Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 270

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor

Khan, M.M., Lester, D.R., Plana, L.A., Rast, A., Jin, X., Painkras, E., Furber, S.B.·2008·2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Abstract

SpiNNaker is a novel chip - based on the ARM processor - which is designed to support large scale spiking neural networks simulations. In this paper we describe some of the features that permit SpiNNaker chips to be connected together to form scalable massively-parallel systems. Our eventual goal is to be able to simulate neural networks consisting of 109neurons running in dasiareal timepsila, by which we mean that a similarly sized collection of biological neurons would run at the same speed. In this paper we describe the methods by which neural networks are mapped onto the system, and how features designed into the chip are to be exploited in practice. We will also describe the modelling and verification activities by which we hope to ensure that, when the chip is delivered, it will work as anticipated.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/ijcnn.2008.4634199

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 270

Metadata

Authors
Khan, M. M., Lester, D. R., Plana, L. A., Rast, A., Jin, X., Painkras, E., & Furber, S. B.
Venue
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Publisher
IEEE
Published
2008-06
Pages
2849-2856
OpenAlex
W2121458485
Cited by
270
References
1
FWCI
9.84
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor. https://app.cochat.ai/papers/10.1109/ijcnn.2008.4634199
The paper
Khan, M. M., Lester, D. R., Plana, L. A., Rast, A., Jin, X., Painkras, E., & Furber, S. B. (2008). SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). https://doi.org/10.1109/ijcnn.2008.4634199

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.