The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Overview of the SpiNNaker System Architecture
Abstract
SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal is to be able to simulate the behavior of aggregates of up to a billion neurons in real time. It consists of an array of ARM9 cores, communicating via packets carried by a custom interconnect fabric. The packets are small (40 or 72 bits), and their transmission is brokered entirely by hardware, giving the overall engine an extremely high bisection bandwidth of over 5 billion packets/s. Three of the principal axioms of parallel machine design (memory coherence, synchronicity, and determinism) have been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations. A further attribute of the system is the acknowledgment, from the initial design stages, that the sheer size of the implementation will make component failures an inevitable aspect of day-to-day operation, and fault detection and recovery mechanisms have been built into the system at many levels of abstraction. This paper describes the architecture of the machine and outlines the underlying design philosophy; software and applications are to be described in detail elsewhere, and only introduced in passing here as necessary to illuminate the description.
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Pakkenberg, B. (2003) · Experimental Gerontology · doi:10.1016/s0531-5565(02)00151-1 · Cited by 485
Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Large-scale neuromorphic computing systems
Furber, S. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/051001 · Cited by 567
Self-citationNeural networks: An overview of early research, current frameworks and new challenges
Prieto, A., Prieto, B., Ortigosa, E. M., Ros, E., Pelayo, F., Ortega, J., & Rojas, I. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.014 · Cited by 391
Perez-Carrasco, J. A., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., & Linares-Barranco, B. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.71 · Cited by 324
Computational event-driven vision sensors for in-sensor spiking neural networks
Zhou, Y., Fu, J., Chen, Z., Zhuge, F., Wang, Y., Yan, J., Ma, S., Xu, L., Yuan, H., Chan, M., et al. (2023) · Nature Electronics · doi:10.1038/s41928-023-01055-2 · Cited by 283
Metadata
- Authors
- Furber, S. B., Lester, D. R., Plana, L. A., Garside, J. D., Painkras, E., Temple, S., & Brown, A. D.
- Venue
- IEEE Transactions on Computers
- Published
- 2013-12
- Volume
- 62
- Issue
- 12
- Pages
- 2454-2467
- ISSN
- 0018-9340 (ISSN-L)
- OpenAlex
- W2153041354
- Cited by
- 645
- References
- 1
- FWCI
- 42.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Furber, S. B., Lester, D. R., Plana, L. A., Garside, J. D., Painkras, E., Temple, S., & Brown, A. D. (2013). Overview of the SpiNNaker System Architecture. IEEE Transactions on Computers. https://doi.org/10.1109/tc.2012.142
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