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End-to-end event-based computation has the potential to push the envelope inlatency 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 majorstreamlining opportunities. This paper presents ColibriES, the first-everneuromorphic hardware embedded system platform with dedicated event-sensorinterfaces and full processing pipelines. ColibriES includes event and frameinterfaces and data processing, aiming at efficient and long-life embeddedsystems in edge scenarios. ColibriES is based on the Kraken system-on-chip andcontains a heterogeneous parallel ultra-low power (PULP) processor, frame-basedand event-based camera interfaces, and two hardware accelerators for thecomputation of both event-based spiking neural networks and frame-based ternaryconvolutional neural networks. This paper explores and accurately evaluates theperformance of event data processing on the example of gesture recognition onColibriES, as the first step of full-system evaluation. In our experiments, wedemonstrate a chip energy consumption of 7.7 \si{\milli\joule} and latency of164.5 \si{\milli\second} of each inference with the DVS Gesture event data setas an example for closed-loop data processing, showcasing the potential ofColibriES for battery-powered applications such as wearable devices and UAVsthat require low-latency closed-loop control.

ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging  Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency  Closed-loop Control Applications