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| Home > Publications database > NeuCoNS and Stacked-Net: Facilitating the Communication for Accelerated Neuroscientific Simulations > print |
| 001 | 1032287 | ||
| 005 | 20250129092350.0 | ||
| 020 | _ | _ | |a 978-3-95806-788-2 |
| 024 | 7 | _ | |2 datacite_doi |a 10.34734/FZJ-2024-06126 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-20241209140418012-7496251-2 |
| 037 | _ | _ | |a FZJ-2024-06126 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-Juel1)178650 |a Kleijnen, Robert |b 0 |e Corresponding author |
| 245 | _ | _ | |a NeuCoNS and Stacked-Net: Facilitating the Communication for Accelerated Neuroscientific Simulations |f - 2024-01-23 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2024 |
| 300 | _ | _ | |a xx, 110, xxi-xxxiv |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |a Book |m book |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1731405090_4997 |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich Reihe Information / Information |v 106 |
| 502 | _ | _ | |a Dissertation, Duisburg-Essen , 2024 |b Dissertation |c Duisburg-Essen |d 2024 |
| 520 | _ | _ | |a Investigating the inner workings of the brain runs into multiple challenges. A couple of those challenges are the level of detail which can be obtained by in vivo experiments and the time required to investigate long-term processes. A potential solution to thesechallenges is the use of simulators which run at an accelerated speed compared to biological real time and allow to probe physiological features that are inaccessible otherwise. Computer systems can simulate parts of the brain, but traditional computer architectures achieve neither the speed-up factor nor the scale desired. The main challenges here are the massively parallel operation of the brain, the decentralisation of memory and the high level of connectivity between neurons. The eld of Neuromorphic Computing attempts to solve these challenges by developing computer systems with a fundamentally dierent architecture. By using the knowledge obtained by neuroscience, the architecture of the system can be based on the structures found in the biological brain to better mimic these characteristics. One of the challenges of such a system is the high throughput, low latency communication of spike events through the system. This work focuses on these challenges and investigates the communication trac generated on a neuromorphic system when running biologically representative large-scale spiking neural networks in order to come up with a suitable solution. The investigation of the communication trac is done using a Python based network simulator, which is presented in this work as well. This simulator analyses the communication trac with respect to both, the amount of communication data as well as the latency. To prove the correct functionality of the simulator, simulation data are compared against results obtained with already existing models as well as experimental data. This comparison not only proves the correct functionality of the simulator, but also shows o some of the advantages of this tool. The simulator oers a higher level of detail than existing models while also being able to handle more complex heterogeneous connectivity models. This last feature is unique for this tool and is especially important in this work as the heterogeneity is a key characteristic in biological neural networks. Simultaneously, thisalso allows the evaluation of neuron mapping algorithms by the simulator. To better understand the impact of dierent network designs, the tool is used to evaluate the performances resulting from a variation of dierent design aspects such as the topology, the routing algorithm, casting protocol and node size. The goal of this study is to develop a novel communication network concept that can facilitate the communication in a large-scale neurmorphic system next to providing the tooling for its examination. To achieve this goal, the knowledge obtained during the simulation study is used to conceptualize a new stacked network topology. This network topology shows a reduction of the network load over a factor of 10 and a reduction of the latency up to a factor of 3, while hardly increasing the hardware cost of the network. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5234 |a 5234 - Emerging NC Architectures (POF4-523) |c POF4-523 |f POF IV |x 0 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1032287/files/Information_106.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0510 |2 StatID |a OpenAccess |
| 915 | _ | _ | |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |a Creative Commons Attribution CC BY 4.0 |
| 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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