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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pauwels, K., Tomasi, M., Diaz, J., Ros, E., Van Hulle, M.M.: A comparison of FPGA and GPU for real-time phase-based optical flow, stereo, and local image features. IEEE Trans. Comput. 61(7), 999–1012 (2012)
Bodily, J., Nelson, B., Wei, Z., Lee, D.-J., Chase, J.: A comparison study on implementing optical flow and digital communications on FPGAs and GPUs. ACM Trans. Reconfigurable Technol. Syst. 3(2), Article 6 (2010)
Muslim, F.B., Ma, L., Roozmeh, M., Lavagno, L.: Efficient FPGA implementation of OpenCL high-performance computing applications via high-level synthesis. IEEE Access 5, 2747–2762 (2017)
Hoffman, H.N.: Program acceleration in a heterogeneous computing environment using OpenCL, FPGA, and CPU. University of Rhode Island DigitalCommons@URI, Open Access Master’s thesis (2017)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of International Joint Conference on Artificial intelligence (IJCAI), San Francisco, CA, USA, vol. 2, pp. 674–679 (1981)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USA (1980)
Büyükaydın, D., Akgün, T.: GPU implementation of an anisotropic Huber-L1 dense optical flow algorithm using OpenCL. In: Proceedings of International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), Samos, Greece, pp. 326–331 (2015)
Shiralkar, M.: A self organization-based optical flow estimator with GPU implementation. All dissertations, p. 630 (2010)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for TV-L 1 optical flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03061-1_2
Janik, I., Tang, Q., Khalid, M.: An overview of Altera SDK for OpenCL: a user perspective. In: Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, NS, Canada, pp. 559–564 (2015)
Zohouri, H.R., Maruyamay, N., Smith, A., Matsuda, M., Matsuoka, S.: Evaluating and optimizing OpenCL kernels for high performance computing with FPGAs. In: Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, UT, USA, pp. 409–420 (2016)
Middlebury Optical Flow webpage. http://vision.middlebury.edu/flow/. Accessed June 2017
Hill, K., Craciun, S., George, A., Lam, H.: Comparative analysis of OpenCL vs. HDL with image-processing kernels on Stratix-V FPGA. In: Proceedings of IEEE International Conference on Application-Specific Systems, Architectures, and Processors (ASAP), Toronto, ON, Canada, pp. 189–193 (2015)
Altera SDK for OpenCL Programming Guide. https://www.altera.com/ja_JP/pdfs/literature/hb/opencl-sdk/aocl_programming_guide.pdf. Accessed June 2017
NALLATECH OpenCL A10 BSP Reference Guide. http://www.nallatech.com/store/pcie-accelerator-cards/nallatech-385a-arria10-1150-fpga. Accessed June 2017
Altera product selection guide. https://www.altera.com/content/dam/altera-www/global/en_US/pdfs/literature/pt/arria-10-product-table.pdf. Accessed June 2017
Güzel, A.E., Levent, V.E., Tosun, M., Özkan, M.A., Akgün, T., Büyükaydın, D., Erbas, C., Ugurdag, H.F.: Using high-level synthesis for rapid design of video processing pipes. In: Proceedings of East-West Design & Test Symposium (EWDTS), Yerevan, Armenia (2016)
Özkan, M.A., Reiche, O., Hannig, F., Teich, J.: FPGA-based accelerator design from a domain-specific language. In: Proceedings of International Conference on Field-Programmable Logic and Applications (FPL), Lausanne, Switzerland (2016)
Artemis JU Project, ALMARVI: Algorithms, Design Methods, and Many-Core Execution Platform for Low-Power Massive Data-Rate Video and Image Processing, GA 621439. http://www.almarvi.eu
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-67597-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67596-1
Online ISBN: 978-3-319-67597-8
eBook Packages: Computer ScienceComputer Science (R0)