Title :
HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
Author :
Minkovich, Kirill ; Thibeault, Corey M. ; O´Brien, Michael J. ; Nogin, Aleksey ; Youngkwan Cho ; Srinivasa, Narayan
Author_Institution :
Inf. & Syst. Sci. Dept., HRL Labs. LLC, Malibu, CA, USA
Abstract :
Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.
Keywords :
graphics processing units; neural nets; GPGPU cards; GPGPU clusters; HRL spiking simulator; HRLSim; brain function; general purpose graphical processing units; high performance spiking neural network simulator; large scale spiking neural models; real time simulation; Analytical models; Biological system modeling; Computational modeling; Delays; Graphics processing units; Mathematical model; Neurons; Distributed graphical processing units (GPUs) programming; general purpose GPU (GPGPU); large scale; neuron simulation; spike timing-dependent plasticity (STDP); spiking neural simulation;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2276056
