AI 中文总结
研究如何通过自动化端到端优化和部署DNN提高自主纳米无人机性能，聚焦PULP-Dronet在Crazyflie 2.1纳米无人机上的部署，实现内存占用减少、推理时间加速，提升了无人机在避障、自由飞行和车道跟随等方面的性能，还开源了相关软件设计。
AI 中文摘要
节能超低功耗（ULP）并行处理器的发展和卷积神经网络（CNN）的普及推动了自动驾驶纳米级无人机（UAV）的出现。纳米无人机上有限的计算/内存资源给基于视觉的CNN的最小化和优化带来挑战。本文探索方法和软件工具，以简化和自动化基于视觉的CNN导航在ULP多核片上系统上的部署，该系统作为Crazyflie 2.1纳米无人机的任务计算机。聚焦于PULP-Dronet的部署，从初始训练到最终闭环评估。结果显示内存占用减少2倍，推理时间加速1.6倍，保证相同预测精度并显著改善现场行为，实现避障、自由飞行和车道跟随等，且计算功耗不到无人机功率预算的1.6%。为促进新应用和未来研究，开源了与Crazyflie 2.1兼容的可运行项目的所有软件设计。
英文摘要
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10 cm robotic platforms are envisioned as next-generation ubiquitous smart-sensors and unobtrusive robotic-helpers. However, the limited computational/memory resources available aboard nano-UAVs introduce the challenge of minimizing and optimizing vision-based CNNs -- which to date require error-prone, labor-intensive iterative development flows. This work explores methodologies and software tools to streamline and automate all the deployment of vision-based CNN navigation on a ULP multicore system-on-chip acting as a mission computer on a Crazyflie 2.1 nano-UAV. We focus on the deployment of PULP-Dronet, a state-of-the-art CNN for autonomous navigation of nano-UAVs, from the initial training to the final closed-loop evaluation. Compared to the original hand-crafted CNN, our results show a 2x reduction of memory footprint and a speedup of 1.6x in inference time while guaranteeing the same prediction accuracy and significantly improving the behavior in the field, achieving: i) obstacle avoidance with a peak braking-speed of 1.65 m/s and improving the speed/braking-space ratio of the baseline, ii) free flight in a familiar environment up to 1.96 m/s (0.5 m/s for the baseline), and iii) lane following on a path featuring a 90 deg turn -- all while using for computation less than 1.6% of the drone's power budget. To foster new applications and future research, we open-source all the software design in a ready-to-run project compatible with the Crazyflie 2.1
Comments16 pages, 8 figures, 5 tables. This paper has been accepted for publication in the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) copyright 2021 IEEE
DOI：10.1109/JETCAS.2021.3126259
