Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware
Stöckel A, Jenzen C, Thies M, Rückert U (2017)
Frontiers in Computational Neuroscience 11: 71.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
Download
![Open access file OA](https://pub.uni-bielefeld.de/images/access_open.png)
Einrichtung
Abstract / Bemerkung
Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP). Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output.
Erscheinungsjahr
2017
Zeitschriftentitel
Frontiers in Computational Neuroscience
Band
11
Art.-Nr.
71
ISSN
1662-5188
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2913968
Zitieren
Stöckel A, Jenzen C, Thies M, Rückert U. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience. 2017;11: 71.
Stöckel, A., Jenzen, C., Thies, M., & Rückert, U. (2017). Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience, 11, 71. https://doi.org/10.3389/fncom.2017.00071
Stöckel, Andreas, Jenzen, Christoph, Thies, Michael, and Rückert, Ulrich. 2017. “Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware”. Frontiers in Computational Neuroscience 11: 71.
Stöckel, A., Jenzen, C., Thies, M., and Rückert, U. (2017). Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience 11:71.
Stöckel, A., et al., 2017. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience, 11: 71.
A. Stöckel, et al., “Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware”, Frontiers in Computational Neuroscience, vol. 11, 2017, : 71.
Stöckel, A., Jenzen, C., Thies, M., Rückert, U.: Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. Frontiers in Computational Neuroscience. 11, : 71 (2017).
Stöckel, Andreas, Jenzen, Christoph, Thies, Michael, and Rückert, Ulrich. “Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware”. Frontiers in Computational Neuroscience 11 (2017): 71.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
Volltext(e)
Name
fncom-11-00071.stoeckel.pdf
1.13 MB
Access Level
![Open access file OA](https://pub.uni-bielefeld.de/images/access_open.png)
Zuletzt Hochgeladen
2019-09-06T09:18:52Z
MD5 Prüfsumme
01d1e9bee7db43a16fe1acdcd0b3bf53
Daten bereitgestellt von European Bioinformatics Institute (EBI)
Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
54 References
Daten bereitgestellt von Europe PubMed Central.
Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb.
Bhalla US, Bower JM., J. Neurophysiol. 69(6), 1993
PMID: 7688798
Bhalla US, Bower JM., J. Neurophysiol. 69(6), 1993
PMID: 7688798
Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity.
Bill J, Schuch K, Bruderle D, Schemmel J, Maass W, Meier K., Front Comput Neurosci 4(), 2010
PMID: 21031027
Bill J, Schuch K, Bruderle D, Schemmel J, Maass W, Meier K., Front Comput Neurosci 4(), 2010
PMID: 21031027
Wax on, wax off: nest soil facilitates indirect transfer of recognition cues between ant nestmates.
Bos N, Grinsted L, Holman L., PLoS ONE 6(4), 2011
PMID: 21559364
Bos N, Grinsted L, Holman L., PLoS ONE 6(4), 2011
PMID: 21559364
AUTHOR UNKNOWN, 1998
Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.
Brette R, Gerstner W., J. Neurophysiol. 94(5), 2005
PMID: 16014787
Brette R, Gerstner W., J. Neurophysiol. 94(5), 2005
PMID: 16014787
Maximum likelihood analysis of spike trains of interacting nerve cells.
Brillinger DR., Biol Cybern 59(3), 1988
PMID: 3179344
Brillinger DR., Biol Cybern 59(3), 1988
PMID: 3179344
Brüderle D.., 2009
A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.
Bruderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grubl A, Wendt K, Muller E, Schwartz MO, de Oliveira DH, Jeltsch S, Fieres J, Schilling M, Muller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zuhl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schuffny R, Schemmel J, Meier K., Biol Cybern 104(4-5), 2011
PMID: 21618053
Bruderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grubl A, Wendt K, Muller E, Schwartz MO, de Oliveira DH, Jeltsch S, Fieres J, Schilling M, Muller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zuhl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schuffny R, Schemmel J, Meier K., Biol Cybern 104(4-5), 2011
PMID: 21618053
PyNN: A Common Interface for Neuronal Network Simulators.
Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P., Front Neuroinform 2(), 2008
PMID: 19194529
Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P., Front Neuroinform 2(), 2008
PMID: 19194529
Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms.
Diamond A, Nowotny T, Schmuker M., Front Neurosci 9(), 2015
PMID: 26778950
Diamond A, Nowotny T, Schmuker M., Front Neurosci 9(), 2015
PMID: 26778950
A software framework for mapping neural networks to a wafer-scale neuromorphic hardware system
Ehrlich M., Wendt K., Zühl L., Schüffny R., Brüderle D., Müller E.., 2010
Ehrlich M., Wendt K., Zühl L., Schüffny R., Brüderle D., Müller E.., 2010
Achieving High Performance with FPGA-Based Computing.
Herbordt MC, Vancourt T, Gu Y, Sukhwani B, Conti A, Model J, Disabello D., Computer (Long Beach Calif) 40(3), 2007
PMID: 21603088
Herbordt MC, Vancourt T, Gu Y, Sukhwani B, Conti A, Model J, Disabello D., Computer (Long Beach Calif) 40(3), 2007
PMID: 21603088
Realizing biological spiking network models in a configurable wafer-scale hardware system
Fieres J., Schemmel J., Meier K.., 2008
Fieres J., Schemmel J., Meier K.., 2008
Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System.
Friedmann S, Schemmel J, Grubl A, Hartel A, Hock M, Meier K., IEEE Trans Biomed Circuits Syst 11(1), 2016
PMID: 28113678
Friedmann S, Schemmel J, Grubl A, Hartel A, Hock M, Meier K., IEEE Trans Biomed Circuits Syst 11(1), 2016
PMID: 28113678
Overview of the SpiNNaker system architecture
Furber S., Lester D., Plana L., Garside J., Painkras E., Temple S.., 2013
Furber S., Lester D., Plana L., Garside J., Painkras E., Temple S.., 2013
Spike-response model
Gerstner W.., 2008
Gerstner W.., 2008
Gerstner W., Kistler W., Naud R., Paninski L.., 2014
NEST (NEural simulation tool)
Gewaltig M.-O., Diesmann M.., 2007
Gewaltig M.-O., Diesmann M.., 2007
Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex.
Gray CM, McCormick DA., Science 274(5284), 1996
PMID: 8810245
Gray CM, McCormick DA., Science 274(5284), 1996
PMID: 8810245
Finding a roadmap to achieve large neuromorphic hardware systems.
Hasler J, Marr B., Front Neurosci 7(), 2013
PMID: 24058330
Hasler J, Marr B., Front Neurosci 7(), 2013
PMID: 24058330
Hebb D.., 1949
Neural networks and physical systems with emergent collective computational abilities.
Hopfield JJ., Proc. Natl. Acad. Sci. U.S.A. 79(8), 1982
PMID: 6953413
Hopfield JJ., Proc. Natl. Acad. Sci. U.S.A. 79(8), 1982
PMID: 6953413
Jeltsch S.., 2014
Knoblauch A.., 2003
Kohonen T.., 1977
Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations.
Lansner A., Trends Neurosci. 32(3), 2009
PMID: 19187979
Lansner A., Trends Neurosci. 32(3), 2009
PMID: 19187979
A logical calculus of the ideas immanent in nervous activity
McCulloch W., Pitts W.., 1943
McCulloch W., Pitts W.., 1943
An efficient SpiNNaker implementation of the neural engineering framework
Mundy A., Knight J., Stewart T., Furber S.., 2015
Mundy A., Knight J., Stewart T., Furber S.., 2015
A simplex method for function minimization
Nelder J., Mead R.., 1965
Nelder J., Mead R.., 1965
SpiNNaker: a 1-w 18-core system-on-chip for massively-parallel neural network simulation
Painkras E., Plana L., Garside J., Temple S., Galluppi F., Patterson C.., 2013
Painkras E., Plana L., Garside J., Temple S., Galluppi F., Patterson C.., 2013
Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms.
Petrovici MA, Vogginger B, Muller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schuffny R, Schemmel J, Meier K., PLoS ONE 9(10), 2014
PMID: 25303102
Petrovici MA, Vogginger B, Muller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schuffny R, Schemmel J, Meier K., PLoS ONE 9(10), 2014
PMID: 25303102
Six networks on a universal neuromorphic computing substrate.
Pfeil T, Grubl A, Jeltsch S, Muller E, Muller P, Petrovici MA, Schmuker M, Bruderle D, Schemmel J, Meier K., Front Neurosci 7(), 2013
PMID: 23423583
Pfeil T, Grubl A, Jeltsch S, Muller E, Muller P, Petrovici MA, Schmuker M, Bruderle D, Schemmel J, Meier K., Front Neurosci 7(), 2013
PMID: 23423583
Press W., Teukolsky S., Vetterling W., Flannery B.., 2007
Neuronal parameter optimization
Prinz A.., 2007
Prinz A.., 2007
The Leaky Integrate-and-Fire neuron: a platform for synaptic model exploration on the SpiNNaker chip
Rast A., Galluppi F., Jin X., Furber S.., 2010
Rast A., Galluppi F., Jin X., Furber S.., 2010
Tolerance of a binary associative memory towards stuck-at-faults
Rückert U., Surmann H.., 1991
Rückert U., Surmann H.., 1991
A wafer-scale neuromorphic hardware system for large-scale neural modeling
Schemmel J., Brüderle D., Grübl A., Hock M., Meier K., Millner S.., 2010
Schemmel J., Brüderle D., Grübl A., Hock M., Meier K., Millner S.., 2010
Neuromorphic hardware in the loop: training a deep spiking setwork on the brainscales wafer-scale system
Schmitt S., Klaehn J., Bellec G., Gruebl A., Guettler M., Hartel A.., 2017
Schmitt S., Klaehn J., Bellec G., Gruebl A., Guettler M., Hartel A.., 2017
A neuromorphic network for generic multivariate data classification.
Schmuker M, Pfeil T, Nawrot MP., Proc. Natl. Acad. Sci. U.S.A. 111(6), 2014
PMID: 24469794
Schmuker M, Pfeil T, Nawrot MP., Proc. Natl. Acad. Sci. U.S.A. 111(6), 2014
PMID: 24469794
VLSI Implementation of a 2.8 Gevent/s Packet-Based AER Interface with Routing and Event Sorting Functionality.
Scholze S, Schiefer S, Partzsch J, Hartmann S, Mayr CG, Hoppner S, Eisenreich H, Henker S, Vogginger B, Schuffny R., Front Neurosci 5(), 2011
PMID: 22016720
Scholze S, Schiefer S, Partzsch J, Hartmann S, Mayr CG, Hoppner S, Eisenreich H, Henker S, Vogginger B, Schuffny R., Front Neurosci 5(), 2011
PMID: 22016720
Iterative retrieval of sparsely coded associative memory patterns
Schwenker F., Sommer F., Palm G.., 1996
Schwenker F., Sommer F., Palm G.., 1996
Correctness and performance of the SpiNNaker architecture
Sharp T., Furber S.., 2013
Sharp T., Furber S.., 2013
Die Lernmatrix
Steinbuch K.., 1961
Steinbuch K.., 1961
Stöckel A.., 2015
Power analysis of large-scale, real-time neural networks on SpiNNaker
Stromatias E., Galluppi F., Patterson C., Furber S.., 2013
Stromatias E., Galluppi F., Patterson C., Furber S.., 2013
Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on SpiNNaker
Stromatias E., Neil D., Galluppi F., Pfeiffer M., Liu S., Furber S.., 2015
Stromatias E., Neil D., Galluppi F., Pfeiffer M., Liu S., Furber S.., 2015
A pulse communication flow ready for accelerated neuromorphic experiments
Thanasoulis V., Vogginger B., Partzsch J., Schuffny R.., 2014
Thanasoulis V., Vogginger B., Partzsch J., Schuffny R.., 2014
Traub R., Miles R.., 1991
Full-scale simulation of a cortical microcircuit on SpiNNaker
Van S., Rowley A., Hopkins M., Schmidt M., Senk J., Stokes A.., 2016
Van S., Rowley A., Hopkins M., Schmidt M., Senk J., Stokes A.., 2016
Non-holographic associative memory.
Willshaw DJ, Buneman OP, Longuet-Higgins HC., Nature 222(5197), 1969
PMID: 5789326
Willshaw DJ, Buneman OP, Longuet-Higgins HC., Nature 222(5197), 1969
PMID: 5789326
GeNN: a code generation framework for accelerated brain simulations.
Yavuz E, Turner J, Nowotny T., Sci Rep 6(), 2016
PMID: 26740369
Yavuz E, Turner J, Nowotny T., Sci Rep 6(), 2016
PMID: 26740369
Export
Markieren/ Markierung löschen
Markierte Publikationen
Web of Science
Dieser Datensatz im Web of Science®Quellen
PMID: 28878642
PubMed | Europe PMC
Suchen in