM Cortex-M processors. Programming is typically done using C/C++ with assembly language where needed. Key aspects are:
- Write code using ARM’s CMSIS libraries which provide common interfaces.
- Use SVCall exceptions for Supervisor calls to the OS.
- Manage the Memory Protection Unit (MPU) regions.
- Use vendor HAL drivers to handle the peripherals.
- Advanced debugging using IDE integration and the SWD interface.
- Measuring code execution times, power usage to optimize performance.
- Using DSP intrinsics to leverage signal processing instructions.
Start with simple LED blink programs, move on to interfacing sensors, motors. The vendor libraries and example projects will demonstrate good programming practices.
Real-Time Operating System Concepts
Most applications use a Real-Time Operating System (RTOS) on the Cortex-M processor. The RTOS provides:
- Concurrency through multi-threading and scheduling.
- Synchronization primitives like mutexes, semaphores.
- Time handling features – delays, time measurement and timers.
- Memory management and allocation features.
- Inter-thread communication mechanisms like message queues.
Some popular RTOSes for ARM Cortex-M are FreeRTOS, ThreadX, Micrium uC/OS, Arm RTX. Going through RTOS concepts will help write robust applications.
The Cortex-M processors support a wide range of peripherals to connect various devices and sensors. Understanding how to use them is key.
- Serial buses like I2C, SPI for connecting sensors and SD cards.
- ADC and DAC for analog interfaces.
- Timers and PWM for generating precise signals.
- USB device for connecting as a peripheral to hosts.
- CAN, Ethernet, WiFi using external controller chips.
- Graphics LCD, Camera interfaces.
Start with simple LEDs, buttons, move on to add different peripherals. The datasheet and vendor code examples will be helpful.
Cortex-M processors complement Linux systems in many ways:
- Cortex-M chip handling real-time tasks, Linux provides application layer.
- Cortex-M as a subsystem, controlled by Linux through sysfs or other mechanisms.
- Custom Linux device drivers to control Cortex-M processors.
- Automated build systems to compile code for both Linux target and Cortex-M.
Look at boards like Zynq All Programmable SoC which combine ARM Linux processors with Cortex-M cores. This architecture provides the best of both worlds.
Machine Learning on Cortex-M
Machine Learning is becoming ubiquitous. Running ML algorithms efficiently on microcontrollers can enable many edge devices. The key aspects are:
- Using CMSIS-NN library for neural network layers.
- Quantizing networks to 8-bit fixed point for better performance.
- Generating optimized code with Arm NN compiler.
- Leveraging DSP instructions to speed up processing.
- Using ML frameworks like TensorFlow Lite Micro.
Start with simple anomaly detection using k-NN algorithm, move on to running small convolutional neural networks for image recognition.
Debugging on Target Hardware
Debugging directly on the target hardware helps identify issues quickly. Cortex-M processors have built-in debug features:
- SWD or JTAG interface for connecting the debugger.
- Breakpoints, watchpoints for stopping at specific conditions.
- Examining processor core registers, peripheral registers.
- Stepping through code, monitoring variables in memory.
- Recording trace data into buffers for offline analysis.
Use Eclipse, Visual Studio Code or proprietary IDEs that integrate such debugging tools. Having effective debugging skills is vital for embedded development.
This covers the overall workflow to go from zero experience to productively using the ARM Cortex-M processors. Some other avenues for learning further are:
- Reading application notes on vendor websites for useful examples.
- Trying different IDEs and compilers to find the optimal development toolchain.
- Participating in ARM Cortex-M communities to discuss with experts.
- Attending conferences like Embedded World to learn about new technologies.
- Considering Arm’s training courses on embedded and IoT development topics.
ARM Cortex-M is a very broad area. Focus on the fundamentals, then go deeper on aspects relevant to your specific application area for the best results.