• DocumentCode
    727199
  • Title

    Live demonstration: Handwritten digit recognition using spiking deep belief networks on SpiNNaker

  • Author

    Stromatias, Evangelos ; Neil, Daniel ; Galluppi, Francesco ; Pfeiffer, Michael ; Shih-Chii Liu ; Furber, Steve

  • Author_Institution
    Adv. Processor Technol. Group, Univ. of Manchester, Manchester, UK
  • fYear
    2015
  • fDate
    24-27 May 2015
  • Firstpage
    1901
  • Lastpage
    1901
  • Abstract
    We demonstrate an interactive handwritten digit recognition system with a spike-based deep belief network running in real-time on SpiNNaker, a biologically inspired many-core architecture. Results show that during the simulation a SpiNNaker chip can deliver spikes in under 1 μs, with a classification latency in the order of tens of milliseconds, while consuming less than 0.3 W.
  • Keywords
    belief networks; handwritten character recognition; image recognition; multiprocessing systems; neural chips; pattern classification; SpiNNaker chip; biologically inspired manycore architecture; classification latency; interactive handwritten digit recognition system; real-time system; spike-based deep belief network; Biological neural networks; Computer architecture; Handwriting recognition; Mobile handsets; Neurons; Real-time systems; Associated Track 8.1: Neural Networks and Systems; Spiking Neural Network circuits and systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
  • Conference_Location
    Lisbon
  • Type

    conf

  • DOI
    10.1109/ISCAS.2015.7169034
  • Filename
    7169034
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=727199