Cortex-M7 vs. Cortex-M3 FIR Filter Performance Discrepancy Overview
The performance discrepancy between the Cortex-M7 and Cortex-M3 processors when executing a 128-tap FIR filter implementation has raised significant questions. The Cortex-M7, clocked at 600 MHz, demonstrates a runtime of 1260 microseconds for the FIR filter, while the Cortex-M3, clocked at 84 MHz, takes 44028 microseconds. This results in a performance ratio of approximately 35x, which is significantly higher than the expected 16x based on clock speed and dual-issue capabilities of the Cortex-M7. When measuring cycle counts using the DWT_CYCCNT register, the Cortex-M7 completes the task in 756,000 cycles compared to the Cortex-M3’s 3,698,352 cycles, yielding a cycle count ratio of 4.9x. This discrepancy suggests that the Cortex-M7’s architectural advantages extend beyond raw clock speed and dual-issue execution.
The FIR filter implementation involves a nested loop structure with two load operations (LDR) and a multiply-accumulate operation (SMLAL) in the inner loop. The Cortex-M7’s superior performance can be attributed to its advanced microarchitecture, including a deeper pipeline, branch prediction, and cache subsystems, which mitigate the performance penalties associated with memory accesses and instruction flow. However, the exact contribution of each feature to the observed performance gain remains unclear, necessitating a detailed exploration of the underlying causes.
Cortex-M7 Microarchitectural Advantages and Memory Access Bottlenecks
The Cortex-M7’s performance advantage over the Cortex-M3 can be attributed to several microarchitectural enhancements, including its 6-stage pipeline, dual-issue capability, branch prediction, and cache subsystems. These features collectively reduce the impact of memory access bottlenecks and instruction flow disruptions, which are more pronounced in the Cortex-M3’s simpler 3-stage pipeline.
Pipeline Depth and Dual-Issue Execution
The Cortex-M7’s 6-stage pipeline allows for greater instruction-level parallelism compared to the Cortex-M3’s 3-stage pipeline. This deeper pipeline enables the Cortex-M7 to fetch, decode, and execute more instructions concurrently, reducing the likelihood of pipeline stalls. Additionally, the Cortex-M7’s dual-issue capability allows it to execute two instructions per cycle under optimal conditions, further enhancing throughput. In contrast, the Cortex-M3’s simpler pipeline is more susceptible to stalls caused by data dependencies and memory access latencies.
Branch Prediction
The Cortex-M7 incorporates a branch predictor, which reduces the performance penalty associated with conditional branches in the FIR filter’s inner loop. By predicting the outcome of branches, the Cortex-M7 can prefetch and execute instructions along the predicted path, minimizing pipeline flushes and stalls. The Cortex-M3, lacking branch prediction, incurs a higher penalty for mispredicted branches, leading to increased cycle counts.
Cache Subsystems
The Cortex-M7 features separate instruction and data caches, which significantly reduce memory access latencies. In the FIR filter implementation, the Cortex-M7’s caches mitigate the performance impact of the two LDR operations in the inner loop by providing faster access to the FIR coefficients and input signal data. The Cortex-M3, without caches, experiences higher memory access latencies, leading to more frequent pipeline stalls and increased cycle counts.
Memory Access Bottlenecks
The FIR filter’s inner loop involves two LDR operations, which access the FIR coefficients and input signal data from memory. These memory accesses are a critical bottleneck, particularly for the Cortex-M3, which lacks caches and has a simpler pipeline. The Cortex-M7’s caches and deeper pipeline reduce the impact of these memory accesses, resulting in fewer stalls and lower cycle counts.
Optimizing FIR Filter Performance on Cortex-M7 and Cortex-M3
To fully leverage the Cortex-M7’s architectural advantages and mitigate the Cortex-M3’s performance bottlenecks, several optimization strategies can be employed. These strategies focus on reducing memory access latencies, minimizing pipeline stalls, and maximizing instruction-level parallelism.
Data Alignment and Cache Utilization
Ensuring that the FIR coefficients and input signal data are aligned to cache line boundaries can improve cache utilization and reduce memory access latencies on the Cortex-M7. This alignment minimizes cache misses and ensures that the data required for the FIR filter’s inner loop is readily available in the cache. On the Cortex-M3, where caches are absent, data alignment can still improve memory access efficiency by reducing the number of memory accesses required to fetch the data.
Loop Unrolling and Software Pipelining
Loop unrolling and software pipelining can be used to reduce the overhead of loop control instructions and maximize instruction-level parallelism. By unrolling the inner loop of the FIR filter, the number of loop control instructions is reduced, allowing the Cortex-M7’s dual-issue capability to be more effectively utilized. Software pipelining can further reduce pipeline stalls by overlapping the execution of multiple iterations of the loop.
Compiler Optimizations
Compiler optimizations, such as enabling high optimization levels and using specific compiler flags, can significantly improve the performance of the FIR filter on both the Cortex-M7 and Cortex-M3. For example, enabling the -O3 optimization level in GCC can result in more aggressive instruction scheduling and loop unrolling, reducing cycle counts. Additionally, using the -mcpu=cortex-m7 or -mcpu=cortex-m3 flags ensures that the compiler generates code tailored to the specific architecture, further enhancing performance.
Memory Access Patterns
Optimizing memory access patterns can reduce the number of memory accesses and improve cache utilization. For example, accessing the FIR coefficients and input signal data in a sequential manner can improve spatial locality, reducing cache misses on the Cortex-M7. On the Cortex-M3, sequential memory accesses can reduce the number of memory accesses required to fetch the data, improving performance.
Cycle-Accurate Profiling
Using cycle-accurate profiling tools, such as the DWT_CYCCNT register, can provide detailed insights into the performance of the FIR filter on both the Cortex-M7 and Cortex-M3. By measuring the cycle counts for specific sections of the code, performance bottlenecks can be identified and addressed. For example, if the cycle counts for the LDR operations in the inner loop are disproportionately high, optimizations can be focused on reducing memory access latencies.
Table: Comparison of Cortex-M7 and Cortex-M3 FIR Filter Performance
| Feature | Cortex-M7 | Cortex-M3 |
|---|---|---|
| Clock Speed | 600 MHz | 84 MHz |
| Pipeline Depth | 6-stage | 3-stage |
| Dual-Issue Execution | Yes | No |
| Branch Prediction | Yes | No |
| Cache Subsystems | Instruction and Data Caches | No Caches |
| FIR Filter Runtime | 1260 us | 44028 us |
| Cycle Count | 756,000 cycles | 3,698,352 cycles |
| Performance Ratio | 35x (runtime), 4.9x (cycle count) | 1x (baseline) |
Conclusion
The observed performance discrepancy between the Cortex-M7 and Cortex-M3 in executing the FIR filter is primarily due to the Cortex-M7’s advanced microarchitectural features, including its deeper pipeline, dual-issue capability, branch prediction, and cache subsystems. These features collectively reduce the impact of memory access bottlenecks and instruction flow disruptions, resulting in significantly lower cycle counts and runtime. By employing optimization strategies such as data alignment, loop unrolling, compiler optimizations, and cycle-accurate profiling, the performance of the FIR filter can be further enhanced on both architectures. Understanding the specific contributions of each microarchitectural feature to the performance gain is essential for developing efficient embedded systems and leveraging the full potential of ARM processors.
