ARM Cortex-M7 Flash Access Performance Variability Due to Address Alignment
The ARM Cortex-M7 processor, known for its high performance and efficiency, can exhibit significant variability in execution time for certain operations, particularly when accessing data from flash memory. This variability is especially pronounced when performing looped load-byte (LDRB) operations from flash memory to core registers. The execution time of these operations can vary dramatically depending on the alignment of the data in flash memory. Specifically, when the data is 32-byte aligned, performance is optimal, but when the data is not aligned, performance degrades significantly. This behavior is not observed when the LDRB instruction is executed only once; it becomes apparent only during looped execution. Understanding the root causes of this performance variability and implementing appropriate solutions is crucial for optimizing firmware performance on the Cortex-M7.
Memory System Inefficiencies and Non-Word-Aligned Accesses
The primary cause of the performance variability in the Cortex-M7 when executing looped LDRB instructions from flash memory is related to the memory system’s handling of non-word-aligned accesses. The Cortex-M7 features a highly optimized memory system designed to maximize throughput and minimize latency. However, this optimization is most effective when data accesses are aligned to the natural boundaries of the memory system, typically 32 bytes in the case of the Cortex-M7.
When data is not aligned to these boundaries, the memory system must perform additional work to fetch the required data. This can involve fetching more data than necessary, resulting in increased latency and reduced throughput. In the case of looped LDRB instructions, this inefficiency is compounded, as each iteration of the loop may require the memory system to perform these additional fetches. This leads to a significant increase in execution time compared to when the data is aligned.
Another factor contributing to the performance variability is the Cortex-M7’s cache behavior. The Cortex-M7 includes an instruction cache (I-Cache) and a data cache (D-Cache), both of which are designed to reduce memory access latency. However, the effectiveness of these caches is highly dependent on the alignment of the data being accessed. When data is not aligned, cache line utilization is suboptimal, leading to increased cache misses and reduced performance.
Additionally, the Cortex-M7’s prefetch unit, which is responsible for fetching instructions and data ahead of time to reduce latency, can also be affected by non-aligned accesses. The prefetch unit may struggle to accurately predict the required data when it is not aligned, leading to inefficiencies in the prefetching process and further increasing execution time.
Implementing Data Alignment and Cache Optimization Techniques
To address the performance variability caused by non-aligned accesses in the Cortex-M7, several techniques can be employed to ensure optimal performance. These techniques focus on aligning data to the natural boundaries of the memory system and optimizing cache utilization.
Data Alignment: The most effective way to ensure optimal performance is to align data to 32-byte boundaries in flash memory. This can be achieved by manually aligning the data or by using compiler directives to enforce alignment. For example, in GCC, the __attribute__((aligned(32))) directive can be used to ensure that data is aligned to 32-byte boundaries. This ensures that the memory system can fetch the data efficiently, reducing latency and improving throughput.
Cache Optimization: Optimizing cache utilization is another key factor in improving performance. This can be achieved by ensuring that data is aligned to cache line boundaries, typically 32 bytes in the Cortex-M7. Additionally, the use of cache preloading techniques can help to reduce cache misses and improve performance. The Cortex-M7’s cache control registers can be used to preload data into the cache before it is accessed, reducing the likelihood of cache misses and improving overall performance.
Prefetch Optimization: The Cortex-M7’s prefetch unit can also be optimized to improve performance. This can be achieved by ensuring that data is aligned and by using prefetch hints to guide the prefetch unit. The Cortex-M7’s prefetch control registers can be used to provide hints to the prefetch unit, helping it to accurately predict the required data and reduce latency.
Loop Unrolling: While loop unrolling was suggested as a potential solution, it may not be practical in all cases, especially when dealing with large amounts of code. However, in cases where loop unrolling is feasible, it can help to reduce the overhead associated with looped LDRB instructions. By reducing the number of loop iterations, the number of LDRB instructions executed is also reduced, leading to improved performance.
Memory Barrier Instructions: The use of memory barrier instructions can also help to improve performance by ensuring that data is properly synchronized between the core and the memory system. The Cortex-M7 supports several memory barrier instructions, including the Data Synchronization Barrier (DSB) and the Data Memory Barrier (DMB). These instructions can be used to ensure that data accesses are properly ordered, reducing the likelihood of stalls and improving overall performance.
Compiler Optimizations: Finally, compiler optimizations can play a significant role in improving performance. Modern compilers, such as GCC and ARM Compiler, offer a range of optimization options that can help to improve the performance of code running on the Cortex-M7. These optimizations include instruction scheduling, loop unrolling, and data alignment. By enabling these optimizations, developers can ensure that their code is optimized for the Cortex-M7’s architecture, leading to improved performance.
In conclusion, the performance variability observed in the Cortex-M7 when executing looped LDRB instructions from flash memory is primarily due to the memory system’s handling of non-word-aligned accesses. By aligning data to 32-byte boundaries, optimizing cache utilization, and employing other techniques such as prefetch optimization and memory barrier instructions, developers can ensure optimal performance on the Cortex-M7. These techniques, when combined with compiler optimizations, can help to mitigate the performance variability and ensure that firmware runs efficiently on the Cortex-M7.
