000908049 001__ 908049 000908049 005__ 20250129092500.0 000908049 0247_ $$2doi$$a10.3390/jlpea12020023 000908049 0247_ $$2Handle$$a2128/31826 000908049 0247_ $$2WOS$$aWOS:000817696700001 000908049 037__ $$aFZJ-2022-02346 000908049 082__ $$a530 000908049 1001_ $$0P:(DE-Juel1)178650$$aKleijnen, Robert$$b0$$eCorresponding author 000908049 245__ $$aA Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks 000908049 260__ $$aBasel$$bMDPI$$c2022 000908049 3367_ $$2DRIVER$$aarticle 000908049 3367_ $$2DataCite$$aOutput Types/Journal article 000908049 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1676461222_10239 000908049 3367_ $$2BibTeX$$aARTICLE 000908049 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000908049 3367_ $$00$$2EndNote$$aJournal Article 000908049 520__ $$aAccelerated 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. 000908049 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0 000908049 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x1 000908049 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de 000908049 7001_ $$0P:(DE-Juel1)156319$$aRobens, Markus$$b1 000908049 7001_ $$0P:(DE-Juel1)133935$$aSchiek, Michael$$b2 000908049 7001_ $$0P:(DE-Juel1)142562$$avan Waasen, Stefan$$b3 000908049 773__ $$0PERI:(DE-600)2662567-2$$a10.3390/jlpea12020023$$gVol. 12, no. 2, p. 23 -$$n2$$p23 -$$tJournal of Low Power Electronics and Applications$$v12$$x2079-9268$$y2022 000908049 8564_ $$uhttps://juser.fz-juelich.de/record/908049/files/Network%20Simulator%20Extended%20Version%20JLPEA.pdf$$yOpenAccess 000908049 8564_ $$uhttps://juser.fz-juelich.de/record/908049/files/Supplementary%20Material.pdf$$yRestricted 000908049 909CO $$ooai:juser.fz-juelich.de:908049$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire 000908049 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178650$$aForschungszentrum Jülich$$b0$$kFZJ 000908049 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156319$$aForschungszentrum Jülich$$b1$$kFZJ 000908049 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)133935$$aForschungszentrum Jülich$$b2$$kFZJ 000908049 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)142562$$aForschungszentrum Jülich$$b3$$kFZJ 000908049 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0 000908049 9141_ $$y2022 000908049 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0 000908049 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000908049 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2020-09-03 000908049 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-09-03 000908049 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-09 000908049 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-08-24T10:13:55Z 000908049 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-08-24T10:13:55Z 000908049 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2022-08-24T10:13:55Z 000908049 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-09 000908049 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2022-11-09 000908049 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-09 000908049 920__ $$lyes 000908049 9201_ $$0I:(DE-Juel1)ZEA-2-20090406$$kZEA-2$$lZentralinstitut für Elektronik$$x0 000908049 9801_ $$aFullTexts 000908049 980__ $$ajournal 000908049 980__ $$aVDB 000908049 980__ $$aI:(DE-Juel1)ZEA-2-20090406 000908049 980__ $$aUNRESTRICTED 000908049 981__ $$aI:(DE-Juel1)PGI-4-20110106