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DTSTART:19700308T020000
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LOCATION:D161
DTSTART;TZID=America/Chicago:20181111T153000
DTEND;TZID=America/Chicago:20181111T155000
UID:submissions.supercomputing.org_SC18_sess156_ws_pmes104@linklings.com
SUMMARY:Shortest Path and Neighborhood Subgraph Extraction on a Spiking Me
 mristive Neuromorphic Implementation
DESCRIPTION:Catherine Schuman, Kathleen Hamilton, and Tiffany Mintz (Oak R
 idge National Laboratory); Md Musabbir Adnan (University of Tennessee); Bo
 n Woong Ku and Sung-Kyu Lim (Georgia Institute of Technology); and Garrett
  S. Rose (University of Tennessee)\n\nSpiking neuromorphic computers (SNCs
 ) are promising as a post Moore's law technology because of their potentia
 l for very low power computation.  SNCs have primarily been demonstrated o
 n machine learning applications, but they can also be used for application
 s beyond machine learning.  Here, we demonstrate two graph problems (short
 est path and neighborhood subgraph extraction) that can be solved using SN
 Cs.  We estimate the performance of a memristive SNC  for these applicatio
 ns on three real-world graphs.\n\nTag: Architectures, Heterogeneous System
 s, Quantum Computing\n\nRegistration Category: Workshop Reg Pass\n\nSessio
 n Chair: Jeffrey S. Vetter (Oak Ridge National Laboratory (ORNL))\n\n
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