  Small-size unmanned aerial vehicles (UAV) have the potential to dramatically
increase safety and reduce cost in applications like critical infrastructure
maintenance and post-disaster search and rescue. Many scenarios require UAVs to
shrink toward nano and pico-size form factors. The key open challenge to
achieve true autonomy on Nano-UAVs is to run complex visual tasks like object
detection, tracking, navigation and obstacle avoidance fully on board, at high
speed and robustness, under tight payload and power constraints. With the
Kraken SoC, fabricated in 22nm FDX technology, we demonstrate a
multi-visual-sensor capability exploiting both event-based and BW/RGB imagers,
combining their output for multi-functional visual tasks previously impossible
on a single low-power chip for Nano-UAVs. Kraken is an ultra-low-power,
heterogeneous SoC architecture integrating three acceleration engines and a
vast set of peripherals to enable efficient interfacing with standard
frame-based sensors and novel event-based DVS. Kraken enables highly sparse
event-driven sub-uJ/inf SNN inference on a dedicated neuromorphic
energy-proportional accelerator. Moreover, it can perform frame-based inference
by combining a 1.8TOp\s\W 8-cores RISC-V processor cluster with mixed-precision
DNN extensions with a 1036TOp\s\W} TNN accelerator.
