Abstract
The implementation of a synfire chain (SFC) application that performs synchronous alignment mapped on a hardware multi-processor architecture (SNAVA) is reported. This demonstrates a flexible SNN modeling capability of the architecture. The neural algorithm is executed by means of a digital Spiking Neural Network (SNN) emulator, using single instruction multiple data (SIMD) processing. The flexibility and capability of SNAVA to solve complex nonlinear algorithm was verified using time slot emulation on a customized neural topology. The SFC application has been implemented on an FPGA Kintex 7 using a network of 200 neurons with 7500 synaptic connections.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Carrillo, S., et al.: Scalable hierarchical network on chip architecture for spiking neural network hardware implementations. IEEE Trans. Parallel Distrib. Syst. 24(12), 2451–2461 (2012)
Khan, M., et al.: SpiNNaker: mapping neural networks onto a massively-parallel chip multiprocessor. In: International Joint Conference on Neural Networks (2008)
Moore, S., et al.: Bluehive a field programmable custom computing machine for extreme-scale real-time neural network simulation. In: IEEE 20th Annual International Symposium Field-Programmable Custom Computing Machines (FCCM) (2012)
Nageswaran, J.M., et al.: A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Netw. 22(5–6), 791–800 (2009)
Sanchez, G.: Spiking neural network emulation based on configurable devices. Doctoral dissertation, Universitat Politecnica de Catalunya (2014)
Dorta, T., et al.: AER-SRT: scalable spike distribution by means of synchronous serial ring topology address event representation. Neurocomputing 171, 1684–1690 (2015)
Diesmann, M., et al.: Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999)
Brette, R., Goodman, D.: Brian documentation realease 1.4.2 (2016)
Acknowledgments
This work was 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 Ecuador.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zapata, M., Madrenas, J. (2016). Synfire Chain Emulation by Means of Flexible SNN Modeling on a SIMD Multicore Architecture. 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_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-44778-0_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44777-3
Online ISBN: 978-3-319-44778-0
eBook Packages: Computer ScienceComputer Science (R0)