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Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max Flow

Conference ·
 [1];  [1];  [2];  [1];  [1]
  1. ORNL
  2. University of Vermont
+ Show Author Affiliations
Neuromorphic computing is poised to become a promising computing paradigm in the post Moore’s law era due to its extremely low power usage and inherent parallelism. Spiking neural networks are the traditional use case for neuromorphic systems, and have proven to be highly effective at machine learning tasks such as control problems. More recently, neuromorphic systems have been applied outside of the arena of machine learning, primarily in the field of graph algorithms. Neuromorphic systems have been shown to perform graph algorithms faster and with lower power consumption than their traditional (GPU/CPU) counterparts, and are hence an attractive option for a co-processing unit in future high performance computing systems, where graph algorithms play a critical role. In this paper, we present a neuromorphic implementation of cycle detection, odd cycle detection, and the Ford-Fulkerson max-flow algorithm. We further evaluate the performance of these implementations using the NEST neuromorphic simulator by using spike counts and simulation time as proxies for energy consumption and run time. In addition to gains inherent in neuromorphic systems, we show that within the neuromorphic implementations early stopping criteria can be implemented to further improve performance.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1873836
Country of Publication:
United States
Language:
English

References (10)

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conference May 2010
Provable Neuromorphic Advantages for Computing Shortest Paths conference July 2020
Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model journal May 2018
Efficient Classification of Supercomputer Failures Using Neuromorphic Computing conference November 2018
Evolutionary Optimization for Neuromorphic Systems
  • Schuman, Catherine D.; Mitchell, J. Parker; Patton, Robert M.
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conference June 2020
The SpiNNaker Project journal May 2014
NEST (NEural Simulation Tool) journal January 2007
Neuromorphic Graph Algorithms conference March 2020
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning journal January 2018
Modeling epidemic spread with spike-based models conference July 2020

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