| 001 | 906212 | ||
| 005 | 20250129092441.0 | ||
| 024 | 7 | _ | |a 10.1109/MCSoC51149.2021.00054 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-01296 |
| 100 | 1 | _ | |a Kleijnen, Robert |0 P:(DE-Juel1)178650 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) |c Singapore |d 2021-12-20 - 2021-12-23 |w Singapore |
| 245 | _ | _ | |a A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks |
| 260 | _ | _ | |c 2021 |b IEEE |
| 300 | _ | _ | |a 320-327 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1645173585_339 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a Observing long-term learning effects caused by neuron activity in the human brain in vivo, over a period of weeks, months, or years, is impractical. Over the last decade, the field of neuromorphic computing hardware has grown significantly, i.e. SpiNNaker, BrainScaleS and Neurogrid. These novel many-core simulation platforms offer a practical alternative to study neuron behaviour in the brain at an accelerated rate, with a high level of detail. However, they do by far not reach human brain scales yet as in particular the massive amount of spike communication turns out to be a bottleneck. In this paper, we introduce a network simulator specifically developed for the analysis of bandwidth load and latency of different network topologies and communication protocols in neuromorphic computing communication networks in high detail. Unique to this simulator, compared to state of the art network models and simulators, is its ability to simulate the impact of heterogeneous neural connectivity by different models as well as the evaluation of neuron mapping algorithms. We cross-check the simulator by comparing the results of a run using a homogeneous neural network to the bandwidth load resulting from comparable works, but simultaneously show the increased level of detail reached with our simulator. Finally, we show the impact heterogeneous connectivity can have on the bandwidth and how different neuron mapping algorithms can enhance this effect. |
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| 700 | 1 | _ | |a Robens, Markus |0 P:(DE-Juel1)156319 |b 1 |u fzj |
| 700 | 1 | _ | |a Schiek, Michael |0 P:(DE-Juel1)133935 |b 2 |u fzj |
| 700 | 1 | _ | |a van Waasen, Stefan |0 P:(DE-Juel1)142562 |b 3 |u fzj |
| 773 | _ | _ | |a 10.1109/MCSoC51149.2021.00054 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/906212/files/Network%20Simulator%20Paper%20-%20Accepted%20Version.pdf |y Restricted |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)ZEA-2-20090406 |k ZEA-2 |l Zentralinstitut für Elektronik |x 0 |
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| 981 | _ | _ | |a I:(DE-Juel1)PGI-4-20110106 |
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