AAAI 2026technical0 citations
Spikingformer: A Key Foundation Model for Spiking Neural Networks
Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Han Zhang, Jiaqi Wang, Huihui Zhou, Zhengyu Ma, Yonghong Tian
Abstract
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, some foundation SNN backbones (including Spikformer and SEW ResNet) suffer from non-spike computations (integer-float multiplications) caused by the structure of their residual connections. These non-spike computations increase SNNs
BibTeX
@inproceedings{aaai2026_spikingformerake,
title = {Spikingformer: A Key Foundation Model for Spiking Neural Networks},
author = {Chenlin Zhou and Liutao Yu and Zhaokun Zhou and Han Zhang and Jiaqi Wang and Huihui Zhou and Zhengyu Ma and Yonghong Tian},
booktitle = {AAAI 2026},
year = {2026}
}