Abstract
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.
Original language | English |
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Title of host publication | ICONS 2021 - Proceedings of International Conference on Neuromorphic Systems 2021 |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450386913 |
DOIs | |
State | Published - Jul 27 2021 |
Event | 2021 International Conference on Neuromorphic Systems, ICONS 2021 - Virtual, Onlie, United States Duration: Jul 27 2021 → Jul 29 2021 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 2021 International Conference on Neuromorphic Systems, ICONS 2021 |
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Country/Territory | United States |
City | Virtual, Onlie |
Period | 07/27/21 → 07/29/21 |
Funding
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under contract number DE-AC05-00OR22725.
Keywords
- Graph algorithms
- Neuromorphic computing