Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 29, Issue 8, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065719500047
Keywords
Neuromorphic hardware; spiking neural network; FPGA
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Funding
- Spanish Ministry of Science, Innovation and Universities
- Regional European Development Funds (FEDER) [TEC2014-56244-R, TEC2017-84877-R, BES-2015-076161]
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Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN model (SSNN) is compared with previous SSNN models, achieving a higher processing speed. We also show how the proposed model can be scaled to high-volume neural networks trained by using back propagation and applied to a pattern classification task. The proposed model achieves better results compared with other recently-published SNN models configured with unsupervised STDP learning.
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