When developing embedded systems using an RTOS (real-time operating system) on an Arm Cortex-M processor, properly determining the stack requirements for each task is crucial for avoiding stack overflows and system crashes. The key factors that influence stack usage are the RTOS itself, the Cortex-M core, and the application code. By analyzing these elements, stack sizes can be calculated to provide ample space for stack-intensive operations.
Stack Usage of the RTOS Kernel
The RTOS kernel utilizes the stack for its own operations like context switching between tasks. The amount of stack an RTOS consumes depends on the specific RTOS and its configuration. For example, FreeRTOS recommends a minimum stack size of 128 bytes for each task when using its default configuration. However, kernel stacks can be significantly larger with components like inter-task communication and stream buffers enabled. Checking the RTOS documentation for guidelines is crucial.
Impact of Cortex-M Core Architecture
The Cortex-M core architecture influences stack usage due to exception handling and context switching mechanisms. For example, Cortex-M3 and Cortex-M4 cores push up to 16 registers onto the current task’s stack when an exception occurs. And during context switches, the core automatically saves the necessary registers of the outgoing task. Therefore, the specific Cortex-M core and how the RTOS utilizes it will impact the stack.
Stack Requirements of Application Code
Beyond the RTOS and processor, the key determinant of stack requirements comes from the application code itself. Functions that declare large arrays, strings, or other objects on the stack can quickly consume space. Calls to other functions also increase usage. Code with multiple layers of nested function calls, recursion, or complex data manipulations require larger stack allocations per task. Analyzing the call graph and complexity is important.
Calculating Worst-Case Stack Usage
With knowledge of the RTOS, processor, and application code, maximum potential per-task stack usage can be calculated. One technique is to allocate variables in each task’s context that would mimic worst-case memory usage. The stack pointer can then be sampled before and after task execution to gauge the max usage. An additional 20-30% buffer on top of this measured worst-case is recommended.
Configuring RTOS Task Stacks
With stack requirements determined, the RTOS can be configured. Many RTOSs specify stack sizes in bytes during task creation. While tasks can share a stack, isolation per task works best. The linker script can contain stack sections for each task. Total stack usage across all tasks should fit within available SRAM. If stacks overflow during runtime, increasing the allocated sizes may be necessary.
Stack Overflow Detection
To detect stack overflows during runtime, stack canaries or MPU regions can be used. A known value is placed at the end of each task stack which gets overwritten if the stack overflows. The RTOS checks this value and flags overflows. Alternatively, the MPU can set a protected region at the bottom of each stack to trigger exceptions upon overflow. These mechanisms allow early detection and diagnosis of insufficient stack space.
Optimizing Stack Usage
If stack usage is too high, optimizations can reduce requirements. Minimizing unnecessary local variables, inlining functions instead of calling them, simplifying nested code, and eliminating recursion are good ways to trim stack usage. Choosing the optimal compiler optimization settings balances code size and stack usage. RTOS features like inter-task communication may need to be disabled if stacks are overflowing. Prioritizing stack reduction can enable an application to fit into smaller Cortex-M cores.
Determining accurate stack size requirements is necessary to create stable RTOS-based applications on Arm Cortex-M processors. By understanding the stack usage profiles of the RTOS kernel, Cortex-M core, and application code, maximum per-task stack heights can be calculated. Adding overflow detection and optimizations can further improve stack usage. Proper stack management ensures Cortex-M applications leveraging an RTOS avoid crashes and operate reliably.