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SpiNNaker: Spiking Neural Network Architecture
Mission statement novel computer architecture inspired by the working of the human brain
Founder Steve Furber
Website spinnaker.cs.manchester.ac.uk

SpiNNaker (Spiking Neural Network Architecture) is a manycore computer architecture designed by the Advanced Processor Technologies Research Group (APT) at the School of Computer Science, University of Manchester,[1] led by Steve Furber, to simulate the human brain.(see Human Brain Project). It is planned to use 1 million ARM processors (currently .5 million)[2] in a massively parallel computing platform based on spiking neural networks.[3][3][4][5][6][7][8][9][10][11]

The completed design is to be housed in 10 19-inch racks each rack holds 100,000 cores[12] the cards themselves holding the chips are held in 5 Blade enclosures and each core emulates 1000 Neurons.[12]

SpiNNaker is being used as one component of the neuromorphic computing platform for the Human Brain Project.[13][14]

See also[edit]

  • TrueNorth – a processor architecture designed solely for spiking neural networks.


  1. ^ Advanced Processor Technologies Research Group
  2. ^ Steve Furber interviewed on BBC Click
  3. ^ a b SpiNNaker Home Page, University of Manchester, retrieved 11 June 2012 
  4. ^ Furber, S. B.; Galluppi, F.; Temple, S.; Plana, L. A. (2014). "The SpiNNaker Project". Proceedings of the IEEE: 1. doi:10.1109/JPROC.2014.2304638. 
  5. ^ Xin Jin; Furber, S. B.; Woods, J. V. (2008). "Efficient modelling of spiking neural networks on a scalable chip multiprocessor". 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). pp. 2812–2819. doi:10.1109/IJCNN.2008.4634194. ISBN 978-1-4244-1820-6. 
  6. ^ A million ARM cores to host brain simulator News article on the project in the EE Times
  7. ^ Temple, S.; Furber, S. (2007). "Neural systems engineering". Journal of the Royal Society Interface. 4 (13): 193. doi:10.1098/rsif.2006.0177.  A manifesto for the SpiNNaker project, surveying and reviewing the general level of understanding of brain function and approaches to building computer modelof the brain.
  8. ^ Plana, L. A.; Furber, S. B.; Temple, S.; Khan, M.; Shi, Y.; Wu, J.; Yang, S. (2007). "A GALS Infrastructure for a Massively Parallel Multiprocessor". IEEE Design & Test of Computers. 24 (5): 454. doi:10.1109/MDT.2007.149.  A description of the Globally Asynchronous, Locally Synchronous (GALS) nature of SpiNNaker, with an overview of the asynchronous communications hardware designed to transmit neural 'spikes' between processors.
  9. ^ Navaridas, J.; Luján, M.; Miguel-Alonso, J.; Plana, L. A.; Furber, S. (2009). "Understanding the interconnection network of SpiNNaker". Proceedings of the 23rd international conference on Conference on Supercomputing - ICS '09. p. 286. doi:10.1145/1542275.1542317. ISBN 9781605584980.  Modelling and analysis of the SpiNNaker interconnect in a million-core machine, showing the suitability of the packet-switched network for large-scale spiking neural network simulation.
  10. ^ Rast, A.; Galluppi, F.; Davies, S.; Plana, L.; Patterson, C.; Sharp, T.; Lester, D.; Furber, S. (2011). "Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware". Neural Networks. 24 (9): 961–978. doi:10.1016/j.neunet.2011.06.014. PMID 21778034.  A demonstration of SpiNNaker's ability to simulate different neural models (simultaneously, if necessary) in contrast to other neuromorphic hardware.
  11. ^ Sharp, T.; Galluppi, F.; Rast, A.; Furber, S. (2012). "Power-efficient simulation of detailed cortical microcircuits on SpiNNaker". Journal of Neuroscience Methods. 210 (1): 110–118. doi:10.1016/j.jneumeth.2012.03.001. PMID 22465805.  Four-chip, real-time simulation of a four-million-synapse cortical circuit, showing the extreme energy efficiency of the SpiNNaker architecture
  12. ^ a b Video interview by computerphile with Steve Furber
  13. ^ Calimera, A; Macii, E; Poncino, M (2013). "The Human Brain Project and neuromorphic computing". Functional neurology. 28 (3): 191–6. PMC 3812737free to read. PMID 24139655. 
  14. ^ Monroe, D. (2014). "Neuromorphic computing gets ready for the (really) big time". Communications of the ACM. 57 (6): 13–15. doi:10.1145/2601069.