Ulutaş, UmutTosun, MustafaLevent, Vecdi EmreBüyükaydın, D.Akgün, T.Uğurdağ, Hasan Fatih2017-10-302017-10-302017978-3-319-67596-1http://hdl.handle.net/10679/5714https://doi.org/10.1007/978-3-319-67597-8_9Due to copyright restrictions, the access to the full text of this article is only available via subscription.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.engrestrictedAccessconferenceObject7788910110.1007/978-3-319-67597-8_9Altera SDK for OpenCLDense optical flowFPGAHigh-Level Synthesis2-s2.0-85029785920