publication . Article . 2015

Benchmarking neuromorphic systems with Nengo.

Trevor eBekolay; Terrence C Stewart; Chris eEliasmith;
Open Access English
  • Published: 01 Oct 2015 Journal: Frontiers in Neuroscience, volume 9 (issn: 1662-4548, eissn: 1662-453X, Copyright policy)
  • Publisher: Frontiers Media S.A.
Abstract
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 Ne...
Subjects
free text keywords: Neuroscience, Original Research, Nengo, benchmarking, neuromorphic hardware, large-scale neural networks, spiking neural networks, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
23 references, page 1 of 2

Bednar J. A. (2009). Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Front. Neuroinform. 3:8. 10.3389/neuro.11.008.2009 19352443 [OpenAIRE] [PubMed] [DOI]

Bekolay T.Bergstra J.Hunsberger E.DeWolf T.Stewart T. C.Rasmussen D.. (2013). Nengo: a Python tool for building large-scale functional brain models. Front. Neuroinform.7:48. 10.3389/fninf.2013.00048 24431999 [OpenAIRE] [PubMed] [DOI]

Benjamin B. V.Gao P.McQuinn E.Choudhary S.Chandrasekaran A. R.Bussat J.-M. (2014). Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716. 10.1109/JPROC.2014.2313565 [DOI]

Brüderle D.Petrovici M. A.Vogginger B.Ehrlich M.Pfeil T.Millner S.. (2011). A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol. Cybern.104, 263–296. 10.1007/s00422-011-0435-9 21618053 [OpenAIRE] [PubMed] [DOI]

Choudhary S.Sloan S.Fok S.Neckar A.Trautmann E.Gao P. (2012). Silicon neurons that compute, in Proceedings of the 2012 International Conference on Artificial Neural Networks and Machine Learning (Lausanne: Springer), 121–128.

Davison A. P.Brüderle D.Eppler J.Kremkow J.Muller E.Pecevski D.. (2008). PyNN: a common interface for neuronal network simulators. Front. Neuroinform.2:11. 10.3389/neuro.11.011.2008 19194529 [OpenAIRE] [PubMed] [DOI]

Duysens J.Van de Crommert H. W. A. A. (1998). Neural control of locomotion; Part 1: the central pattern generator from cats to humans. Gait Posture 7, 131–141. 10.1016/S0966-6362(97)00042-8 10200383 [OpenAIRE] [PubMed] [DOI]

Ehr lich M.Wendt K.Zühl L.Schüffny R.Brüderle D.Müller E. (2010). A software framework for mapping neural networks to a wafer-scale neuromorphic hardware system, in Proceedings of the 2010 Conference on Artificial Neural Networks and Intelligent Information Processing (Funchal), 43–52.

Eliasmith C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. New York, NY: Oxford University Press.

Eliasmith C.Anderson C. H. (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. Cambridge, MA: MIT Press.

Eliasmith C.Stewart T. C.Choo X.Bekolay T.DeWolf T.Tang Y.. (2012). A large-scale model of the functioning brain. Science 338, 1202–1205. 10.1126/science.1225266 23197532 [OpenAIRE] [PubMed] [DOI]

Furber S. B.Galluppi F.Temple S.Plana L. A. (2014). The SpiNNaker project. Proc. IEEE 102, 652–665. 10.1109/JPROC.2014.2304638 [OpenAIRE] [DOI]

Galluppi F.Davies S.Furber S.Stewart T.Eliasmith C. (2012). Real time on-chip implementation of dynamical systems with spiking neurons, in Proceedings of the 2012 International Joint Conference on Neural Networks (Brisbane, QLD: IEEE), 1–8.

Goodman D.Brette R. (2008). Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. 2:5. 10.3389/neuro.11.005.2008 19115011 [OpenAIRE] [PubMed] [DOI]

Hilbert D. (1891). Ueber die stetige abbildung einer line auf ein flächenstück. Math. Ann. 38, 459–460. 10.1007/BF01199431 [DOI]

23 references, page 1 of 2
Abstract
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 Ne...
Subjects
free text keywords: Neuroscience, Original Research, Nengo, benchmarking, neuromorphic hardware, large-scale neural networks, spiking neural networks, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
23 references, page 1 of 2

Bednar J. A. (2009). Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Front. Neuroinform. 3:8. 10.3389/neuro.11.008.2009 19352443 [OpenAIRE] [PubMed] [DOI]

Bekolay T.Bergstra J.Hunsberger E.DeWolf T.Stewart T. C.Rasmussen D.. (2013). Nengo: a Python tool for building large-scale functional brain models. Front. Neuroinform.7:48. 10.3389/fninf.2013.00048 24431999 [OpenAIRE] [PubMed] [DOI]

Benjamin B. V.Gao P.McQuinn E.Choudhary S.Chandrasekaran A. R.Bussat J.-M. (2014). Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716. 10.1109/JPROC.2014.2313565 [DOI]

Brüderle D.Petrovici M. A.Vogginger B.Ehrlich M.Pfeil T.Millner S.. (2011). A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol. Cybern.104, 263–296. 10.1007/s00422-011-0435-9 21618053 [OpenAIRE] [PubMed] [DOI]

Choudhary S.Sloan S.Fok S.Neckar A.Trautmann E.Gao P. (2012). Silicon neurons that compute, in Proceedings of the 2012 International Conference on Artificial Neural Networks and Machine Learning (Lausanne: Springer), 121–128.

Davison A. P.Brüderle D.Eppler J.Kremkow J.Muller E.Pecevski D.. (2008). PyNN: a common interface for neuronal network simulators. Front. Neuroinform.2:11. 10.3389/neuro.11.011.2008 19194529 [OpenAIRE] [PubMed] [DOI]

Duysens J.Van de Crommert H. W. A. A. (1998). Neural control of locomotion; Part 1: the central pattern generator from cats to humans. Gait Posture 7, 131–141. 10.1016/S0966-6362(97)00042-8 10200383 [OpenAIRE] [PubMed] [DOI]

Ehr lich M.Wendt K.Zühl L.Schüffny R.Brüderle D.Müller E. (2010). A software framework for mapping neural networks to a wafer-scale neuromorphic hardware system, in Proceedings of the 2010 Conference on Artificial Neural Networks and Intelligent Information Processing (Funchal), 43–52.

Eliasmith C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. New York, NY: Oxford University Press.

Eliasmith C.Anderson C. H. (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. Cambridge, MA: MIT Press.

Eliasmith C.Stewart T. C.Choo X.Bekolay T.DeWolf T.Tang Y.. (2012). A large-scale model of the functioning brain. Science 338, 1202–1205. 10.1126/science.1225266 23197532 [OpenAIRE] [PubMed] [DOI]

Furber S. B.Galluppi F.Temple S.Plana L. A. (2014). The SpiNNaker project. Proc. IEEE 102, 652–665. 10.1109/JPROC.2014.2304638 [OpenAIRE] [DOI]

Galluppi F.Davies S.Furber S.Stewart T.Eliasmith C. (2012). Real time on-chip implementation of dynamical systems with spiking neurons, in Proceedings of the 2012 International Joint Conference on Neural Networks (Brisbane, QLD: IEEE), 1–8.

Goodman D.Brette R. (2008). Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. 2:5. 10.3389/neuro.11.005.2008 19115011 [OpenAIRE] [PubMed] [DOI]

Hilbert D. (1891). Ueber die stetige abbildung einer line auf ein flächenstück. Math. Ann. 38, 459–460. 10.1007/BF01199431 [DOI]

23 references, page 1 of 2
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