在超低功耗RISC-V集群上并行化光流估计以用于纳米无人机导航
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation
AI总结：
本文提出了一种针对新一代多核低功耗RISC-V MCU的光流计算并行化策略，在GAP8上实现7.21倍加速和500fps帧率，使微型无人机最大飞行速度翻倍且大幅降低时钟频率。
摘要：
光流估计对于无人机（UAV）的自主导航和定位至关重要。在微型和纳米无人机上，光流的实时计算在低功耗和资源受限的微控制器单元（MCU）上运行。因此，针对在传统单核MCU上的实时执行，已经提出了轻量级的光流算法。本文介绍了一种针对新一代多核低功耗基于RISC-V的微控制器单元的光流计算高效并行化策略。我们的方法在更低的时钟速度下实现了更高的帧率。它已在商用八核MCU（GAP8）的八核集群上实现并评估，达到了7.21的并行加速因子，在50 MHz时钟频率下运行时允许500帧每秒的帧率。提出的并行算法显著提升了微型无人机上的摄像头帧率，从而实现了更高的飞行速度：最大飞行速度可以翻倍，同时使用的时钟频率不到以前单核实现的三分之一。
英文摘要：
Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50 MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous single-core implementations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
