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Leveraging real-time data to detect disasters such as wildfires, extreme weather, earthquakes, tsunamis, human health emergencies, or global diseases is an important opportunity. However, much of this data is generated in the field and the volumes involved mean that it is impractical for transmission back to a central data-centre for processing. Instead, edge devices are required to generate insights from sensor data streaming in, but an important question given the severe performance and power constraints that these must operate under is that of the most suitable CPU architecture. One class of device that we believe has a significant role to play here is that of micro-cores, which combine many simple low-power cores in a single chip. However, there are many to choose from, and an important question is which is most suited to what situation. This paper presents the Eithne framework, designed to simplify benchmarking of micro-core architectures. Three benchmarks, LINPACK, DFT and FFT, have been implemented atop of this framework and we use these to explore the key characteristics and concerns of common micro-core designs within the context of operating on the edge for disaster detection. The result of this work is an extensible framework that the community can use help develop and test these devices in the future.

Benchmarking micro-core architectures for detecting disasters at the edge