Skip to main content
SpringerLink
Log in

Smart Hardware Implementation of Spiking Neural Networks

  • Conference paper
  • First Online:
Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

Included in the following conference series:

Abstract

During last years a lot of attention have been focused to the hardware implementation of Artificial Neural Networks (ANN) to efficiently exploit the inherent parallelism associated to these systems. From the different types of ANN, the Spiking Neural Networks (SNN) arise as a promising bio-inspired model that is able to emulate the expected neural behavior with a high confidence. Many works are centered in using analog circuitry to reproduce SNN with a high degree of precision, while minimizing the area and the energy costs. Nevertheless, the reliability and flexibility of these systems is lower if compared with digital implementations. In this paper we present a new, low-cost bio-inspired digital neural model for SNN along with an auxiliary Computer Aided Design (CAD) tool for the efficient implementation of high-volume SNN.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bohte, S.M., La Poutré, H., Kok, J.N.: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans. Neural Netw. 13(2), 426–435 (2002)

    Article  Google Scholar 

  2. Cassidy, A., Denham, S., Kanold, P., Andreou, A.: FPGA based silicon spiking neural array. In: Biomedical Circuits and Systems Conference BIOCAS 2007, no. 1, pp. 75–78 (2007)

    Google Scholar 

  3. Cassidy, A.S., Georgiou, J., Andreou, A.G.: Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses and neural architecture optimization. Neural Netw. 45, 4–26 (2013)

    Article  Google Scholar 

  4. Kaulmann, T., Dikmen, D., Rückert, U.: A digital framework for pulse coded neural network hardware with bit-serial operation. In: Proceedings - 7th International Conference on Hybrid Intelligent Systems, HIS 2007, pp. 302–307 (2007)

    Google Scholar 

  5. Koch, C., Segev, I.: A Bradford book, vol. 2. MIT Press, Cambridge (1998)

    Google Scholar 

  6. London, M., Roth, A., Beeren, L., Häusser, M., Latham, P.E.: Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466(7302), 7–123 (2010)

    Article  Google Scholar 

  7. Misra, J., Saha, I.: Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1–3), 239–255 (2010)

    Article  Google Scholar 

  8. Morro, A., Canals, V., Oliver, A., Alomar, M.L., Galán-Prado, F., Ballester, P.J., Rosselló, J.L.: A stochastic spiking neural network for virtual screening. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–5 (2017). doi:10.1109/TNNLS.2017.2657601

    Article  Google Scholar 

  9. Natschlager, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Netw.: Comput. Neural Syst. 9(731855466), 319–332 (1998)

    Article  MATH  Google Scholar 

  10. Omondi, A.R., Rajapakse, J.C.: FPGA Implementations of Neural Networks. Springer, Heidelberg (2006)

    Book  Google Scholar 

  11. Rossello, J.L., Alomar, M.L., Morro, A., Oliver, A., Canals, V.: High-density liquid-state machine circuitry for time-series forecasting. Int. J. Neural Syst. 26(5), 1550036 (2016)

    Article  Google Scholar 

  12. Rosselló, J.L., Canals, V., Oliver, A., Morro, A.: Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing. Int. J. Neural Syst. 24(05), 1430003 (2014)

    Article  Google Scholar 

  13. Schrauwen, B., D’Haene, M., Verstraeten, D., Campenhout, J.V.: Compact hardware liquid state machines on FPGA for real-time speech recognition. Neural Netw. 21(2–3), 511–523 (2008)

    Article  Google Scholar 

  14. Soltic, S., Kasabov, N.: Knowledge extraction from evolving spiking neural networks with rank order population coding. Int. J. Neural Syst. 20(06), 437–445 (2010)

    Article  Google Scholar 

  15. Steinmetz, P.N., Manwani, A., Koch, C., London, M., Segev, I.: Subthreshold voltage noise due to channel fluctuations in active neuronal membranes. J. Comput. Neurosci. 9(2), 133–148 (2000)

    Article  Google Scholar 

  16. Wysoski, S.G., Benuskova, L., Kasabov, N.: Fast and adaptive network of spiking neurons for multi-view visual pattern recognition. Neurocomputing 71(13–15), 2563–2575 (2008)

    Article  Google Scholar 

Download references

Acknowledgment

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the Regional European Development Funds (FEDER) under grant contract TEC2014-56244-R, and fellowship (BES-2015-076161).

Author information

Authors and Affiliations

  1. Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Palma, Spain

    Fabio Galán-Prado & Josep L. Rosselló

Authors
  1. Fabio Galán-Prado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josep L. Rosselló
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabio Galán-Prado .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Galán-Prado, F., Rosselló, J.L. (2017). Smart Hardware Implementation of Spiking Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59153-7_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation