Abstract
Recently, Spiking Neural Networks (SNNs) have been considered as alternatives to the common deep neural networks (DNNs) when the energy efficiency has been targeted. The SNNs adopt an event-driven information processing approach in which the computational expenses are reduced considerably compared to DNNs without affecting the system performance. This paper presents an efficient framework based on SNNs for touch modality classification. The proposed work outperforms similar state of the art solutions by achieving a classification accuracy of 99.97% with decreased complexity and increased number of classes.
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Dabbous, A., Ibrahim, A., Valle, M. (2023). Feed-Forward SNN for Touch Modality Prediction. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_21
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DOI: https://doi.org/10.1007/978-3-031-16281-7_21
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