Temporal-adaptive Weight Quantization for Spiking Neural Networks
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917098087776256 |
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| author | Zhang, Han Meng, Qingyan Wang, Jiaqi Chen, Baiyu Ma, Zhengyu Fan, Xiaopeng |
| author_facet | Zhang, Han Meng, Qingyan Wang, Jiaqi Chen, Baiyu Ma, Zhengyu Fan, Xiaopeng |
| contents | Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this study, inspired by astrocyte-mediated synaptic modulation in the biological nervous systems, we propose Temporal-adaptive Weight Quantization (TaWQ), which incorporates weight quantization with temporal dynamics to adaptively allocate ultra-low-bit weights along the temporal dimension. Extensive experiments on static (e.g., ImageNet) and neuromorphic (e.g., CIFAR10-DVS) datasets demonstrate that our TaWQ maintains high energy efficiency (4.12M, 0.63mJ) while incurring a negligible quantization loss of only 0.22% on ImageNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal-adaptive Weight Quantization for Spiking Neural Networks Zhang, Han Meng, Qingyan Wang, Jiaqi Chen, Baiyu Ma, Zhengyu Fan, Xiaopeng Neural and Evolutionary Computing Artificial Intelligence Computer Vision and Pattern Recognition Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this study, inspired by astrocyte-mediated synaptic modulation in the biological nervous systems, we propose Temporal-adaptive Weight Quantization (TaWQ), which incorporates weight quantization with temporal dynamics to adaptively allocate ultra-low-bit weights along the temporal dimension. Extensive experiments on static (e.g., ImageNet) and neuromorphic (e.g., CIFAR10-DVS) datasets demonstrate that our TaWQ maintains high energy efficiency (4.12M, 0.63mJ) while incurring a negligible quantization loss of only 0.22% on ImageNet. |
| title | Temporal-adaptive Weight Quantization for Spiking Neural Networks |
| topic | Neural and Evolutionary Computing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.17567 |