Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their outstanding ability to be used in different sectors and serve in difficult and dangerous areas. Moreover, the advancements in computer vision and artificial intelligence have increased the use of UAVs in various applications and solutions, such as forest fires detection and borders monitoring. However, using deep neural networks (DNNs) with UAVs introduces several challenges of processing deeper networks and complex models, which restricts their on-board computation. In this work, we present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board and finds the minimum decision-making latency. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. The formulated optimization solution is an NP-hard problem. Hence it is not adequate for online resource allocation. Therefore, we introduce an online heuristic solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs. The proposed approach is general enough to be used for different low decision-latency applications as well as for all CNN types organized into the pipeline of layers (e.g., VGG) or based on residual blocks (e.g., ResNet).

翻译：最近,无人驾驶航空飞行器(无人驾驶飞行器)因其在不同部门使用和在困难和危险地区服务的出色能力而引起极大关注;此外,计算机视觉和人工智能的进步增加了在各种应用和解决办法(如森林火灾探测和边界监测)中使用无人驾驶航空飞行器的情况;然而,利用无人驾驶航空飞行器使用深神经网络(DNNS)带来了若干挑战,处理更深的网络和复杂模型,从而限制其机载计算;在这项工作中,我们提出了一项战略,旨在将推断请求传播给资源紧张的无人驾驶飞行器群,这些无人驾驶飞行器将捕获的图像分类在机上,并找到最起码的决策时间。我们把该模型设计成一个优化问题,最大限度地减少获取图像和作出最后决定之间的距离。所拟订的优化解决方案是一个难以解决的难题。因此,我们引入了一种在线超常化解决方案,即不易理解,以找到在现有的无人驾驶航空飞行器中保持最佳耐用度的多层定位战略。拟议的方法已作为一般的、基于不同决策的甚低层,即基于甚高频层的甚高分辨率,作为不同决策的基础。