publication . Article . Other literature type . 2018

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

Sacha J. van Albada; Andrew G. Rowley; Johanna Senk; Michael Hopkins; Maximilian Schmidt; Maximilian Schmidt; Alan B. Stokes; David R. Lester; Markus Diesmann; Markus Diesmann; ...
Open Access English
  • Published: 01 May 2018 Journal: Frontiers in Neuroscience, volume 12 (issn: 1662-4548, eissn: 1662-453X, Copyright policy)
  • Publisher: Frontiers Media S.A.
Abstract
The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within...
Subjects
free text keywords: Neuroscience, Original Research, neuromorphic computing, high-performance computing, parallel computing, accuracy of simulation, energy to solution, benchmarking, strong scaling, computational neuroscience, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, 610, Psychology, Neuromorphic engineering, Artificial neural network, Software, business.industry, business, Application domain, Energy consumption, Supercomputer, Thread (computing), ddc:610
Funded by
RCUK| Biologically-Inspired Massively Parallel Architectures - computing beyond a million processors
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/G015740/1
  • Funding stream: EPSRC
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| BIMPC
Project
BIMPC
Biologically-Inspired Massively-Parallel Computation
  • Funder: European Commission (EC)
  • Project Code: 320689
  • Funding stream: FP7 | SP2 | ERC
,
EC| HBP
Project
HBP
The Human Brain Project
  • Funder: European Commission (EC)
  • Project Code: 604102
  • Funding stream: FP7 | SP1 | ICT
,
RCUK| A scalable chip multiprocessor for large-scale neural simulation
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/D07908X/1
  • Funding stream: EPSRC
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Flagships: FET Flagships
FET FP7FET Flagships: The Human Brain Project
FET H2020FET FLAG: HBP FET Flagship core project
57 references, page 1 of 4

Akopyan F.Sawada J.Cassidy A.Alvarez-Icaza R.Arthur J.Merolla P. (2015). TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 34, 1537–1557. 10.1109/TCAD.2015.2474396 [OpenAIRE] [DOI]

Attwell D.Laughlin S. B. (2001). An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145. 10.1097/00004647-200110000-00001 11598490 [OpenAIRE] [PubMed] [DOI]

Benna M. K.Fusi S. (2016). Computational principles of synaptic memory consolidation. Nat. Neurosci. 19, 1697–1706. 10.1038/nn.4401 27694992 [OpenAIRE] [PubMed] [DOI]

Brecht M.Roth A.Sakmann B. (2003). Dynamic receptive fields of reconstructed pyramidal cells in layers 3 and 2 of rat somatosensory barrel cortex. J. Physiol. 553, 243–265. 10.1113/jphysiol.2003.044222 12949232 [OpenAIRE] [PubMed] [DOI]

Cain N.Iyer R.Koch C.Mihalas S. (2016). The computational properties of a simplified cortical column model. PLoS Comput. Biol. 12:e1005045. 10.1371/journal.pcbi.1005045 27617444 [OpenAIRE] [PubMed] [DOI]

Crook S.Bednar J.Berger S.Cannon R.Davison A.Djurfeldt M.. (2012). Creating, documenting and sharing network models. Network 23, 131–149. 10.3109/0954898X.2012.722743 22994683 [OpenAIRE] [PubMed] [DOI]

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

Eppler J. M.Pauli R.Peyser A.Ippen T.Morrison A.Senk J. (2015). NEST 2.8.0. 10.5281/zenodo.32969 [OpenAIRE] [DOI]

Forschungszentrum Jülich (2018). Ready for Exascale: Researchers Find Algorithm for Large-Scale Brain Simulations on Next-Generation Supercomputers. Jülich: Press release.

Freedman D.Diaconis P. (1981). On the histogram as a density estimator: L 2 theory. Zeitschrift Wahrscheinlichkeitstheorie verwandte Gebiete 57, 453–476. 10.1007/BF01025868 [OpenAIRE] [DOI]

Furber S. B.Lester D. R.Plana L. A.Garside J. D.Painkras E.Temple S. (2013). Overview of the SpiNNaker system architecture. IEEE Trans. Comput. 62, 2454–2467. 10.1109/TC.2012.142 [OpenAIRE] [DOI]

Gewaltig M.-O.Diesmann M. (2007). NEST (NEural Simulation Tool). Scholarpedia 2:1430. [OpenAIRE]

Grün S.Rotter S. (2010). Analysis of Parallel Spike Trains. Berlin: Springer.

Hagen E.Dahmen D.Stavrinou M. L.Lindén H.Tetzlaff T.van Albada S. J.. (2016). Hybrid scheme for modeling local field potentials from point-neuron networks. Cereb. Cortex 26, 4461–4496. 10.1093/cercor/bhw237 27797828 [OpenAIRE] [PubMed] [DOI]

Hanuschkin A.Kunkel S.Helias M.Morrison A.Diesmann M. (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Front. Neuroinformatics 4:113. 10.3389/fninf.2010.00113 21031031 [OpenAIRE] [PubMed] [DOI]

57 references, page 1 of 4
Abstract
The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within...
Subjects
free text keywords: Neuroscience, Original Research, neuromorphic computing, high-performance computing, parallel computing, accuracy of simulation, energy to solution, benchmarking, strong scaling, computational neuroscience, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, 610, Psychology, Neuromorphic engineering, Artificial neural network, Software, business.industry, business, Application domain, Energy consumption, Supercomputer, Thread (computing), ddc:610
Funded by
RCUK| Biologically-Inspired Massively Parallel Architectures - computing beyond a million processors
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/G015740/1
  • Funding stream: EPSRC
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| BIMPC
Project
BIMPC
Biologically-Inspired Massively-Parallel Computation
  • Funder: European Commission (EC)
  • Project Code: 320689
  • Funding stream: FP7 | SP2 | ERC
,
EC| HBP
Project
HBP
The Human Brain Project
  • Funder: European Commission (EC)
  • Project Code: 604102
  • Funding stream: FP7 | SP1 | ICT
,
RCUK| A scalable chip multiprocessor for large-scale neural simulation
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/D07908X/1
  • Funding stream: EPSRC
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Flagships: FET Flagships
FET FP7FET Flagships: The Human Brain Project
FET H2020FET FLAG: HBP FET Flagship core project
57 references, page 1 of 4

Akopyan F.Sawada J.Cassidy A.Alvarez-Icaza R.Arthur J.Merolla P. (2015). TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 34, 1537–1557. 10.1109/TCAD.2015.2474396 [OpenAIRE] [DOI]

Attwell D.Laughlin S. B. (2001). An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145. 10.1097/00004647-200110000-00001 11598490 [OpenAIRE] [PubMed] [DOI]

Benna M. K.Fusi S. (2016). Computational principles of synaptic memory consolidation. Nat. Neurosci. 19, 1697–1706. 10.1038/nn.4401 27694992 [OpenAIRE] [PubMed] [DOI]

Brecht M.Roth A.Sakmann B. (2003). Dynamic receptive fields of reconstructed pyramidal cells in layers 3 and 2 of rat somatosensory barrel cortex. J. Physiol. 553, 243–265. 10.1113/jphysiol.2003.044222 12949232 [OpenAIRE] [PubMed] [DOI]

Cain N.Iyer R.Koch C.Mihalas S. (2016). The computational properties of a simplified cortical column model. PLoS Comput. Biol. 12:e1005045. 10.1371/journal.pcbi.1005045 27617444 [OpenAIRE] [PubMed] [DOI]

Crook S.Bednar J.Berger S.Cannon R.Davison A.Djurfeldt M.. (2012). Creating, documenting and sharing network models. Network 23, 131–149. 10.3109/0954898X.2012.722743 22994683 [OpenAIRE] [PubMed] [DOI]

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

Eppler J. M.Pauli R.Peyser A.Ippen T.Morrison A.Senk J. (2015). NEST 2.8.0. 10.5281/zenodo.32969 [OpenAIRE] [DOI]

Forschungszentrum Jülich (2018). Ready for Exascale: Researchers Find Algorithm for Large-Scale Brain Simulations on Next-Generation Supercomputers. Jülich: Press release.

Freedman D.Diaconis P. (1981). On the histogram as a density estimator: L 2 theory. Zeitschrift Wahrscheinlichkeitstheorie verwandte Gebiete 57, 453–476. 10.1007/BF01025868 [OpenAIRE] [DOI]

Furber S. B.Lester D. R.Plana L. A.Garside J. D.Painkras E.Temple S. (2013). Overview of the SpiNNaker system architecture. IEEE Trans. Comput. 62, 2454–2467. 10.1109/TC.2012.142 [OpenAIRE] [DOI]

Gewaltig M.-O.Diesmann M. (2007). NEST (NEural Simulation Tool). Scholarpedia 2:1430. [OpenAIRE]

Grün S.Rotter S. (2010). Analysis of Parallel Spike Trains. Berlin: Springer.

Hagen E.Dahmen D.Stavrinou M. L.Lindén H.Tetzlaff T.van Albada S. J.. (2016). Hybrid scheme for modeling local field potentials from point-neuron networks. Cereb. Cortex 26, 4461–4496. 10.1093/cercor/bhw237 27797828 [OpenAIRE] [PubMed] [DOI]

Hanuschkin A.Kunkel S.Helias M.Morrison A.Diesmann M. (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Front. Neuroinformatics 4:113. 10.3389/fninf.2010.00113 21031031 [OpenAIRE] [PubMed] [DOI]

57 references, page 1 of 4
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publication . Article . Other literature type . 2018

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

Sacha J. van Albada; Andrew G. Rowley; Johanna Senk; Michael Hopkins; Maximilian Schmidt; Maximilian Schmidt; Alan B. Stokes; David R. Lester; Markus Diesmann; Markus Diesmann; ...