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The Human Brain Project is a pan-European initiative that began in October 2013 and is
projected to last for ten years although we are currently only in Phase I, which will last
until April of 2016.
The project is funded by the EU's ICT program (which support technology research) with a projected cost of one billion euros. Its aim is to help to bring together a wide range of
research communities from hardware engineers to neuroscientists, programmers to
philosophers so that collectively we can make significant progress in unravelling the
most complicated machine known to Man: the human brain itself!

This massive project is divided into a number of different themes called pillars.
Our group in Manchester is part of the neuromorphic pillar whose interest is in
developing and supporting novel computer hardware which can accelerate the
simulation of large neural networks. In this first phase, our aims are to:

  • Develop and improve the software running on the largest SpiNNaker machines
    (consisting of between 100,000 and a million simple microprocessors connected
    in a hexagonal grid) to allow networks of many millions of neurons to be simulated
    in real time.
  • Make SpiNNaker hardware available to researchers all over the world via a simple
    web interface so that they can run their simulations remotely
  • Use this platform here in Manchester to contribute to the research into brain function
  • Work with partners to design a test chip for a next generation SpiNNaker machine,
    learning from our experience with the current SpiNNaker machine and feedback
    from our user base.
    The design will be completed and the machine built in the next phase of the project


SpiNNaker is a novel computer architecture inspired by the working of the human
brain whose development has been funded by the UK's Engineering and Physical
Sciences Research Council, EPSRC.

SpiNNaker Overview

A SpiNNaker machine is a massively parallel computing platform, targeted towards three main areas of research:

Neuroscience: Understanding how the brain works is a Grand Challenge of 21st century science. We will provide the platform to help neuroscientists to unravel the mystery that is the mind. The largest SpiNNaker machine will be capable of simulating a billion simple neurons, or millions of neurons with complex structure and internal dynamics.

Robotics: SpiNNaker is a good target for researchers in robotics, who need mobile, low power computation. A small SpiNNaker board makes it possible to simulate a network of tens of thousands of spiking neurons, process sensory input and generate motor output, all in real time and in a low power system.

Computer Science: SpiNNaker breaks the rules followed by traditional supercomputers that rely on deterministic, repeatable communications and reliable computation. SpiNNaker nodes communicate using simple messages (spikes) that are inherently unreliable. This break with determinism offers new challenges, but also the potential to discover powerful new principles of massively parallel computation.

Where to go to find out more:
For developers:
  • To access tools and software to run on SpiNNaker systems, see our Downloads page
  • Our Support page provides white papers, documents and FAQs.
  • The Publications page gives details of papers describing SpiNNaker in detail
Contact Us:

For further information on Spinnaker development boards or the Spinnaker project contact us at:

Our mail address is:

APT Group,
School of Computer Science,
University of Manchester
Oxford Road,
M13 9PL

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The design and construction of the SpiNNaker machine was funded by EPSRC and The University of Manchester. The ongoing support and software development, with provision of internet access to the machine, is being supported by the EU through the ICT Flagship Human Brain Project. Research using the machine is being supported from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement no. 320689 BIMPC - "Biologically-Inspired Massively-Parallel Computation". The research has also received support from ARM Ltd, and from Samsung through their GRO programme. We are grateful to all these funding bodies and companies for their support.