Details

Autor(en) / Beteiligte

• Ul Mustafa, Naveed
• O’Riordan, Martin J.
• Rogers, Stephen
• Ozturk, Ozcan

Titel

Exploiting architectural features of a computer vision platform towards reducing memory stalls

Ist Teil von

• Journal of real-time image processing, 2020-08, Vol.17 (4), p.853-870

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Computer vision applications are becoming more and more popular in embedded systems such as drones, robots, tablets, and mobile devices. These applications are both compute and memory intensive, with memory bound stalls (MBS) making a significant part of their execution time. For maximum reduction in memory stalls, compilers need to consider architectural details of a platform and utilize its hardware components efficiently. In this paper, we propose a compiler optimization for a vision-processing system through classification of memory references to reduce MBS. As the proposed optimization is based on the architectural features of a specific platform, i.e., Myriad 2, it can only be applied to other platforms having similar architectural features. The optimization consists of two steps: affinity analysis and affinity-aware instruction scheduling. We suggest two different approaches for affinity analysis, i.e., source code annotation and automated analysis. We use LLVM compiler infrastructure for implementation of the proposed optimization. Application of annotation-based approach on a memory-intensive program shows a reduction in stall cycles by 67.44%, leading to 25.61% improvement in execution time. We use 11 different image-processing benchmarks for evaluation of automated analysis approach. Experimental results show that classification of memory references reduces stall cycles, on average, by 69.83%. As all benchmarks are both compute and memory intensive, we achieve improvement in execution time by up to 30%, with a modest average of 5.79%.