In this section
@article{Cheung:2016:10.3389/fnins.2015.00516,
author = {Cheung, K and Schultz, SR and Luk, W},
doi = {10.3389/fnins.2015.00516},
journal = {Frontiers in Neuroscience},
title = {NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors},
url = {http://dx.doi.org/10.3389/fnins.2015.00516},
volume = {9},
year = {2016}
}
TY - JOUR
AB - NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
AU - Cheung,K
AU - Schultz,SR
AU - Luk,W
DO - 10.3389/fnins.2015.00516
PY - 2016///
SN - 1662-4548
TI - NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
T2 - Frontiers in Neuroscience
UR - http://dx.doi.org/10.3389/fnins.2015.00516
UR - http://hdl.handle.net/10044/1/28395
VL - 9
ER -