Optical flow (OF) is an integral part of many vision systems, especially in the embedded and mobile application with ever-increasing challenges in achieving higher speed, minimal resource and lower power consumption. The work introduces a Dense High Throughput Optical Flow (DHTOF) architecture based on a novel fast converging Red-Black Successive Over Relaxation (RBSOR) solver architecture for computing dense and accurate OF using Horn and Schunck Optical Flow (HSOF) algorithm from Full High Definition (FHD) frames in real-time. The DHTOF architecture can capture dense OF from Ultra High Definition (UHD) frames at 48 Frames Per Second (FPS) with a throughput of 406 Megapixels/sec achieving a Throughput Per Watt (TPW) of 43 Giga Operation Per Second Per Watt (GOPS/Watt). The superscalar and deeply pipelined DHTOF architecture achieve same or lower Average Angular Error (AAE) with ≈ 4 × lesser number of RBSOR solver iterations as compared to the prior HSOF implementations based on Jacobi solver. It consumes 12.5 × lesser resources and 29.3% lower power for FHD resolution when compared to prior architectures. The proposed DHTOF architecture achieves highest area delay normalized speedup (at least by 28.2 ×) among the state of the art HSOF architectures. The successful evaluation of the proposed architecture for real-time OF sensor is demonstrated in Xilinx Virtex-VC707 Field Programmable Gate Array (FPGA) evaluation board.

光流（OF）是许多视觉系统不可或缺的一部分，尤其是在嵌入式和移动应用中，在实现更高速度，最小化资源和降低功耗方面面临越来越多的挑战。这项工作介绍了一种密集的高通量光流（DHTOF）架构，该架构基于一种新颖的快速收敛的红黑连续超松弛（RBSOR）解算器架构，该算法使用来自Full High Definition的Horn and Schunck光流（HSOF）算法来计算密集且精确的OF（FHD）实时帧。DHTOF体系结构可以捕获密集的OF它以每秒48帧（FPS）的超高清（UHD）帧和406兆像素/秒的吞吐率实现了每秒43 Giga Operation Per Watt（GOPS / Watt）的每瓦吞吐量（TPW）。与基于Jacobi求解器的现有HSOF实现相比，超标量和深度流水线DHTOF架构可实现相同或更低的平均角误差（AAE），且RBSOR求解器迭代次数减少了≈4×。与以前的架构相比，FHD分辨率消耗的资源少12.5倍，功耗降低29.3 ％。所提出的DHTOF体系结构在最先进的HSOF体系结构中实现了最高的区域延迟归一化加速（至少28.2倍）。对所提议的实时OF体系结构的成功评估Xilinx Virtex - VC 707现场可编程门阵列（FPGA）评估板演示了该传感器。