Efficient parallel implementation of multilayer backpropagation networks on SpiNNakerCitation formats

  • External authors:
  • Xin Jin
  • Mikel Luján
  • Alexander D. Rast
  • Stephen R. Welbourne

Standard

Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker. / Jin, Xin; Luján, Mikel; Plana, Luis A.; Rast, Alexander D.; Welbourne, Stephen R.; Furber, Steve B.

CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.. New York, USA : Association for Computing Machinery, 2010. p. 89-90.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Jin, X, Luján, M, Plana, LA, Rast, AD, Welbourne, SR & Furber, SB 2010, Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker. in CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.. Association for Computing Machinery, New York, USA, pp. 89-90, 7th ACM International Conference on Computing Frontiers, CF'10, Bertinoro, 1/07/10. https://doi.org/10.1145/1787275.1787297

APA

Jin, X., Luján, M., Plana, L. A., Rast, A. D., Welbourne, S. R., & Furber, S. B. (2010). Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker. In CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf. (pp. 89-90). Association for Computing Machinery. https://doi.org/10.1145/1787275.1787297

Vancouver

Jin X, Luján M, Plana LA, Rast AD, Welbourne SR, Furber SB. Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker. In CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.. New York, USA: Association for Computing Machinery. 2010. p. 89-90 https://doi.org/10.1145/1787275.1787297

Author

Jin, Xin ; Luján, Mikel ; Plana, Luis A. ; Rast, Alexander D. ; Welbourne, Stephen R. ; Furber, Steve B. / Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker. CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.. New York, USA : Association for Computing Machinery, 2010. pp. 89-90

Bibtex

@inproceedings{a19c70b7b3404670976417335061b4dc,
title = "Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker",
abstract = "This paper presents an efficient implementation and performance analysis of mapping multilayer perceptron networks with the backpropagation learning rule on SpiNNaker - a massively parallel architecture dedicated for neural network simulation. A new algorithm called pipelined checker-boarding partitioning scheme is proposed for efficient mapping. The new mapping algorithm relies on a checker-board partitioning scheme, but the key advantage comes from introducing a pipelined mode. The six-stage pipelined mode captures the parallelism within each partition of the weight matrix, allowing the overlapping of communication and computation. Not only does the proposed mapping localize communication, but it can also hide a part of or even all the communication for high efficiency. {\textcopyright} 2010 author/owner(s).",
keywords = "backpropagation, mapping, mlp, parallel, pipeline, spinnaker",
author = "Xin Jin and Mikel Luj{\'a}n and Plana, {Luis A.} and Rast, {Alexander D.} and Welbourne, {Stephen R.} and Furber, {Steve B.}",
year = "2010",
doi = "10.1145/1787275.1787297",
language = "English",
isbn = "9781450300445",
pages = "89--90",
booktitle = "CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.",
publisher = "Association for Computing Machinery",
address = "United States",
note = "7th ACM International Conference on Computing Frontiers, CF'10 ; Conference date: 01-07-2010",

}

RIS

TY - GEN

T1 - Efficient parallel implementation of multilayer backpropagation networks on SpiNNaker

AU - Jin, Xin

AU - Luján, Mikel

AU - Plana, Luis A.

AU - Rast, Alexander D.

AU - Welbourne, Stephen R.

AU - Furber, Steve B.

PY - 2010

Y1 - 2010

N2 - This paper presents an efficient implementation and performance analysis of mapping multilayer perceptron networks with the backpropagation learning rule on SpiNNaker - a massively parallel architecture dedicated for neural network simulation. A new algorithm called pipelined checker-boarding partitioning scheme is proposed for efficient mapping. The new mapping algorithm relies on a checker-board partitioning scheme, but the key advantage comes from introducing a pipelined mode. The six-stage pipelined mode captures the parallelism within each partition of the weight matrix, allowing the overlapping of communication and computation. Not only does the proposed mapping localize communication, but it can also hide a part of or even all the communication for high efficiency. © 2010 author/owner(s).

AB - This paper presents an efficient implementation and performance analysis of mapping multilayer perceptron networks with the backpropagation learning rule on SpiNNaker - a massively parallel architecture dedicated for neural network simulation. A new algorithm called pipelined checker-boarding partitioning scheme is proposed for efficient mapping. The new mapping algorithm relies on a checker-board partitioning scheme, but the key advantage comes from introducing a pipelined mode. The six-stage pipelined mode captures the parallelism within each partition of the weight matrix, allowing the overlapping of communication and computation. Not only does the proposed mapping localize communication, but it can also hide a part of or even all the communication for high efficiency. © 2010 author/owner(s).

KW - backpropagation

KW - mapping

KW - mlp

KW - parallel

KW - pipeline

KW - spinnaker

U2 - 10.1145/1787275.1787297

DO - 10.1145/1787275.1787297

M3 - Conference contribution

SN - 9781450300445

SP - 89

EP - 90

BT - CF 2010 - Proceedings of the 2010 Computing Frontiers Conference|CF - Proc. Comput. Front. Conf.

PB - Association for Computing Machinery

CY - New York, USA

T2 - 7th ACM International Conference on Computing Frontiers, CF'10

Y2 - 1 July 2010

ER -