  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).
