Exploring Applications of Random Walks on Spiking Neural Algorithms
- Colorado School of Mines, Golden, CO (United States)
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Neuromorphic computing has many promises in the future of computing due to its energy efficient and scalable implementation. Here we extend a neural algorithm that is able to solve the diffusion equation PDE by implementing random walks on neuromorphic hardware. Additionally, we introduce four random walk applications that use this spiking neural algorithm. The four applications currently implemented are: generating a random walk to replicate an image, finding a path between two nodes, finding triangles in a graph, and partitioning a graph into two sections. We then made these four applications available to be implemented on software using a graphical user interface (GUI).
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1471656
- Report Number(s):
- SAND-2018-10250R; 667985
- Country of Publication:
- United States
- Language:
- English
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