Compact Hardware Synthesis of Stochastic Spiking Neural Networks
| Autor: | Alejandro Morán, Fabio Galan-Prado, J. Font, Miquel Roca, Josep L. Rosselló |
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| Rok vydání: | 2019 |
| Předmět: |
Spiking neural network
Stochastic Processes Computer Networks and Communications Computer science Computers Action Potentials General Medicine Hardware synthesis Pattern Recognition Automated Machine Learning Computer architecture Neural Networks Computer Field-programmable gate array Neuromorphic hardware |
| Zdroj: | International journal of neural systems. 29(8) |
| ISSN: | 1793-6462 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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