International Journal of Parallel Programming

, Volume 40, Issue 6, pp 553–582 | Cite as

Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System

  • Alexander D. Rast
  • Javier Navaridas
  • Xin Jin
  • Francesco Galluppi
  • Luis A. Plana
  • Jose Miguel-Alonso
  • Cameron Patterson
  • Mikel Luján
  • Steve Furber
Open Access


Neural networks present a fundamentally different model of computation from the conventional sequential digital model, for which conventional hardware is typically poorly matched. However, a combination of model and scalability limitations has meant that neither dedicated neural chips nor FPGA’s have offered an entirely satisfactory solution. SpiNNaker introduces a different approach, the “neuromimetic” architecture, that maintains the neural optimisation of dedicated chips while offering FPGA-like universal configurability. This parallel multiprocessor employs an asynchronous event-driven model that uses interrupt-generating dedicated hardware on the chip to support real-time neural simulation. Nonetheless, event handling, particularly packet servicing, requires careful and innovative design in order to avoid local processor congestion and possible deadlock. We explore the impact that spatial locality, temporal causality and burstiness of traffic have on network performance, using tunable, biologically similar synthetic traffic patterns. Having established the viability of the system for real-time operation, we use two exemplar neural models to illustrate how to implement efficient event-handling service routines that mitigate the problem of burstiness in the traffic. Extending work published in ACM Computing Frontiers 2010 with on-chip testing, simulation results indicate the viability of SpiNNaker for large-scale neural modelling, while emphasizing the need for effective burst management and network mapping. Ultimately, the goal is the creation of a library-based development system that can translate a high-level neural model from any description environment into an efficient SpiNNaker instantiation. The complete system represents a general-purpose platform that can generate an arbitrary neural network and run it with hardware speed and scale.


Asynchronous Burst Network Event-driven Universal Neural Multiprocessor Interconnection Real-time Traffic Characterisation 



The SpiNNaker project is supported by the Engineering and Physical Sciences Research Council, partly through the Advanced Processor Technologies Portfolio Partnership at the University of Manchester, and through Grants EP/D07908X/1 and GR/S61270/01; and also by ARM and Silistix. When this research was performed Dr. Javier Navaridas was supported by a post-doctoral grant of the University of the Basque Country and is now a Newton International Fellow with the University of Manchester. Prof. Jose Miguel-Alonso is supported by the Spanish Ministry of Education and Science, grant TIN2010-14931, and by Basque Government grant IT-242-07. Dr.Mikel Luján holds a Royal Society University Research Fellowship. We appreciate the support of these sponsors and industrial partners.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.


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Copyright information

© The Author(s) 2011

Authors and Affiliations

  • Alexander D. Rast
    • 1
  • Javier Navaridas
    • 1
  • Xin Jin
    • 1
  • Francesco Galluppi
    • 1
  • Luis A. Plana
    • 1
  • Jose Miguel-Alonso
    • 2
  • Cameron Patterson
    • 1
  • Mikel Luján
    • 1
  • Steve Furber
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.School of Computer ScienceUniversity of the Basque CountrySan SebastianSpain

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