  End-to-end event-based computation has the potential to push the envelope in
latency and energy efficiency for edge AI applications. Unfortunately,
event-based sensors (e.g., DVS cameras) and neuromorphic spike-based processors
(e.g., Loihi) have been designed in a decoupled fashion, thereby missing major
streamlining opportunities. This paper presents ColibriES, the first-ever
neuromorphic hardware embedded system platform with dedicated event-sensor
interfaces and full processing pipelines. ColibriES includes event and frame
interfaces and data processing, aiming at efficient and long-life embedded
systems in edge scenarios. ColibriES is based on the Kraken system-on-chip and
contains a heterogeneous parallel ultra-low power (PULP) processor, frame-based
and event-based camera interfaces, and two hardware accelerators for the
computation of both event-based spiking neural networks and frame-based ternary
convolutional neural networks. This paper explores and accurately evaluates the
performance of event data processing on the example of gesture recognition on
ColibriES, as the first step of full-system evaluation. In our experiments, we
demonstrate a chip energy consumption of 7.7 \si{\milli\joule} and latency of
164.5 \si{\milli\second} of each inference with the DVS Gesture event data set
as an example for closed-loop data processing, showcasing the potential of
ColibriES for battery-powered applications such as wearable devices and UAVs
that require low-latency closed-loop control.
