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| 001 | 889327 | ||
| 005 | 20240313094932.0 | ||
| 037 | _ | _ | |a FZJ-2021-00218 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kurth, Anno |0 P:(DE-Juel1)176776 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Bernstein Conference 2020 |c Online |d 2020-09-29 - 2020-10-01 |w Germany |
| 245 | _ | _ | |a Sub Realtime Simulation of a Full Density Cortical Microcircuit Model on a Single Compute Node |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1611238626_3536 |2 PUB:(DE-HGF) |x Other |
| 520 | _ | _ | |a The local cortical microcircuit is a building block of the mammalian brain. Its prominent characteristics are similar across species and cortical areas. This dual homogeneity raises hopes that principles of cortical computation can be discovered. The tissue below a 1 mm2 patch of cortex comprises about 100,000 neurons. It is the smallest network in which both a realistic number of about 10,000 synapses per neuron and a connection probability of 0.1 are realized simultaneously. Potjans and Diesmann [1] compiled a prototype network model of the microcircuit. In this model the spatial structure is neglected and replaced by cell-type specific random connectivity. Each layer is represented by an excitatory and inhibitory population of integrate-and-fire model neurons. The circuit has become a benchmark network for neuromorphic computing systems: its natural size renders questions of downscaling irrelevant [2], it can routinely be simulated by present systems [3,4], and it marks an upper bound from which neuronal networks in nature are necessarily less densely connected.To apply neuronal networks with natural densities in neurorobotics, their simulation needs to become faster. The same holds true for the investigation of the long time scales of system-level learning. Achieving this is a promise of neuromorphic computing. The first intermediate objective is real-time performance, accomplished for the microcircuit only recently [4]. However, these results have to be evaluated in the light of continuously advancing mainstream architectures as they provide more flexibility at potentially lower costs.In this contribution we show performance data for the microcircuit model on two recent AMD EPYC Rome 128 core compute nodes coupled by a point-to-point Infiniband interconnect. The software is the NEST 2.14 [5] (including cherry picked bug fix 726f9b04bbd47c) simulation code providing double precision numerics and weight resolution. On a single compute node we measure sub realtime performance. With two nodes the simulation is 1.7 times faster than realtime. For the single node the energy per synaptic event is 0.26 μJ, and for the fastest configuration using two nodes 0.39 μJ. These values are in the same order of magnitude as the lowest reported for a simulation of the microcircuit model so far [6]. Our study exposes present bottlenecks and can guide the design of future software and hardware systems. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |a Advanced Computing Architectures (aca_20190115) |0 G:(DE-Juel1)aca_20190115 |c aca_20190115 |f Advanced Computing Architectures |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |x 2 |
| 700 | 1 | _ | |a Finnerty, Justin |0 P:(DE-Juel1)174496 |b 1 |u fzj |
| 700 | 1 | _ | |a Terhorst, Dennis |0 P:(DE-Juel1)169778 |b 2 |u fzj |
| 700 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 3 |u fzj |
| 700 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 4 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 5 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:889327 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)176776 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Theory, modelling and simulation |x 0 |
| 914 | 1 | _ | |y 2020 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
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| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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