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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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Authors and Affiliations
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
Xinjie Li, Jianxiong Tang & Jianhuang Lai
Pazhou Lab (Huangpu), Guangzhou, 510555, China
Jianhuang Lai
Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China
Xinjie Li, Jianxiong Tang & Jianhuang Lai
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Correspondence toJianhuang Lai.
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University of Salford, Salford, Lancashire, UK
Apostolos Antonacopoulos
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
Subhasis Chaudhuri
Johns Hopkins University, Baltimore, MD, USA
Rama Chellappa
Chinese Academy of Sciences, Beijing, China
Cheng-Lin Liu
IIT Kharagpur, Kharagpur, West Bengal, India
Saumik Bhattacharya
Indian Statistical Institute Kolkata, Kolkata, West Bengal, India
Umapada Pal
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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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