When developing applications for Arm Cortex-M based microcontrollers, properly estimating the stack memory requirements is crucial for optimizing memory usage and avoiding stack overflows. This article provides a comprehensive guide on techniques and best practices for calculating your Cortex-M application’s stack needs.
What is the Stack?
The stack is a region of memory used to temporarily store information during function calls and interrupt handling. It works on a last-in, first-out basis, meaning the most recently pushed data is accessed first. The stack grows and shrinks dynamically at runtime as functions are called and return. All local variables, parameters, return addresses, and temporary data for a function are stored on the stack.
Why Estimate Stack Requirements?
Arm Cortex-M microcontrollers have limited RAM and ROM. The stack resides in RAM and incorrect sizing can lead to a few key issues:
- Too small – Stack overflow errors occur when the stack overwrites other data
- Too large – Wasted memory that could be used for other data
By properly estimating stack usage, you can minimize RAM requirements and avoid overflows. This is especially important for memory constrained devices.
Factors Impacting Stack Usage
Several factors influence stack requirements including:
- Nested function calls – More nested calls mean more stack variables to store
- Interrupt handler usage – ISRs require stack space
- Number and size of local variables – More and larger locals consume more stack
- Usage of temporary data – Data used temporarily during function execution
- Compiler optimizations – Disabling optimizations increases stack usage
- Alignment requirements – Data alignment affects stack usage
Understanding how each of these factors impacts memory is important for an accurate estimate.
Estimation Techniques
There are several techniques available to estimate stack requirements:
1. Hardware-Based Techniques
These rely on hardware features to determine stack usage:
- MCU STACK – ST Microelectronics feature that measures stack height
- PSP – Read ARM’s process stack pointer register to get current stack value
The disadvantage is lack of portability and lack of support on all MCUs.
2. Compiler-Assisted Techniques
Most compiler toolchains provide options to determine stack usage:
- .map file – View stack usage per function/module
- Simulators – Step through code to simulate stack
- Stack analysis tools – Plugins to analyze stack usage
These techniques provide good estimations but aren’t always perfect. Simulations can miss some scenarios.
3. Manual Calculation
For simple applications, manually calculating stack usage is feasible:
- Calculate data sizes of local variables
- Add data sizes for each nested function call
- Consider impact of interrupts
- Add some buffer as safety margin
Manual calculation allows complete control but lacks automation and can miss complex interactions. Use for simpler functions.
Best Practices for Accurate Estimation
Follow these tips for the most accurate stack estimations:
- Know your toolchain – Compiler impacts stack usage
- Leverage available tools/features – .map files, simulators, etc.
- Measure across multiple scenarios – Different conditions change stack usage
- Add safety margin – Add buffer for unknowns
- Prototype on real hardware – Measure actual hardware usage
- Re-validate with software changes – Any changes can alter stack usage
- Tune compiler optimizations – Optimizations reduce stack usage
Combining the automated analysis tools with some manual techniques and measurements generally provides the most reliable estimations.
Optimizing Stack Usage
Once you have an estimate, optimize stack usage by:
- Minimizing variables scope
- Declaring variables static when possible
- Reducing nested function calls
- Using compiler optimizations
- Allocating large buffers dynamically
Achieving an optimal stack size provides benefits such as:
- Lower memory requirements
- Avoid stack overflows
- Improve performance
- Allow more room for heap usage
With careful planning, you can determine the stack requirements for your Arm Cortex-M application and optimize memory usage.
Conclusion
Estimating stack requirements for Arm Cortex-M applications requires analyzing multiple factors including function calls, interrupt usage, variable sizes, and compiler settings. Leveraging toolchain specific features provides the most accurate estimations. Following best practices such as adding safety margins, measuring across scenarios, and prototyping on hardware helps validate the estimates. With the estimate complete, the stack usage can be optimized through techniques such as reducing variable scope and compiler optimizations. Properly sizing the stack is key to avoiding overflows while minimizing wasted RAM.
