Constructive community race: full-density spiking neural network model drives neuromorphic computing
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918338799599616 |
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| author | Senk, Johanna Kurth, Anno C. Furber, Steve Gemmeke, Tobias Golosio, Bruno Heittmann, Arne Knight, James C. Müller, Eric Noll, Tobias Nowotny, Thomas Coppola, Gorka Peraza Peres, Luca Rhodes, Oliver Rowley, Andrew Schemmel, Johannes Stadtmann, Tim Tetzlaff, Tom Tiddia, Gianmarco van Albada, Sacha J. Villamar, José Diesmann, Markus |
| author_facet | Senk, Johanna Kurth, Anno C. Furber, Steve Gemmeke, Tobias Golosio, Bruno Heittmann, Arne Knight, James C. Müller, Eric Noll, Tobias Nowotny, Thomas Coppola, Gorka Peraza Peres, Luca Rhodes, Oliver Rowley, Andrew Schemmel, Johannes Stadtmann, Tim Tetzlaff, Tom Tiddia, Gianmarco van Albada, Sacha J. Villamar, José Diesmann, Markus |
| contents | The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constructive community race: full-density spiking neural network model drives neuromorphic computing Senk, Johanna Kurth, Anno C. Furber, Steve Gemmeke, Tobias Golosio, Bruno Heittmann, Arne Knight, James C. Müller, Eric Noll, Tobias Nowotny, Thomas Coppola, Gorka Peraza Peres, Luca Rhodes, Oliver Rowley, Andrew Schemmel, Johannes Stadtmann, Tim Tetzlaff, Tom Tiddia, Gianmarco van Albada, Sacha J. Villamar, José Diesmann, Markus Performance Distributed, Parallel, and Cluster Computing The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science. |
| title | Constructive community race: full-density spiking neural network model drives neuromorphic computing |
| topic | Performance Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.21185 |