Title
SpiNNaker: Fault tolerance in a power- and area- constrained large-scale neuromimetic architecture
Abstract
SpiNNaker is a biologically-inspired massively-parallel computer designed to model up to a billion spiking neurons in real-time. A full-fledged implementation of a SpiNNaker system will comprise more than 10^5 integrated circuits (half of which are SDRAMs and half multi-core systems-on-chip). Given this scale, it is unavoidable that some components fail and, in consequence, fault-tolerance is a foundation of the system design. Although the target application can tolerate a certain, low level of failures, important efforts have been devoted to incorporate different techniques for fault tolerance. This paper is devoted to discussing how hardware and software mechanisms collaborate to make SpiNNaker operate properly even in the very likely scenario of component failures and how it can tolerate system-degradation levels well above those expected.
Year
DOI
Venue
2013
10.1016/j.parco.2013.09.001
Parallel Computing
Keywords
Field
DocType
different technique,large-scale neuromimetic architecture,important effort,full-fledged implementation,component failure,biologically-inspired massively-parallel computer,system design,billion spiking neuron,spinnaker system,integrated circuit,fault tolerance,system on chip,spiking neural networks
Architecture,System on a chip,Computer science,Globally asynchronous locally synchronous,Parallel computing,Systems design,Software,Fault tolerance,Spiking neural network,Integrated circuit,Embedded system,Distributed computing
Journal
Volume
Issue
ISSN
39
11
0167-8191
Citations 
PageRank 
References 
5
0.56
26
Authors
17
Name
Order
Citations
PageRank
Javier Navaridas120123.58
S. B. Furber21484179.05
Jim Garside3485.52
X. Jin427419.28
Mukaram Khan51108.55
David R. Lester622021.00
Mikel Luján754046.40
José Miguel-alonso811010.17
Eustace Painkras91477.33
Cameron Patterson1050.56
L. A. Plana1178659.01
Alexander Rast12433.15
Dominic Richards13123.41
Yebin Shi141078.74
Steve Temple1551535.17
Jian Wu1650.56
Shufan Yang1710915.18