The Cortex-M4 processor is designed to provide high performance and low power consumption in embedded applications. However, the load and store instructions can exhibit high latency due to cache misses and bus contention. This article will examine techniques to reduce the load/store latency and improve overall performance on Cortex-M4 based systems.
Understanding Load/Store Latency
The Cortex-M4 has a three stage pipeline – Fetch, Decode and Execute. The load and store instructions access the data bus during the Execute stage. However, if the requested data is not available in the level 1 data cache, it leads to a cache miss. Fetching the data from lower level memories introduces additional latency cycles. The processor stalls until the data becomes available. This increases the effective execution time of the load/store instruction.
In addition to cache misses, the load/store instructions also contend for the shared data bus. So even if the data is present in L1 cache, the instruction may have wait due to ongoing bus transactions by other masters. The Cortex-M4 bus interface uses a round-robin scheduler to arbitrate between multiple bus masters. But this can still lead to high latency for load/store instructions.
Analyzing Load/Store Behavior
To reduce load/store latency, we first need to analyze their behavior in the target application. Some key aspects to examine are:
- Frequency of load/store instructions
- Cache hit/miss ratios for data accesses
- Bus contention scenarios
- Nature of data – random or sequential addresses
- Possibility of conflicts between instruction and data accesses
Profiling tools like Arm DS-5 Streamline can be used to get detailed statistics regarding stall cycles, cache performance and bus transactions. This data will pinpoint the exact load/store instructions that need optimization.
Optimizing Memory Architecture
The first step is optimizing the memory architecture for our specific application requirements. This involves tuning the cache configuration, bus bandwidth allocation and mapping critical data into faster memories.
Cache Configuration
The Cortex-M4 supports different cache sizes up to a max of 64 KB. Choosing an optimal cache size avoids wastage of on-chip memory. The cache also needs to be configured as Harvard architecture with separates instruction and data caches. This prevents contention between instruction fetches and data accesses.
If the application has significant temporal locality in data accesses, increasing the cacheline size can help improve hit rate. Configuring cache write policy as write-through or write-back is another parameter than can be tuned.
Bus Bandwidth Allocation
The bus matrix allows assigning priorities and bandwidth allocation for different bus masters. Latency sensitive transactions like data cache misses should be assigned higher priority compared to bulk data transfers by DMA. Using the Arm CoreLink NIC-400 network interconnect can also help reduce contention.
Faster Memory Mapping
DDR memory has higher access latency than on-chip SRAM. Critical data structures and code segments can be mapped into on-chip memory regions to avoid going to external DRAM. Similarly, flash access can be accelerated by mapping hot code regions into SRAM rather than executing directly from flash.
Compiler Optimizations
The compiler plays a major role in scheduling load/store instructions to minimize stalls. Following compiler techniques help reduce latency:
- Grouping loads before stores to exploit temporal locality
- Software pipelining to overlap cache misses
- Loop inversion and interchange to improve locality
- Unrolling small loops that reuse data
- Allocating variables to registers instead of memory
Compiler flags like -O3 enable the highest level of optimizations. The Arm Compiler 6 toolchain has “optimize for speed” and “optimize for size” modes that impact load/store ordering.
Manual Assembly Coding
For critical code sections, assembly level programming can be used to carefully schedule load/store instructions. Some manual optimizations include:
- Separating instruction and data accesses
- Using double buffering techniques
- Aligning data structures to cache line size
- Prefetching data using PLD instruction
- Unrolling small loops for better pipelining
The LDREX and STREX atomic instructions are also useful for synchronization instead of mutexes.
Data Access Patterns
How the data is accessed can significantly impact load/store latency. Sequential accesses perform better than random accesses. Consolidating disjoint data into structures improves locality. Some ways to optimize data access include:
- Migrating from linked lists to contiguous arrays
- Sorting data based on access order
- Blocking for matrix or image processing
- Storing multiple elements in single physical location
- Using DMA for bulk transfers
Software prefetching using PLD instruction is useful for predictive loading of data elements.
Parallelism Techniques
Various parallel computing techniques can help hide the load/store latency by executing independent instructions during stall cycles.
SIMD Instructions
The Cortex-M4 SIMD instructions operate on multiple data elements concurrently. This improves performance for multimedia and signal processing code.
Multithreading
Executing multiple threads or tasks on a multi-core Cortex-M4 system allows overlapping computation and data access. Context switching helps tolerate latency.
Transaction Level Parallelism
Multiple outstanding bus transactions can be issued to DRAM to maximize bandwidth utilization. The Arm AXI protocol enables out-of-order completion to improve overlap.
Hardware Acceleration
Additional hardware blocks can offload the Cortex-M4 core from intensive data processing tasks. Some options are:
- DMA engines for efficient bulk transfers
- External accelerators and co-processors
- Cryptographic coprocessors for encryption
- Graphic accelerators for image processing
This avoids the M4 core stalling on frequent load/store accesses.
Conclusion
Optimizing load/store instruction latency requires a combination of memory architecture tuning, compiler optimizations, efficient data access patterns and parallelism techniques. Profiling the application usage is key to identifying optimization opportunities. For minimal latency, critical code segments can be hand-coded in assembly language. Hardware accelerators are useful to offload intensive data processing tasks. Overall, a balanced system-level approach is needed to maximize Cortex-M4 performance.
