Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation
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 …
partly because of their potential for very low power computation. SNCs have primarily been …
Spike-based graph centrality measures
We derive several spike-based routines that compute or establish bounds on radial
centrality measures for undirected graphs and trees without the use of matrix multiplication …
centrality measures for undirected graphs and trees without the use of matrix multiplication …
Computer science research needs for parallel discrete event simulation (PDES)
Historically, scientific computing efforts have demonstrated the clear need for, and effective
use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are …
use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are …
Spike-based primitives for graph algorithms
KE Hamilton, TM Mintz, CD Schuman - arXiv preprint arXiv:1903.10574, 2019 - arxiv.org
In this paper we consider graph algorithms and graphical analysis as a new application for
neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking …
neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking …
Towards adaptive spiking label propagation
KE Hamilton, CD Schuman - Proceedings of the International …, 2018 - dl.acm.org
Graph algorithms are a new class of applications for neuromorphic hardware. Rather than
adapting deep learning and standard neural network approaches to a low-precision spiking …
adapting deep learning and standard neural network approaches to a low-precision spiking …