Benchmarking Finite Order Filter Algorithms On Low Power Embedded Processors : Computing Efficiency

Abstract

Efficient   signal   processing   in  embedded   systems hinges  on  selecting  the  right   microcontroller  for  your  Finite Impulse  Response  (FIR)  filter  needs,  yet engineers  often  struggle  without  clear  guidelines  for  making   this  crucial  decision. Our  research tackles  this  practical challenge  by  systematically studying  how different  filter  complexities  affect  microcontroller performance  across  key  areas   like  processing  speed,  memory usage,  CPU  load,  and  power  consumption.  We discovered  that complex, high-order filters need powerful  microcontrollers with robust processing  capabilities  and ample memory,  while simpler, low-order   filters  work  perfectly  well  on  energy-efficient,  low- power  chips  that  prioritize battery life over  raw  performance. The results indicate that a 51^st-order filter utilizes nearly 76% of the available 2 KB SRAM, beyond which the system exhibits frequent instability stemming from insufficient memory allocation for reliable runtime operations. These findings give embedded  system designers a straightforward framework for choosing microcontrollers that strike the right balance  between getting  the job done and  conserving  power.  By understanding these  relationships, engineers  can  make  smarter hardware decisions that  deliver  effective signal processing  while keeping  systems  efficient and  sustainable,  ultimately  leading  to better-performing embedded  devices  that  don’t  waste  precious resources.