Scalable Network Emulation on Analog Neuromorphic Hardware
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909378523693056 |
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| author | Arnold, Elias Spilger, Philipp Straub, Jan V. Müller, Eric Dold, Dominik Meoni, Gabriele Schemmel, Johannes |
| author_facet | Arnold, Elias Spilger, Philipp Straub, Jan V. Müller, Eric Dold, Dominik Meoni, Gabriele Schemmel, Johannes |
| contents | We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks and allows for sequential model emulation on undersized neuromorphic resources if the largest recurrent subnetwork and the required neuron fan-in fit on the substrate. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures. We demonstrate the training of two deep spiking neural network models -- using the MNIST and EuroSAT datasets -- that exceed the physical size constraints of a single-chip BrainScaleS-2 system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Network Emulation on Analog Neuromorphic Hardware Arnold, Elias Spilger, Philipp Straub, Jan V. Müller, Eric Dold, Dominik Meoni, Gabriele Schemmel, Johannes Neural and Evolutionary Computing We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks and allows for sequential model emulation on undersized neuromorphic resources if the largest recurrent subnetwork and the required neuron fan-in fit on the substrate. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures. We demonstrate the training of two deep spiking neural network models -- using the MNIST and EuroSAT datasets -- that exceed the physical size constraints of a single-chip BrainScaleS-2 system. |
| title | Scalable Network Emulation on Analog Neuromorphic Hardware |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.16840 |