The SpiNNaker projectCitation formats

Standard

The SpiNNaker project. / Furber, Steve B.; Galluppi, Francesco; Temple, Steve; Plana, Luis A.

In: Institute of Electrical and Electronics Engineers. Proceedings , Vol. 102, No. 5, 6750072, 2014, p. 652-665.

Research output: Contribution to journalArticlepeer-review

Harvard

Furber, SB, Galluppi, F, Temple, S & Plana, LA 2014, 'The SpiNNaker project', Institute of Electrical and Electronics Engineers. Proceedings , vol. 102, no. 5, 6750072, pp. 652-665. https://doi.org/10.1109/JPROC.2014.2304638

APA

Furber, S. B., Galluppi, F., Temple, S., & Plana, L. A. (2014). The SpiNNaker project. Institute of Electrical and Electronics Engineers. Proceedings , 102(5), 652-665. [6750072]. https://doi.org/10.1109/JPROC.2014.2304638

Vancouver

Furber SB, Galluppi F, Temple S, Plana LA. The SpiNNaker project. Institute of Electrical and Electronics Engineers. Proceedings . 2014;102(5):652-665. 6750072. https://doi.org/10.1109/JPROC.2014.2304638

Author

Furber, Steve B. ; Galluppi, Francesco ; Temple, Steve ; Plana, Luis A. / The SpiNNaker project. In: Institute of Electrical and Electronics Engineers. Proceedings . 2014 ; Vol. 102, No. 5. pp. 652-665.

Bibtex

@article{9fed179f612a405b8801b67ef74bc737,
title = "The SpiNNaker project",
abstract = "The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or 'spike.' In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world. {\textcopyright} 2014 IEEE.",
keywords = "Brain modeling, multicast algorithms, multiprocessor interconnection networks, neural network hardware, parallel programming",
author = "Furber, {Steve B.} and Francesco Galluppi and Steve Temple and Plana, {Luis A.}",
year = "2014",
doi = "10.1109/JPROC.2014.2304638",
language = "English",
volume = "102",
pages = "652--665",
journal = "IEEE. Proceedings",
issn = "0018-9219",
publisher = "IEEE",
number = "5",

}

RIS

TY - JOUR

T1 - The SpiNNaker project

AU - Furber, Steve B.

AU - Galluppi, Francesco

AU - Temple, Steve

AU - Plana, Luis A.

PY - 2014

Y1 - 2014

N2 - The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or 'spike.' In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world. © 2014 IEEE.

AB - The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or 'spike.' In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world. © 2014 IEEE.

KW - Brain modeling

KW - multicast algorithms

KW - multiprocessor interconnection networks

KW - neural network hardware

KW - parallel programming

U2 - 10.1109/JPROC.2014.2304638

DO - 10.1109/JPROC.2014.2304638

M3 - Article

VL - 102

SP - 652

EP - 665

JO - IEEE. Proceedings

JF - IEEE. Proceedings

SN - 0018-9219

IS - 5

M1 - 6750072

ER -