Abstract
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonetheless, their size and simple electronics pose severe challenges in implementing advanced onboard intelligence. This work proposes a new automatic optimization pipeline for visual pose estimation tasks using Deep Neural Networks (DNNs). The pipeline leverages two different Neural Architecture Search (NAS) algorithms to pursue a vast complexity-driven exploration in the DNNs' architectural space. The obtained networks are then deployed on an off-the-shelf nano-drone equipped with a parallel ultra-low power System-on-Chip leveraging a set of novel software kernels for the efficient fused execution of critical DNN layer sequences. Our results improve the state-of-the-art reducing inference latency by up to 3.22x at iso-error.
Abstract (translated)
微型自主无人机（UAVs）因尺寸小巧而备受欢迎，可执行诸如室内导航或人员监测等新任务。然而，它们的尺寸和简单的电子元件在实现高级车载智能方面提出了严重挑战。本文提出了一种使用深度神经网络（DNN）进行视觉姿态估计任务的自动优化管道。该管道利用两种不同的神经架构搜索（NAS）算法，在DNN的架构空间中进行广泛的复杂性驱动探索。获得的网络随后部署在一台配备具有并行超低功耗系统级芯片的消费级纳米无人机上，利用一系列新的软件内核实现关键DNN层序列的高效融合执行。我们的结果将先进的推理延迟降低了至 ISO 错误次数的3.22倍。
URL
https://arxiv.org/abs/2402.15273
