Abstract
In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits their use on resource-limited devices. In this paper, we propose an Ensemble Binary Spiking Neural Network (EB-SNN) for accurate and memory-friendly visual recognition. The EB-SNN is modeled by Ensemble Binary Weights (EBW) module, which integrates multiple binary weights for lightweight SNN modeling. Meanwhile, we propose Knowledge Alignment Strategy to ensure that the EB-SNN can approximate a well-trained SNN for good performance. Experimental results show that the EB-SNN can achieve accuracy of 95.39% on CIFAR10, using \(9.3\%\) memory of full-precision SNN.
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Acknowledgement
This work was supported in part by the Key-Area Research and Development Program of Guangzhou (202007030004); in part by the National Natural Science Foundation of China(62076258).
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Li, X., Tang, J., Lai, J. (2025). EB-SNN: An Ensemble Binary Spiking Neural Network for Visual Recognition. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15308. Springer, Cham. https://doi.org/10.1007/978-3-031-78186-5_21
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DOI: https://doi.org/10.1007/978-3-031-78186-5_21
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