ARM Cortex-M3 Programming Techniques

The ARM Cortex-M3 is a 32-bit processor core licensed by Arm Holdings. It is part of the Cortex-M series of microcontrollers and is designed for embedded applications requiring high performance and low power consumption. The Cortex-M3 core has a 3-stage pipeline, uses the Thumb-2 instruction set, and includes features like a Memory Protection Unit, low latency interrupt handling, and optional high speed bus interface. When programming the Cortex-M3, developers can take advantage of various techniques to optimize performance, memory usage, power consumption, and more.

Compiler Optimizations

Choosing the right compiler and optimization settings is key for getting good performance from Cortex-M3 code. Here are some compiler techniques to consider:

• Use an optimizing compiler like gcc or the ARM compiler – they will generate faster and smaller code than a non-optimizing compiler.
• Enable optimizations like -O2 or -O3 to get the compiler to generate more efficient instruction sequences.
• Use link time optimizations (LTO) to allow optimizations across compilation units.
• Profile the code to see where the hotspots are and focus optimizations on those areas.
• Enable optimizations for code size like -Os if minimizing firmware size is critical.
• Use compiler intrinsics for efficient bit operations, saturating math, etc.
• Use packed structs to allow register-sized data types when size and performance matter.

Instruction Scheduling

The Cortex-M3 pipeline allows basic instruction scheduling optimizations like:

• Placing independent instructions that don’t affect flags next to conditional branches.
• Scheduling load/stores to fall into load/store gaps.
• Unrolling small loops to hide stalls from branches and flag-setting instructions.
• Assigning code having data dependency to different execution units.

This helps increase instruction-level parallelism and improves performance without impacting cycle count.

Loop Optimizations

Tight loops are common in embedded applications, so optimizing them is essential. Some loop optimization techniques include:

• Loop unrolling – reduces branch overhead by replicating the loop body.
• Loop inversion and reversal – improves cache performance by accessing memory sequentially.
• Strength reduction – replaces expensive operations with simpler ones, like multiply with shift.
• Loop fusion – merges similar loops to reduce overhead.
• Loop fission – breaks a loop into multiple loops over the same data to expose parallelism.

Manual loop unrolling is especially useful for small loops where the iteration count is known at compile time.

Reducing Function Call Overhead

Function calls have overhead from pushing/popping stacks and registers. To reduce this:

• Declare small functions as inline so they are inserted into the caller rather than being real function calls.
• Use linker optimizations like tail call optimization to eliminate call/return sequences.
• Pass function parameters in registers instead of the stack where possible.
• Consider using macros instead of functions for very small or frequently called routines.

Optimizing Data Layout

Careful data structure layout can improve performance. Ideas include:

• Packing structs so they fit exactly in registers.
• Ordering struct fields from most-frequently to least-frequently accessed.
• Aligning variables and arrays to word boundaries.
• Placing frequently accessed variables in the first 64KB for faster loads/stores.
• Using bitfields and bitbanding to access specific bit ranges in registers.
• Using unions to reinterpret the same memory as different data types.

Using DMA and Peripherals Intelligently

The Cortex-M3 DMA and peripheral accelerators reduce load on the CPU. Make use of them by:

• Using DMA for bulk memory transfers instead of CPU copy loops.
• Chaining multiple DMA transfers together for large data sets.
• Triggering DMA transfers from peripheral events rather than CPU.
• Handling peripherals like crypto and CRC in hardware rather than software.
• Using peripherals that offload real-time tasks like PWM, ADC, Timers.
• Designing ISRs to be short and non-blocking by offloading work to peripherals.

Optimizing Interrupt Handling

The Cortex-M3 was designed for low interrupt latency. Useful techniques include:

• Minimizing stack usage in ISRs to reduce latency.
• Avoiding function calls in ISRs and keeping ISR code short.
• Using fast interrupt vectors to reduce pipeline flushes.
• Prioritizing and nesting interrupts appropriately.
• Using interrupt masking sparingly to avoid priority inversion.
• Offloading ISR work to main loop or peripherals.

Power Optimization Techniques

For low power applications, consider:

• Minimizing switching on GPIOs to reduce dynamic power.
• Using lower clock speeds when possible by gating the clock or transitioning to sleep modes.
• Disabling peripherals when not in use.
• Putting the processor into sleep mode whenever idle.
• Waking up periodically instead of using interrupts to reduce static power.
• Using DMA and peripherals instead of active CPU computation.
• Efficiently ordering code to maximize sleep time.

Memory and Storage Optimization

Careful use of limited memory resources includes:

• Minimizing stack usage by reusing buffers and keeping stack frames small.
• Using compiler memory allocation optimizations.
• Allocating critical data to faster memory like TCM.
• Reducing initialized data size and moving to flash.
• Placing frequently accessed data and code into tightly coupled memory.
• Compressing tables and constants in flash and decompressing at runtime.
• Using memory pools and custom allocators instead of heap.

Testing and Profiling

To identify optimization opportunities:

• Profile code execution to find hotspots – use debuggers, counters, etc.
• Add timers or counters to see how long routines take to execute.
• Monitor stack usage to check for problems and inefficiencies.
• Generate coverage reports to find unused code to remove.
• Run code linting tools to catch issues early.
• Test corner cases and stress inputs to find weaknesses.
• Simulate at high versus low clock frequencies to reveal bottlenecks.

By applying combinations of these Cortex-M3 programming techniques, developers can create high-performance embedded firmware that makes the best use of available resources. Techniques can be combined and tailored to match the requirements and constraints of each system.

Conclusion

ARM Cortex-M3 microcontrollers provide a balance of performance, power efficiency, and features like memory protection that make them popular for many embedded systems. This article outlined some of the key programming techniques developers can use to optimize Cortex-M3 applications for their design goals. Applying compiler optimizations, efficient data layout, intelligent use of DMA and peripherals, careful interrupt handling, power management, and rigorous testing enables developers to maximize application performance within the constraints of an embedded system built around the Cortex-M3 CPU.