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
OA 1.13 MB
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.
Frontiers in Computational Neuroscience
Page URI


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. doi:10.3389/fncom.2017.00071
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:
This Item is protected by copyright and/or related rights. [...]
Access Level
OA Open Access
Zuletzt Hochgeladen
MD5 Prüfsumme

54 References

Daten bereitgestellt von Europe PubMed Central.

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


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
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
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
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
Realizing biological spiking network models in a configurable wafer-scale hardware system
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
Overview of the SpiNNaker system architecture
Furber S., Lester D., Plana L., Garside J., Painkras E., Temple S.., 2013
Spike-response model
Gerstner W.., 2008

Gerstner W., Kistler W., Naud R., Paninski L.., 2014
NEST (NEural simulation tool)
Gewaltig M.-O., Diesmann M.., 2007
Finding a roadmap to achieve large neuromorphic hardware systems.
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

Jeltsch S.., 2014

Knoblauch A.., 2003

Kohonen T.., 1977
The human brain project.
Markram H., Sci. Am. 306(6), 2012
PMID: 22649994
A logical calculus of the ideas immanent in nervous activity
McCulloch W., Pitts W.., 1943
An efficient SpiNNaker implementation of the neural engineering framework
Mundy A., Knight J., Stewart T., Furber S.., 2015
A simplex method for function minimization
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
On associative memory.
Palm G., Biol Cybern 36(1), 1980
PMID: 7353062
Neural associative memories and sparse coding.
Palm G., Neural Netw 37(), 2012
PMID: 23043727
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
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

Press W., Teukolsky S., Vetterling W., Flannery B.., 2007
Neuronal parameter optimization
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
Tolerance of a binary associative memory towards stuck-at-faults
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
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
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
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
Iterative retrieval of sparsely coded associative memory patterns
Schwenker F., Sommer F., Palm G.., 1996
Correctness and performance of the SpiNNaker architecture
Sharp T., Furber S.., 2013
Die Lernmatrix
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
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
A pulse communication flow ready for accelerated neuromorphic experiments
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
Non-holographic associative memory.
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


Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®


PMID: 28878642
PubMed | Europe PMC

Suchen in

Google Scholar