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Factor Graph Inference Engine on the SpiNNaker Neural Computing System

  • Indar Sugiarto
  • Jörg Conradt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

This paper presents a novel method for implementing Factor Graphs in a SpiNNaker neural computing system. The SpiNNaker system provides resources for fine-grained parallelism, designed for implementing a distributed computing system. We present a framework which utilizes available SpiNNaker resources to implement a discrete Factor Graph: a powerful graphical model for probabilistic inference. Our framework allows mapping and routing a Factor Graph on the SpiNNaker hardware using SpiNNaker’s event-based communication system. An example application of the proposed framework in a real-world robotics scenario is given and the result shows that the framework can handle computation of 26.14 MFLOPS only in 30.5ms. We demonstrate that the framework easily extends for larger Factor Graph networks in a bigger SpiNNaker system, which makes it suitable for complex and challenging computational intelligence tasks.

Keywords

parallel distributed system SpiNNaker Factor Graph 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Indar Sugiarto
    • 1
  • Jörg Conradt
    • 1
  1. 1.Neuroscientific System Theory, Fakultät für Elektro- und InformationstechnikTechnische Universität MünchenGermany

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