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A wafer-scale neuromorphic hardware system for large-scale neural modeling
Abstract
Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal an integrated software/hardware framework has been developed which is centered around a unified neural system description language, called PyNN, that allows the scientist to describe a model and execute it in a transparent fashion on either a neuromorphic hardware system or a numerical simulator. A very large analog neuromorphic hardware system developed within FACETS is able to use complex neural models as well as realistic network topologies, i.e. it can realize more than 10000 synapses per neuron, to allow the direct execution of models which previously could have been simulated numerically only.
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Cited by
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Opportunities for neuromorphic computing algorithms and applications
Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00184-y · Cited by 1,117
Towards artificial general intelligence with hybrid Tianjic chip architecture
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, G., Zou, Z., Wu, Z., He, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1424-8 · Cited by 1,082
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 899
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BackgroundInfluentialNeuro-inspired computing chips
Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics · doi:10.1038/s41928-020-0435-7 · Cited by 892
Metadata
- Authors
- Schemmel, J., Briiderle, D., Griibl, A., Hock, M., Meier, K., & Millner, S.
- Venue
- Proceedings of 2010 IEEE International Symposium on Circuits and Systems
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 1947-1950
- OpenAlex
- W2069552454
- Cited by
- 682
- References
- 0
- FWCI
- 24.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Schemmel, J., Briiderle, D., Griibl, A., Hock, M., Meier, K., & Millner, S. (2010). A wafer-scale neuromorphic hardware system for large-scale neural modeling. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. https://doi.org/10.1109/iscas.2010.5536970
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