\textlessp\textgreaterA novel GPU-based simulation of
spiking neural networks is implemented as a hybrid system
using Parker-Sochacki numerical integration method with
adaptive order. Full single-precision floating-point accuracy
for all model variables is achieved. The implementation is
validated with exact matching of all neuron potential traces
from GPU-based simulation versus those of a reference
CPU-based simulation. A network of 4096 Izhikevich neurons
simulated on an NVIDIA GTX260 device achieves real-time
performance with a speedup of 9 compared to simulation
executed on Opteron 285, 2.6-GHz
device.\textless/p\textgreater
%0 Conference Paper
%1 yudanov_gpu-based_2010
%A Yudanov, D.
%A Shaaban, M.
%A Melton, R.
%A Reznik, L.
%B Neural Networks (IJCNN), The 2010 International Joint Conference on
%D 2010
%K gpu simulation
%P 1--8
%R 10.1109/IJCNN.2010.5596334
%T GPU-based simulation of spiking neural networks with real-time performance & high accuracy
%X \textlessp\textgreaterA novel GPU-based simulation of
spiking neural networks is implemented as a hybrid system
using Parker-Sochacki numerical integration method with
adaptive order. Full single-precision floating-point accuracy
for all model variables is achieved. The implementation is
validated with exact matching of all neuron potential traces
from GPU-based simulation versus those of a reference
CPU-based simulation. A network of 4096 Izhikevich neurons
simulated on an NVIDIA GTX260 device achieves real-time
performance with a speedup of 9 compared to simulation
executed on Opteron 285, 2.6-GHz
device.\textless/p\textgreater
%@ 1098-7576
@inproceedings{yudanov_gpu-based_2010,
abstract = {{\textless}p{\textgreater}A novel {GPU-based} simulation of
spiking neural networks is implemented as a hybrid system
using Parker-Sochacki numerical integration method with
adaptive order. Full single-precision floating-point accuracy
for all model variables is achieved. The implementation is
validated with exact matching of all neuron potential traces
from {GPU-based} simulation versus those of a reference
{CPU-based} simulation. A network of 4096 Izhikevich neurons
simulated on an {NVIDIA} {GTX260} device achieves real-time
performance with a speedup of 9 compared to simulation
executed on Opteron 285, 2.6-{GHz}
device.{\textless}/p{\textgreater}},
added-at = {2014-01-19T15:00:24.000+0100},
author = {Yudanov, D. and Shaaban, M. and Melton, R. and Reznik, L.},
bdsk-url-1 = {http://dx.doi.org/10.1109/IJCNN.2010.5596334},
biburl = {https://www.bibsonomy.org/bibtex/2171604accfbe7e41792012385283b06a/neurokernel},
booktitle = {Neural Networks ({IJCNN)}, The 2010 International Joint Conference on},
doi = {10.1109/IJCNN.2010.5596334},
interhash = {5524f3bfbb8dcef3bdd05575566922e0},
intrahash = {171604accfbe7e41792012385283b06a},
isbn = {1098-7576},
keywords = {gpu simulation},
pages = {1--8},
timestamp = {2014-01-19T15:00:24.000+0100},
title = {{GPU-based} simulation of spiking neural networks with real-time performance \& high accuracy},
year = 2010
}