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FPGA Implementation of a Dense Optical Flow Algorithm Using Altera OpenCL SDK

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Part of the Communications in Computer and Information Science book series (CCIS,volume 778)

Abstract

FPGA acceleration of compute-intensive algorithms is usually not regarded feasible because of the long Verilog or VHDL RTL design efforts they require. Data-parallel algorithms have an alternative platform for acceleration, namely, GPU. Two languages are widely used for GPU programming, CUDA and OpenCL. OpenCL is the choice of many coders due to its portability to most multi-core CPUs and most GPUs. OpenCL SDK for FPGAs and High-Level Synthesis (HLS) in general make FPGA acceleration truly feasible. In data-parallel applications, OpenCL based synthesis is preferred over traditional HLS as it can be seamlessly targeted to both GPUs and FPGAs. This paper shares our experiences in targeting a demanding optical flow algorithm to a high-end FPGA as well as a high-end GPU using OpenCL. We offer throughput and power consumption results on both platforms.

Keywords

  • Altera SDK for OpenCL
  • FPGA
  • High-Level Synthesis
  • Dense optical flow

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Acknowledgments

This work has been at the crossroads of multiple projects, namely, European Union Artemis JU Project called ALMARVI (GA 621439) [19] and TÜBİTAK (The Scientific and Technological Research Council of Turkey) projects ARDEB-114E343, KAMAG-114G029, and TEYDEB-9140015. More specifically, U. Ulutas is supported by KAMAG; M. Tosun and V.E. Levent were supported by ARDEB; D. Büyükaydın and T. Akgün were supported by TEYDEB; and H.F. Ugurdag has assumed leadership roles in the above Artemis, KAMAG, and ARDEB projects.

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Correspondence to Umut Ulutas .

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Ulutas, U., Tosun, M., Levent, V.E., Büyükaydın, D., Akgün, T., Ugurdag, H.F. (2017). FPGA Implementation of a Dense Optical Flow Algorithm Using Altera OpenCL SDK. In: Trajanov, D., Bakeva, V. (eds) ICT Innovations 2017. ICT Innovations 2017. Communications in Computer and Information Science, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-67597-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-67597-8_9

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