A general-purpose model translation system for a universal neural chip

Authors:
Francesco Galluppi;Alexander Rast;Sergio Davies;Steve Furber
Affiliations:
School of Computer Science, University of Manchester, Manchester, United Kingdom;School of Computer Science, University of Manchester, Manchester, United Kingdom;School of Computer Science, University of Manchester, Manchester, United Kingdom;School of Computer Science, University of Manchester, Manchester, United Kingdom
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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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.