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Brian2GeNN: accelerating spiking neural network simulations with graphics hardware

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Title: Brian2GeNN: accelerating spiking neural network simulations with graphics hardware
Authors: Stimberg, M
Goodman, D
Nowotny, T
Item Type: Journal Article
Abstract: “Brian” is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNNis a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance gradegraphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems sothat users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technicalknowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brianscripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators.From the user’s perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown thatusing Brian2GeNN, two non-trivial models from the literature can run tens to hundreds of times faster than on CPU.
Issue Date: 15-Jan-2020
Date of Acceptance: 21-Nov-2019
URI: http://hdl.handle.net/10044/1/75380
DOI: 10.1038/s41598-019-54957-7
ISSN: 2045-2322
Publisher: Nature Publishing Group
Start Page: 1
End Page: 12
Journal / Book Title: Scientific Reports
Volume: 10
Copyright Statement: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: Royal Society
The Royal Society
Funder's Grant Number: RG170298
EESD_P70353
Publication Status: Published
Article Number: 410
Online Publication Date: 2020-01-15
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering