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DTSTART:19700308T020000
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DTSTAMP:20181221T160726Z
LOCATION:D161
DTSTART;TZID=America/Chicago:20181111T144000
DTEND;TZID=America/Chicago:20181111T150000
UID:submissions.supercomputing.org_SC18_sess156_ws_pmes105@linklings.com
SUMMARY:Non-Neural Network Applications for Spiking Neuromorphic Hardware
DESCRIPTION:Workshop\nArchitectures, Heterogeneous Systems, Quantum Comput
 ing, Workshop Reg Pass\n\nNon-Neural Network Applications for Spiking Neur
 omorphic Hardware\n\nAimone, Hamilton, Mniszewski, Reeder, Schuman...\n\nI
 ncreasing power costs for large-scale computing in a post-Moore’s Law syst
 em have forced the high-performance computing community to explore heterog
 eneous systems.  Neuromorphic architectures, inspired by biological neural
  systems, have so far been relegated to auxiliary machine learning applica
 tions.  Here, we discuss growing research showing the viability of ultra-l
 ow-power neural accelerators as co-processors for classic compute algorith
 ms, such as random walk simulations and graph analytics.
URL:https://sc18.supercomputing.org/presentation/?id=ws_pmes105&sess=sess1
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