Implementing learning on the SpiNNaker universal neural chip multiprocessorCitation formats
- Authors:
- Xin Jin
- Alexander Rast
- Francesco Galluppi
- M. Mukaram Khan
- Stephen Furber
Standard
Implementing learning on the SpiNNaker universal neural chip multiprocessor. / Jin, Xin; Rast, Alexander; Galluppi, Francesco; Khan, M. Mukaram; Furber, Stephen.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5863 LNCS PART 1. ed. 2009. p. 425-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5863 LNCS, No. PART 1).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Jin, X, Rast, A, Galluppi, F, Khan, MM
& Furber, S 2009,
Implementing learning on the SpiNNaker universal neural chip multiprocessor. in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 5863 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5863 LNCS, pp. 425-432, 16th International Conference on Neural Information Processing, ICONIP 2009, Bangkok, Thailand,
1/12/09.
https://doi.org/10.1007/978-3-642-10677-4_48APA
Jin, X., Rast, A., Galluppi, F., Khan, M. M.
, & Furber, S. (2009).
Implementing learning on the SpiNNaker universal neural chip multiprocessor. In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5863 LNCS, pp. 425-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5863 LNCS, No. PART 1).
https://doi.org/10.1007/978-3-642-10677-4_48Vancouver
Author
Jin, Xin ; Rast, Alexander ; Galluppi, Francesco ; Khan, M. Mukaram
; Furber, Stephen. /
Implementing learning on the SpiNNaker universal neural chip multiprocessor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5863 LNCS PART 1. ed. 2009. pp. 425-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
Bibtex
@inproceedings{d9532ae31d044f56bb188edb452d2617,
title = "Implementing learning on the SpiNNaker universal neural chip multiprocessor",
abstract = "Large-scale neural simulation requires high-performance hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. Using a scheme driven entirely by pre-synaptic spike events, we optimize both the data representation and processing for efficiency of implementation. The deferred-event model provides a reconfigurable timing record length to meet different accuracy requirements. Results demonstrate successful STDP within a multi-chip simulation containing 60 neurons and 240 synapses. This optimisable learning model illustrates the scalable general-purpose techniques essential for developing functional learning rules on general-purpose, parallel neural hardware.",
keywords = "Event-Driven, Learning, Neural, Spiking, SpiNNaker, STDP",
author = "Xin Jin and Alexander Rast and Francesco Galluppi and Khan, {M. Mukaram} and Stephen Furber",
year = "2009",
doi = "10.1007/978-3-642-10677-4_48",
language = "English",
isbn = "3642106765",
volume = "5863 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "425--432",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 1",
note = "16th International Conference on Neural Information Processing, ICONIP 2009 ; Conference date: 01-12-2009 Through 05-12-2009",
}
RIS
TY - GEN
T1 - Implementing learning on the SpiNNaker universal neural chip multiprocessor
AU - Jin, Xin
AU - Rast, Alexander
AU - Galluppi, Francesco
AU - Khan, M. Mukaram
AU - Furber, Stephen
PY - 2009
Y1 - 2009
N2 - Large-scale neural simulation requires high-performance hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. Using a scheme driven entirely by pre-synaptic spike events, we optimize both the data representation and processing for efficiency of implementation. The deferred-event model provides a reconfigurable timing record length to meet different accuracy requirements. Results demonstrate successful STDP within a multi-chip simulation containing 60 neurons and 240 synapses. This optimisable learning model illustrates the scalable general-purpose techniques essential for developing functional learning rules on general-purpose, parallel neural hardware.
AB - Large-scale neural simulation requires high-performance hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. Using a scheme driven entirely by pre-synaptic spike events, we optimize both the data representation and processing for efficiency of implementation. The deferred-event model provides a reconfigurable timing record length to meet different accuracy requirements. Results demonstrate successful STDP within a multi-chip simulation containing 60 neurons and 240 synapses. This optimisable learning model illustrates the scalable general-purpose techniques essential for developing functional learning rules on general-purpose, parallel neural hardware.
KW - Event-Driven
KW - Learning
KW - Neural
KW - Spiking
KW - SpiNNaker
KW - STDP
UR - http://www.scopus.com/inward/record.url?scp=76649135818&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10677-4_48
DO - 10.1007/978-3-642-10677-4_48
M3 - Conference contribution
AN - SCOPUS:76649135818
SN - 3642106765
SN - 9783642106767
VL - 5863 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 425
EP - 432
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 16th International Conference on Neural Information Processing, ICONIP 2009
Y2 - 1 December 2009 through 5 December 2009
ER -
