ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing
  UAV-Platform with Event-Based and Frame-Based Cameras
Sizhen Bian Lukas Schulthess Georg Rutishauser Alfio Di Mauro Luca Benini Michele Magno
The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles
(UAV) is raising, especially due to the microsecond-level reaction time of the
bio-inspired event sensor, which increases robustness and reduces latency of
the perception tasks compared to a RGB camera. This work presents ColibriUAV, a
UAV platform with both frame-based and event-based cameras interfaces for
efficient perception and near-sensor processing. The proposed platform is
designed around Kraken, a novel low-power RISC-V System on Chip with two
hardware accelerators targeting spiking neural networks and deep ternary neural
networks.Kraken is capable of efficiently processing both event data from a DVS
camera and frame data from an RGB camera. A key feature of Kraken is its
integrated, dedicated interface with a DVS camera. This paper benchmarks the
end-to-end latency and power efficiency of the neuromorphic and event-based UAV
subsystem, demonstrating state-of-the-art event data with a throughput of 7200
frames of events per second and a power consumption of 10.7 \si{\milli\watt},
which is over 6.6 times faster and a hundred times less power-consuming than
the widely-used data reading approach through the USB interface. The overall
sensing and processing power consumption is below 50 mW, achieving latency in
the milliseconds range, making the platform suitable for low-latency autonomous
nano-drones as well.