Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles

TL;DR

This paper presents the F-1 bottleneck analysis model for designing onboard computing systems in autonomous UAVs, providing insights into component relationships to optimize performance and safety.

Contribution

The F-1 model offers a novel, visual, and validated approach for UAV system design, integrating sensor, compute, and mechanical factors for optimal performance.

Findings

F-1 model achieves 5.1% to 9.5% accuracy compared to real flight tests.

Provides a web-based interactive tool for UAV compute system design.

Enables system architects to optimize UAV performance and safety.

Abstract

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}