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.
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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.
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© 2016 Springer International Publishing Switzerland
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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
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DOI: https://doi.org/10.1007/978-3-319-44778-0_43
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