SpiNNaker: Event-Based Simulation—Quantitative BehaviorCitation formats

Standard

SpiNNaker: Event-Based Simulation—Quantitative Behavior : Event-based simulation - quantitative behaviour. / Brown, Andrew D.; Chad, John E.; Kamarudin, Raihaan; Dugan, Kier J.; Furber, Stephen B.

In: IEEE Transactions on Multi-Scale Computing Systems, Vol. 4, No. 3, 2017, p. 450-462.

Research output: Contribution to journalArticlepeer-review

Harvard

Brown, AD, Chad, JE, Kamarudin, R, Dugan, KJ & Furber, SB 2017, 'SpiNNaker: Event-Based Simulation—Quantitative Behavior: Event-based simulation - quantitative behaviour', IEEE Transactions on Multi-Scale Computing Systems, vol. 4, no. 3, pp. 450-462. https://doi.org/10.1109/TMSCS.2017.2748122

APA

Brown, A. D., Chad, J. E., Kamarudin, R., Dugan, K. J., & Furber, S. B. (2017). SpiNNaker: Event-Based Simulation—Quantitative Behavior: Event-based simulation - quantitative behaviour. IEEE Transactions on Multi-Scale Computing Systems, 4(3), 450-462. https://doi.org/10.1109/TMSCS.2017.2748122

Vancouver

Brown AD, Chad JE, Kamarudin R, Dugan KJ, Furber SB. SpiNNaker: Event-Based Simulation—Quantitative Behavior: Event-based simulation - quantitative behaviour. IEEE Transactions on Multi-Scale Computing Systems. 2017;4(3):450-462. https://doi.org/10.1109/TMSCS.2017.2748122

Author

Brown, Andrew D. ; Chad, John E. ; Kamarudin, Raihaan ; Dugan, Kier J. ; Furber, Stephen B. / SpiNNaker: Event-Based Simulation—Quantitative Behavior : Event-based simulation - quantitative behaviour. In: IEEE Transactions on Multi-Scale Computing Systems. 2017 ; Vol. 4, No. 3. pp. 450-462.

Bibtex

@article{22dc4016d97445229a4f0e80e3ae1ad4,
title = "SpiNNaker: Event-Based Simulation—Quantitative Behavior: Event-based simulation - quantitative behaviour",
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 millioncores, 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 simulateslarge 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.",
keywords = "Brain modeling, Computational modeling, Computer architecture, Engines, Event-based computing, Hardware, neural system simulation, neuromorphic computing, Neurons, real-time simulation, Real-time systems, specialised simulation platforms",
author = "Brown, {Andrew D.} and Chad, {John E.} and Raihaan Kamarudin and Dugan, {Kier J.} and Furber, {Stephen B.}",
year = "2017",
doi = "10.1109/TMSCS.2017.2748122",
language = "English",
volume = "4",
pages = "450--462",
journal = "IEEE Transactions on Multi-Scale Computing Systems",
issn = "2332-7766",
publisher = "IEEE",
number = "3",

}

RIS

TY - JOUR

T1 - SpiNNaker: Event-Based Simulation—Quantitative Behavior

T2 - Event-based simulation - quantitative behaviour

AU - Brown, Andrew D.

AU - Chad, John E.

AU - Kamarudin, Raihaan

AU - Dugan, Kier J.

AU - Furber, Stephen B.

PY - 2017

Y1 - 2017

N2 - 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 millioncores, 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 simulateslarge 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.

AB - 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 millioncores, 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 simulateslarge 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.

KW - Brain modeling

KW - Computational modeling

KW - Computer architecture

KW - Engines

KW - Event-based computing

KW - Hardware

KW - neural system simulation

KW - neuromorphic computing

KW - Neurons

KW - real-time simulation

KW - Real-time systems

KW - specialised simulation platforms

UR - http://www.scopus.com/inward/record.url?scp=85035801510&partnerID=8YFLogxK

U2 - 10.1109/TMSCS.2017.2748122

DO - 10.1109/TMSCS.2017.2748122

M3 - Article

AN - SCOPUS:85035801510

VL - 4

SP - 450

EP - 462

JO - IEEE Transactions on Multi-Scale Computing Systems

JF - IEEE Transactions on Multi-Scale Computing Systems

SN - 2332-7766

IS - 3

ER -