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Compact Associative Memory for AER Spike Decoding in FPGA-Based Evolvable SNN Emulation

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

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Abstract

A spike decoding scheme for Address Event Representation (AER)-based transmission in Spiking Neural Network (SNN) emulators is introduced. The proposed scheme is a modified associative memory based on an efficient use of BRAM, supporting connectivity upgrade in real-time for hardware implementations of evolutionary networks. After analysing the different options and selecting the most efficient one, a prototype example based on FPGA is provided together with a novel hashing technique to demonstrate a compact on-chip solution for implementing inter-chip connectivity in SNN.

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Acknowledgments

Work supported in part by the Spanish Ministry of Science and Innovation under Project TEC2015-67278-R, and European Social Fund (ESF). Mireya Zapata holds a scholarship from National Secretary of High Education Science Technology and Innovation (SENACYT) of the Ecuadorian government.

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Authors and Affiliations

  1. Department of Electronics Engineering, Universitat Politècnica de Catalunya, Jordi Girona, 1-3, edif. C4, 08034, Barcelona, Catalunya, Spain

    Mireya Zapata & Jordi Madrenas

Authors
  1. Mireya Zapata
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  2. Jordi Madrenas
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Corresponding author

Correspondence to Mireya Zapata .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Zapata, M., Madrenas, J. (2016). Compact Associative Memory for AER Spike Decoding in FPGA-Based Evolvable SNN Emulation. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_47

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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