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      • Department of Computer Engineering
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      Exploiting architectural features of a computer vision platform towards reducing memory stalls

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      Author(s)
      Mustafa, Naveed Ul
      O’Riordan, M. J.
      Rogers, S.
      Öztürk, Özcan
      Date
      2020
      Source Title
      Journal of Real-Time Image Processing
      Print ISSN
      1861-8200
      Publisher
      Springer
      Volume
      17
      Issue
      4
      Pages
      853 - 870
      Language
      English
      Type
      Article
      Item Usage Stats
      74
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      99
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      Abstract
      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%.
      Keywords
      Computer vision
      Compiler optimization
      Execution time
      Memory bound stalls
      Permalink
      http://hdl.handle.net/11693/75485
      Published Version (Please cite this version)
      https://dx.doi.org/10.1007/s11554-018-0830-8
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