Optimized Deployment of Deep Neural Networks for Visual Pose Estimation
  on Nano-drones
Matteo Risso Francesco Daghero Beatrice Alessandra Motetti Daniele Jahier Pagliari Enrico Macii Massimo Poncino Alessio Burrello
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.