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DTSTAMP:20210402T160546Z
LOCATION:Track 10
DTSTART;TZID=America/New_York:20201113T164000
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UID:submissions.supercomputing.org_SC20_sess232_ws_urgent104@linklings.com
SUMMARY:Benchmarking Micro-Core Architectures for Detecting Disasters at t
 he Edge
DESCRIPTION:Workshop\n\nBenchmarking Micro-Core Architectures for Detectin
 g Disasters at the Edge\n\nJamieson, Brown\n\nLeveraging real-time data to
  detect disasters such as wildfires, extreme weather, earthquakes, tsunami
 s, human health emergencies or global diseases is an important opportunity
 . Much of this data, however, is generated in the field and the volumes in
 volved mean that it is impractical for transmission back to a central data
 -center for processing. Instead, edge devices are required to generate ins
 ights from sensor data streaming in, but an important question, given the 
 severe performance and power constraints under which these must operate, i
 s that of the most suitable CPU architecture. One class of device that we 
 believe has a significant role to play is that of micro-cores, which combi
 ne many simple low-power cores in a single chip. There are many from which
  to choose, however, and an important question is which device is most sui
 ted to which situation.\n\nThis paper presents the Eithne framework, desig
 ned to simplify benchmarking of micro-core architectures. Three benchmarks
 , LINPACK, DFT and FFT, have been implemented atop this framework. 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 detectio
 n. The result of this work is an extensible framework that the community c
 an use to help develop and test these devices in the future.\n\nTag: Appli
 cations, Scientific Computing, Simulation, State of the Practice, Technolo
 gy Challenge\n\nRegistration Category: Workshop Reg Pass
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