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SpiNNaker: event-based simulation - quantitative behaviour

SpiNNaker: event-based simulation - quantitative behaviour
SpiNNaker: event-based simulation - quantitative behaviour
SpiNNaker (Spiking Neural Network Architecture) is a specialised computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state - held in distributed memory - is not coherent. Time models itself: there is no notion of computed simulation time - wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behaviour closer to its intended simulation target - neural systems. We describe how SpiNNaker simulates large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to 9 million synaptic connections on a 768 core subsystem (~1400th of the full system) to accurately produce logically predicted results.
Event-based computing, neuromorphic computing, neural system simulation, real-time simulation, specialised simulation platforms
2332-7766
Brown, Andrew
5c19e523-65ec-499b-9e7c-91522017d7e0
Chad, John
d220e55e-3c13-4d1d-ae9a-1cfae8ccfbe1
Kamarudin, Muhammad Raihaan, Bin
1d7cfa98-4e34-48ad-b39b-6a3d3f120063
Dugan, Kier
1673d1bb-5b55-484d-9c95-4eae04d0cdfb
Furber, Stephen
e9e61fb3-2bb8-45be-9f39-aaf7cbe0a801
Brown, Andrew
5c19e523-65ec-499b-9e7c-91522017d7e0
Chad, John
d220e55e-3c13-4d1d-ae9a-1cfae8ccfbe1
Kamarudin, Muhammad Raihaan, Bin
1d7cfa98-4e34-48ad-b39b-6a3d3f120063
Dugan, Kier
1673d1bb-5b55-484d-9c95-4eae04d0cdfb
Furber, Stephen
e9e61fb3-2bb8-45be-9f39-aaf7cbe0a801

Brown, Andrew, Chad, John, Kamarudin, Muhammad Raihaan, Bin, Dugan, Kier and Furber, Stephen (2017) SpiNNaker: event-based simulation - quantitative behaviour. IEEE Transactions on Multiscale Computing Systems. (doi:10.1109/TMSCS.2017.2748122).

Record type: Article

Abstract

SpiNNaker (Spiking Neural Network Architecture) is a specialised computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state - held in distributed memory - is not coherent. Time models itself: there is no notion of computed simulation time - wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behaviour closer to its intended simulation target - neural systems. We describe how SpiNNaker simulates large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to 9 million synaptic connections on a 768 core subsystem (~1400th of the full system) to accurately produce logically predicted results.

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TMSCS-2017-05-0030-main - Accepted Manuscript
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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 22 November 2017
Additional Information: AM is in publisher template.
Keywords: Event-based computing, neuromorphic computing, neural system simulation, real-time simulation, specialised simulation platforms

Identifiers

Local EPrints ID: 413671
URI: http://eprints.soton.ac.uk/id/eprint/413671
ISSN: 2332-7766
PURE UUID: 3d364441-3da6-4da7-8e7d-5bfb781eb48f
ORCID for John Chad: ORCID iD orcid.org/0000-0001-6442-4281

Catalogue record

Date deposited: 31 Aug 2017 16:31
Last modified: 17 Dec 2019 05:56

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