The paper introduces a spike‑aware, gradient‑based mixed‑precision quantization framework for Spiking Neural Networks (SNNs), which assigns bit‑widths adaptively using first‑order gradient information. This approach avoids the memory and computational overhead of Hessian‑based methods like HAWQ‑V2 and is evaluated on VGG, ResNet, and Vision Transformer architectures across CIFAR‑10/100, ImageNet, and DVS‑CIFAR‑10 datasets. On ResNet‑34 with ImageNet, the method attains 73.30% accuracy at 4.78× compression, matching HAWQ‑V2 while reducing memory usage by 2.31× and bit‑allocation runtime by 100×, and it demonstrates energy‑efficient deployment without performance loss.
By Rajshekhar Rakshit, Devgokul B. V, Rakshitha Kalkura, Gopalakrishnan Srinivasan
In recent years, Spiking Neural Networks (SNNs) have gained massive attention as an energy-efficient neural computing paradigm due to their event-driven and sparse computing capability. However, maint...
arXiv:2608. 07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane states are commonly retained in floating point even after weight quantization.
By Hui Xie, Tong Shi, Haotong Qin, Aishan Liu, Xiaode Liu, Jinyang Guo
arXiv:2606. 13016v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.
By Zhanglu Yan, Jiayi Mao, Kaiwen Tang, Fanfan Li, Gang Pan, Tao Luo, Bowen Zhu, Qianhui Liu, Weng-Fai Wong
arXiv:2607. 11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs).
By Jiahong Zhang, Sijun Shen, Man Yao, Han Xu, Mingqiang Huang, Yonghong Tian, Bo Xu, Guoqi Li
The paper introduces the Dormant Spiking Neuron (DSN), a state-driven mechanism that allows neurons in spiking neural networks to enter a dormant state when their membrane potential falls below a negative threshold. This dynamic gating prunes redundant computations at the instance level and is trained end‑to‑end via backpropagation through time with surrogate gradients. Experiments on static and neuromorphic datasets, including ImageNet, show that DSN reduces computational energy by 40% to over 70% and improves baseline SNN accuracy by 1.34%, outperforming state‑of‑the‑art static pruning methods.
By Ruichen Ma, Yicai Chen, Pujun Zhou, Yue Zuo, Chongyang Li, Shuang Liu, Shaogang Hu, Guanchao Qiao