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
 
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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

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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.
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