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HRLSim: a high performance spiking neural network simulator for GPGPU clusters.

Kirill Minkovich, Corey M Thibeault, Michael John O'Brien

IEEE Transactions on Neural Networks and Learning Systems

|May 9, 2014

Summary

This study introduces HRL Spiking Simulator (HRLSim), a scalable tool for modeling large-scale spiking neural networks. HRLSim leverages general purpose graphical processing units (GPGPUs) for efficient brain function research and applications.

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Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Understanding brain function relies on modeling large-scale spiking neural networks.
Developing efficient simulation tools is crucial for advancing neuroscience and AI applications.

Purpose of the Study:

To introduce the HRL Spiking Simulator (HRLSim), a novel environment for large-scale spiking neural network simulation.
To detail the simulator's suitability for implementation on general purpose graphical processing unit (GPGPU) clusters.
To analyze the performance of HRLSim across various cluster configurations.

Main Methods:

Description of the HRLSpiking Simulator (HRLSim) architecture.
Implementation of HRLSim on a cluster of general purpose graphical processing units (GPGPUs).
Performance analysis of HRLSim for different cluster configurations.

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Main Results:

HRLSim is suitable for implementation on GPGPU clusters.
Performance analysis demonstrates scalability and efficiency.
Novel aspects of the simulator are presented.

Conclusions:

HRLSim provides an affordable and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.
The use of inexpensive GPGPU cards makes HRLSim a powerful resource for neuroscience research and AI development.