% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Kleijnen:908049,
      author       = {Kleijnen, Robert and Robens, Markus and Schiek, Michael and
                      van Waasen, Stefan},
      title        = {{A} {N}etwork {S}imulator for the {E}stimation of
                      {B}andwidth {L}oad and {L}atency {C}reated by
                      {H}eterogeneous {S}piking {N}eural {N}etworks on
                      {N}euromorphic {C}omputing {C}ommunication {N}etworks},
      journal      = {Journal of Low Power Electronics and Applications},
      volume       = {12},
      number       = {2},
      issn         = {2079-9268},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-02346},
      pages        = {23 -},
      year         = {2022},
      abstract     = {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.},
      cin          = {ZEA-2},
      ddc          = {530},
      cid          = {I:(DE-Juel1)ZEA-2-20090406},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / ACA -
                      Advanced Computing Architectures (SO-092)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-HGF)SO-092},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000817696700001},
      doi          = {10.3390/jlpea12020023},
      url          = {https://juser.fz-juelich.de/record/908049},
}