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Benchmarking neuromorphic systems with Nengo

Nengo is a software package for designing and simulating large-scale neural models. Nengo is architected such that the same Nengo model can be simulated on any of several Nengo backends with few to no modifications. Backends translate a model to specific platforms, which include GPUs and neuromorphi...

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Detalles Bibliográficos
Autores principales: Bekolay, Trevor, Stewart, Terrence C., Eliasmith, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609756/
https://www.ncbi.nlm.nih.gov/pubmed/26539076
http://dx.doi.org/10.3389/fnins.2015.00380
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author Bekolay, Trevor
Stewart, Terrence C.
Eliasmith, Chris
author_facet Bekolay, Trevor
Stewart, Terrence C.
Eliasmith, Chris
author_sort Bekolay, Trevor
collection PubMed
description Nengo is a software package for designing and simulating large-scale neural models. Nengo is architected such that the same Nengo model can be simulated on any of several Nengo backends with few to no modifications. Backends translate a model to specific platforms, which include GPUs and neuromorphic hardware. Nengo also contains a large test suite that can be run with any backend and focuses primarily on functional performance. We propose that Nengo's large test suite can be used to benchmark neuromorphic hardware's functional performance and simulation speed in an efficient, unbiased, and future-proof manner. We implement four benchmark models and show that Nengo can collect metrics across five different backends that identify situations in which some backends perform more accurately or quickly.
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spelling pubmed-46097562015-11-04 Benchmarking neuromorphic systems with Nengo Bekolay, Trevor Stewart, Terrence C. Eliasmith, Chris Front Neurosci Neuroscience Nengo is a software package for designing and simulating large-scale neural models. Nengo is architected such that the same Nengo model can be simulated on any of several Nengo backends with few to no modifications. Backends translate a model to specific platforms, which include GPUs and neuromorphic hardware. Nengo also contains a large test suite that can be run with any backend and focuses primarily on functional performance. We propose that Nengo's large test suite can be used to benchmark neuromorphic hardware's functional performance and simulation speed in an efficient, unbiased, and future-proof manner. We implement four benchmark models and show that Nengo can collect metrics across five different backends that identify situations in which some backends perform more accurately or quickly. Frontiers Media S.A. 2015-10-19 /pmc/articles/PMC4609756/ /pubmed/26539076 http://dx.doi.org/10.3389/fnins.2015.00380 Text en Copyright © 2015 Bekolay, Stewart and Eliasmith. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Bekolay, Trevor
Stewart, Terrence C.
Eliasmith, Chris
Benchmarking neuromorphic systems with Nengo
title Benchmarking neuromorphic systems with Nengo
title_full Benchmarking neuromorphic systems with Nengo
title_fullStr Benchmarking neuromorphic systems with Nengo
title_full_unstemmed Benchmarking neuromorphic systems with Nengo
title_short Benchmarking neuromorphic systems with Nengo
title_sort benchmarking neuromorphic systems with nengo
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609756/
https://www.ncbi.nlm.nih.gov/pubmed/26539076
http://dx.doi.org/10.3389/fnins.2015.00380
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