A General-Purpose Model Translation System for a Universal Neural Chip

GALLUPPI, Francesco, RAST, Alexander, DAVIES, Sergio and FURBER, Steve (2010). A General-Purpose Model Translation System for a Universal Neural Chip. In: Neural Information Processing. Theory and Algorithms 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part I. Lecture Notes in Computer Science book series . Springer Berlin Heidelberg, 58-65.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-64...
Link to published version:: https://doi.org/10.1007/978-3-642-17537-4_8
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    Abstract

    This paper describes how an emerging standard neural network modelling language can be used to configure a general-purpose neural multi-chip system by describing the process of writing and loading neural network models on the SpiNNaker neuromimetic hardware. It focuses on the implementation of a SpiNNaker module for PyNN, a simulator-independent language for neural networks modelling. We successfully extend PyNN to deal with different non-standard (eg. Izhikevich) cell types, rapidly switch between them and load applications on a parallel hardware by orchestrating the software layers below it, so that they will be abstracted to the final user. Finally we run some simulations in PyNN and compare them against other simulators, successfully reproducing single neuron and network dynamics and validating the implementation.

    Item Type: Book Section
    Uncontrolled Keywords: 08 Information and Computing Sciences; Artificial Intelligence & Image Processing
    Identification Number: https://doi.org/10.1007/978-3-642-17537-4_8
    Page Range: 58-65
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 27 Apr 2020 16:16
    Last Modified: 27 Apr 2020 16:17
    URI: http://shura.shu.ac.uk/id/eprint/24469

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