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000908994 005__ 20240313103131.0
000908994 0247_ $$2Handle$$a2128/32688
000908994 037__ $$aFZJ-2022-02936
000908994 041__ $$aEnglish
000908994 1001_ $$0P:(DE-Juel1)168379$$aTrensch, Guido$$b0$$eCorresponding author$$ufzj
000908994 1112_ $$aNEST Conference 2022$$cOnline$$d2022-06-23 - 2022-06-24$$wGermany
000908994 245__ $$aA Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks
000908994 260__ $$c2022
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000908994 520__ $$aDespite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high interest, but even the fastest supercomputer available today is not able to meet the challenge of accurate and reproducible simulation with hyper-real acceleration. The development of novel neuromorphic computer architectures holds out promise. Advances in System-on-Chip (SoC) device technology and tools are now providing interesting new design possibilities for application-specific implementations. We propose a novel hybrid software-hardware architecture approach for a neuromorphic compute node intended to work in a multi-node cluster configuration. The node design builds on the Xilinx Zynq-7000 SoC device architecture that combines a powerful programmable logic gate array (FPGA) and a dual-core ARM Cortex-A9 processor extension on a single chip. Although high acceleration can be achieved at low workloads, the development also reveals current technological limitations that also apply to CPU implementations of neural network simulation tools.
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000908994 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
000908994 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x2
000908994 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000908994 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b1$$ufzj
000908994 8564_ $$uhttps://juser.fz-juelich.de/record/908994/files/NEST_2022_neuromorphic_compute_node.pdf$$yOpenAccess
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000908994 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168379$$aForschungszentrum Jülich$$b0$$kFZJ
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000908994 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b1$$kFZJ
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000908994 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x1
000908994 9141_ $$y2022
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000908994 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000908994 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
000908994 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
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