S$^2$NN: Sub-bit Spiking Neural Networks

• URL: http://arxiv.org/abs/2509.24266v2
• Date: Fri, 24 Oct 2025 08:50:37 GMT
• Title: S$^2$NN: Sub-bit Spiking Neural Networks
• Authors: Wenjie Wei, Malu Zhang, Jieyuan Zhang, Ammar Belatreche, Shuai Wang, Yimeng Shan, Hanwen Liu, Honglin Cao, Guoqing Wang, Yang Yang, Haizhou Li,
• Abstract summary: Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence.<n>Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks.<n>We propose Sub-bit Spiking Neural Networks (S$2$NNs) that represent weights with less than one bit.
• Score: 53.08060832135342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks. To further explore the compression and acceleration potential of SNNs, we propose Sub-bit Spiking Neural Networks (S$^2$NNs) that represent weights with less than one bit. Specifically, we first establish an S$^2$NN baseline by leveraging the clustering patterns of kernels in well-trained binary SNNs. This baseline is highly efficient but suffers from \textit{outlier-induced codeword selection bias} during training. To mitigate this issue, we propose an \textit{outlier-aware sub-bit weight quantization} (OS-Quant) method, which optimizes codeword selection by identifying and adaptively scaling outliers. Furthermore, we propose a \textit{membrane potential-based feature distillation} (MPFD) method, improving the performance of highly compressed S$^2$NN via more precise guidance from a teacher model. Extensive results on vision tasks reveal that S$^2$NN outperforms existing quantized SNNs in both performance and efficiency, making it promising for edge computing applications.

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