TY  - CONF
AU  - Trensch, Guido
AU  - Morrison, Abigail
TI  - A Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks
M1  - FZJ-2022-02936
PY  - 2022
AB  - 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.
T2  - NEST Conference 2022
CY  - 23 Jun 2022 - 24 Jun 2022, Online (Germany)
Y2  - 23 Jun 2022 - 24 Jun 2022
M2  - Online, Germany
LB  - PUB:(DE-HGF)6
UR  - https://juser.fz-juelich.de/record/908994
ER  -