Scalable multi-precision simulation of spiking neural networks on GPU with OpenCL

Author

Yudanov, Dmitri ; Reznik, Leon

Author_Institution

Adv. Micro Devices (AMD), Austin, TX, USA

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

Abstract

Biologically-realistic multi-precision spiking neural network (SNN) simulation is designed and implemented on a new GPU device Radeon™ HD 7970 using OpenCL framework. The implementation aims to investigate the role of time precision in simulated SNNs. Simulation methods and GPU platforms are reviewed. Simulation model and process are presented and analyzed. The GPU model is capable of simulating a SNN with up to two million neurons. GPU and CPU results are directly verified and found to match exactly.

Keywords

biocomputing; digital simulation; graphics processing units; neural nets; CPU; GPU device; OpenCL framework; Radeon HD 7970; SNN; biologically-realistic multiprecision spiking neural network simulation; graphical processing units; scalable multiprecision simulation; Biological system modeling; Computational modeling; Graphics processing unit; Mathematical model; Neurons; Numerical models; Synchronization; GPU implementation; OpenCL; high precision; spiking neural network simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252440

Filename

6252440