  Edge computing solutions that enable the extraction of high-level information
from a variety of sensors is in increasingly high demand. This is due to the
increasing number of smart devices that require sensory processing for their
application on the edge. To tackle this problem, we present a smart vision
sensor System on Chip (SoC), featuring an event-based camera and a low-power
asynchronous spiking Convolutional Neural Network (sCNN) computing architecture
embedded on a single chip. By combining both sensor and processing on a single
die, we can lower unit production costs significantly. Moreover, the simple
end-to-end nature of the SoC facilitates small stand-alone applications as well
as functioning as an edge node in larger systems. The event-driven nature of
the vision sensor delivers high-speed signals in a sparse data stream. This is
reflected in the processing pipeline, which focuses on optimising highly sparse
computation and minimising latency for 9 sCNN layers to 3.36{\mu}s for an
incoming event. Overall, this results in an extremely low-latency visual
processing pipeline deployed on a small form factor with a low energy budget
and sensor cost. We present the asynchronous architecture, the individual
blocks, and the sCNN processing principle and benchmark against other sCNN
capable processors.
