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    SpiNNaker - A Spiking Neural Network Architecture

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    Contributor(s)
    Furber, Steve (editor)
    Bogdan, Petruț (editor)
    Language
    English
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    Abstract
    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come.
    URI
    https://library.oapen.org/handle/20.500.12657/47874
    Keywords
    Neuromorphic computing; Brain-inspired computing; Massively-parallel computing; Spiking neural networks; Neuro-robotics
    DOI
    10.1561/9781680836523
    ISBN
    9781680836530, 9781680836523, 9781680836530
    Publisher
    Now Publishers
    Publication date and place
    Norwell, MA, 2020
    Imprint
    Now Publishers
    Series
    NowOpen,
    Classification
    Artificial intelligence
    Pages
    350
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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