Abstract
Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.
Original language | English |
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Title of host publication | Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450361231 |
DOIs | |
State | Published - Mar 26 2019 |
Event | 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 - Albany, United States Duration: Mar 26 2019 → Mar 28 2019 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 |
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Country/Territory | United States |
City | Albany |
Period | 03/26/19 → 03/28/19 |
Bibliographical note
Publisher Copyright:© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keywords
- Graph algorithms
- Memristors
- Neighborhood
- Neuromorphic computing
- Shortest path