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Article

A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks †

1
Central Institute of Engineering, Electronics and Analytics—Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
2
Faculty of Engineering, Communication Systems, University of Duisburg-Essen, 47057 Duisburg, Germany
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in MCSoC-2021, Kleijnen, R.; Robens, M.; Schiek, M.; van Waasen, S. A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks. In Proceedings of the 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC), Singapore, 20–23 December 2021; pp. 320–327. https://doi.org/10.1109/MCSoC51149.2021.00054.
Academic Editor: Andrea Acquaviva
J. Low Power Electron. Appl. 2022, 12(2), 23; https://doi.org/10.3390/jlpea12020023
Received: 1 February 2022 / Revised: 25 February 2022 / Accepted: 3 March 2022 / Published: 21 April 2022
Accelerated simulations of biological neural networks are in demand to discover the principals of biological learning. Novel many-core simulation platforms, e.g., SpiNNaker, BrainScaleS and Neurogrid, allow one to study neuron behavior in the brain at an accelerated rate, with a high level of detail. However, they do not come anywhere near simulating the human brain. The massive amount of spike communication has turned out to be a bottleneck. We specifically developed a network simulator to analyze in high detail the network loads and latencies caused by different network topologies and communication protocols in neuromorphic computing communication networks. This simulator allows simulating the impacts of heterogeneous neural networks and evaluating neuron mapping algorithms, which is a unique feature among state-of-the-art network models and simulators. The simulator was cross-checked by comparing the results of a homogeneous neural network-based run with corresponding bandwidth load results from comparable works. Additionally, the increased level of detail achieved by the new simulator is presented. Then, we show the impact heterogeneous connectivity can have on the network load, first for a small-scale test case, and later for a large-scale test case, and how different neuron mapping algorithms can influence this effect. Finally, we look at the latency estimations performed by the simulator for different mapping algorithms, and the impact of the node size. View Full-Text
Keywords: network simulator; neuromorphic computing; communication network; heterogeneous connectivity models; neuron mappings network simulator; neuromorphic computing; communication network; heterogeneous connectivity models; neuron mappings
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MDPI and ACS Style

Kleijnen, R.; Robens, M.; Schiek, M.; van Waasen, S. A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks. J. Low Power Electron. Appl. 2022, 12, 23. https://doi.org/10.3390/jlpea12020023

AMA Style

Kleijnen R, Robens M, Schiek M, van Waasen S. A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks. Journal of Low Power Electronics and Applications. 2022; 12(2):23. https://doi.org/10.3390/jlpea12020023

Chicago/Turabian Style

Kleijnen, Robert, Markus Robens, Michael Schiek, and Stefan van Waasen. 2022. "A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks" Journal of Low Power Electronics and Applications 12, no. 2: 23. https://doi.org/10.3390/jlpea12020023

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