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| Home > Publications database > A Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks > print |
| 001 | 908994 | ||
| 005 | 20240313103131.0 | ||
| 024 | 7 | _ | |a 2128/32688 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-02936 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Trensch, Guido |0 P:(DE-Juel1)168379 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a NEST Conference 2022 |c Online |d 2022-06-23 - 2022-06-24 |w Germany |
| 245 | _ | _ | |a A Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Despite 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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| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 1 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/908994/files/NEST_2022_neuromorphic_compute_node.pdf |y OpenAccess |
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