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

arXiv: https://arxiv.org/abs/2204.10898 · HTML: https://mlsanity.com/paper/2204.10898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI](https://mlsanity.com/paper/2401.01040/) (2024)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips](https://mlsanity.com/paper/2308.05869/) (2023)

## Abstract (arXiv)

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: ~urlhttps://bit.ly/skyline-tool