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The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles(UAV) is raising, especially due to the microsecond-level reaction time of thebio-inspired event sensor, which increases robustness and reduces latency ofthe perception tasks compared to a RGB camera. This work presents ColibriUAV, aUAV platform with both frame-based and event-based cameras interfaces forefficient perception and near-sensor processing. The proposed platform isdesigned around Kraken, a novel low-power RISC-V System on Chip with twohardware accelerators targeting spiking neural networks and deep ternary neuralnetworks.Kraken is capable of efficiently processing both event data from a DVScamera and frame data from an RGB camera. A key feature of Kraken is itsintegrated, dedicated interface with a DVS camera. This paper benchmarks theend-to-end latency and power efficiency of the neuromorphic and event-based UAVsubsystem, demonstrating state-of-the-art event data with a throughput of 7200frames 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 thanthe widely-used data reading approach through the USB interface. The overallsensing and processing power consumption is below 50 mW, achieving latency inthe milliseconds range, making the platform suitable for low-latency autonomousnano-drones as well.

ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras