December 15th, 2017 by
Deterministic digital signal processing (DSP) programming optimization guidelines are blueprints for writing efficient codes. The guidelines can be applied to a wide range of DSP compilers. These guidelines include:
Using DMA & Wise Memory Data Allocation
Using DMA controllers frees up deterministic digital signal processing(DSP)core to handle other tasks. So, developers must not place arrays at the beginning or end of memory blocks to prevent creating software pipeline problems. Software pipelining enhances tight loops by adding a data set when DSP is executing a previous task. But, if an array is placed on the memory edge, the loop’s last iteration should try to fetch data beyond the memory space. Compilers are tasked to execute the last iteration slowly to avoid an error from occurring.
Choosing Variable Data Types
Developers ought to understand the deterministic digital signal processing internal architecture they choose to work on, in order to use the native DSP data type effectively. Operations under the native data types tend to be fast because they are implemented by hardware. On the other hand, operations under emulated data types are executed via software functions; hence, they take up more resources and are slow.
Avoiding Data Aliasing
Aliasing arises when many valuables point to similar data. This is a situation that could disrupt optimization as the compiler analyzes the code to define when aliasing could occur. If two or more pointers point to different data, the compiler will act conservatively to avoid optimization and maintain the program’s accuracy and correctness.
Write Code Loops Cautiously
Loops are present in deterministic digital signal processing algorithms. Meaning that their coding can influence program execution performance effortlessly. Control statements and function calls must be avoided within a loop to prevent pipeline flushes. To fit in a DSP cache memory entirely and ease local repeat optimization, loop code must be kept small. In the presence of numerous loops, the compilers focus optimization efforts to inner loops. Pulling operations from outer lops to inner loops can boost system performance.
Aware of Time-Consuming Operations
Operations like division do not contain hardware support for single cycle implementation. Hence, they are implemented by functions executing iterative approximations algorithms like the Newton Raphson. For instance; a power of two operation division can be converted to an easy unsigned variables right shift. Deterministic digital signal processing developers should be aware of this fact and try their best to avoid them when possible.
DSP Software Impacts Power Optimization
Deterministic digital signal processing software impacts power consumption significantly. In most cases, software with efficient processor cycles for carrying out its tasks is energy efficient as well. The software should minimize the access required for off-chip memory. The power required to access off-chip memory is always higher than the power used to access on-chip memory. The power consumption can be optimized in DSP supporting selective disablement of unutilized functional blocks.
Just like all systems, deterministic digital signal processing must be implemented right to ensure maximum functionality. Above are just a few guidelines and developers should be on the lookout for upcoming trends and required programming changes.