When using the GNU-ARM toolchain to compile code for Cortex-M0/M1 microcontrollers, developers may encounter code generation issues that lead to inefficient or incorrect code. The Cortex-M0 and Cortex-M1 are low-power microcontroller cores designed for cost-sensitive and power-constrained embedded applications. Optimizing code size and performance is critical. This article provides an overview of common code generation problems and solutions when using GNU-ARM with these cores.
Loop Optimization
Due to the limited registers available on Cortex-M0/M1, loops may compile poorly with GNU-ARM. Issues include:
- Unnecessary reloading of loop counter each iteration
- Inefficient looping constructs generated
- Failure to optimize away loop overhead
This can lead to larger code size and lower performance. There are several ways to improve loop code generation:
- Use single induction variable for loop counter
- Minimize operations inside loops
- Use pragmas to suggest loop optimizations
- Select optimization flags carefully (e.g. -O3)
Proper loop coding techniques for these microcontrollers can help the compiler generate efficient looping machine code.
Register Allocation
The Cortex-M0 only has 8 general purpose registers available for allocation. The Cortex-M1 has 12 general purpose registers. Due to this constraint, register allocation can be a challenge for GNU-ARM. Issues that may occur include:
- Frequent reloading of values from stack
- Unnecessary spilling of registers to stack
- Excessive push/pop instructions around function calls
There are several ways to alleviate register pressure:
- Declare large data objects as static or global to avoid stack
- Minimize local variables
- Use smaller data types where possible
- Set optimization flags for size/speed tradeoff
Efficient register use is key for optimizing ARM code generation.
Conditional Execution
The Cortex-M0/M1 lack advanced branch prediction and deep pipelines of larger ARM cores. Conditional code can cause pipeline stalls if not optimized well. Issues that can occur:
- Branches decoded late due to large number of instructions between condition check and branch
- Branch penalties from incorrect static branch prediction
- Pipeline stalls from conditional execution based on flags
Solutions include:
- Minimize instructions between condition check and branch
- Use conditional instructions instead of branching where possible
- Optimize branching code to be predictable for static prediction
- Use optimization flags to favor branch chain merging
Efficient conditionally executed code is important for performance on Cortex-M0/M1.
Function Inlining
Inlining small functions can optimize call overhead for constrained Cortex-M0/M1 pipelines. However, GNU-ARM may fail to inline in some cases leading to larger code size. Some common issues:
- No inlining of static functions: must declare inline
- Functions not inlined across source files
- Larger functions not inlined due to code size increase
Solutions include:
- Declare small static functions as inline
- Use link time optimization to enable cross-module inlining
- Set inlining optimization flags
- Break large functions into smaller inlineable parts
Balancing inlining with code size increase is key for optimization.
Frame Pointer Omission
The frame pointer register (R11) may be unnecessarily allocated by GNU-ARM, using up a precious low register. This can occur due to:
- Compiler inability to prove frame pointer is not needed
- Presence of variable length stack allocations
- Lack of optimization flag indicating frame pointer not required
Solutions include:
- Omit stack probing variable length allocations
- Use optimization flags to indicate frame pointer not needed
- Set frame pointer to only be allocated when required
Eliminating unnecessary frame pointer use reduces register pressure.
Vector Table Optimization
The Cortex-M0/M1 vector table for interrupts and exceptions can waste code size if not optimized. Issues include:
- Table size not minimized based on needed vectors
- Lack of sharing for common interrupt service routines
- Table not placed in flash efficiently
Solutions include:
- Only define needed vectors, use toolchain to trim
- Use same handler for multiple interrupts if applicable
- Place table in flash using linker script
An efficient vector table reduces overhead and flash usage.
Literal Pool Placement
Constant literals and jump tables can increase code size if not managed properly. Issues include:
- Literal pools scattered through code haphazardly
- Lack of optimal flash placement for literal pools
- Failure to utilize PC-relative addressing modes
Solutions involve:
- Use compilation flags controlling literal pool placement
- Place literal pools efficiently using linker scripts
- Enable PC-relative addressing of constant data where possible
Careful literal pool placement and addressing reduces overhead.
Tail Call Optimization
Recursive algorithms and mutually recursive functions can benefit from tail call optimization. However, GNU-ARM may fail to optimize in some cases. This leads to unnecessary stack usage. The issues are:
- No tail call generation due to call stack requirements
- Lack of tail call optimization flags
- Recursive tail call requirements not met
Solutions include:
- Redesign algorithm to enable tail call optimization
- Use tail call friendly function prototypes
- Set compiler flags to enable aggressive tail call generation
Tail call optimization reduces stack overhead in recursive code.
Conclusion
Code generation for the constrained Cortex-M0/M1 requires careful toolchain usage and coding techniques. Following best practices for loops, conditionals, inlining, register use, literals, and other code structures can help enable GNU-ARM to produce optimal code. Leveraging an understanding of the underlying hardware and using the right compiler flags is key. With attention to these code generation details, developers can fully realize the performance and efficiency benefits of the Cortex-M0/M1 in embedded applications.
