论文标题
无人机的车顶线模型：用于自动无人机的板载计算表征的瓶颈分析工具
Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles
论文作者
论文摘要
我们介绍了一种称为F-1的早期相瓶颈分析和表征模型，用于设计针对自动无人驾驶飞机（UAV）的计算系统。该模型通过利用自动无人机中各种组件（例如传感器，计算和身体动力学）之间的基本关系来提供见解。为了确保安全操作，同时必须仔细选择或设计了无人机的性能（例如，速度），计算，传感器和其他机械性能。 F-1模型提供了视觉见解，可以帮助系统架构师了解自动无人机的最佳计算设计或选择。该模型是使用Real UAV对实验验证的，与现实世界飞行测试相比，该误差在5.1 \％至9.5 \％之间。可以免费使用的F-1基于网络的工具，称为Skyline的工具：〜\ url {https://bit.ly/skyline-tool}
We introduce an early-phase bottleneck analysis and characterization model called the F-1 for designing computing systems that target autonomous Unmanned Aerial Vehicles (UAVs). The model provides insights by exploiting the fundamental relationships between various components in the autonomous UAV, such as sensor, compute, and body dynamics. To guarantee safe operation while maximizing the performance (e.g., velocity) of the UAV, the compute, sensor, and other mechanical properties must be carefully selected or designed. The F-1 model provides visual insights that can aid a system architect in understanding the optimal compute design or selection for autonomous UAVs. The model is experimentally validated using real UAVs, and the error is between 5.1\% to 9.5\% compared to real-world flight tests. An interactive web-based tool for the F-1 model called Skyline is available for free of cost use at: ~\url{https://bit.ly/skyline-tool}
