arXiv:2608.30792v1 Announce Type: cross
Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...
By Valentin M. Meunier, Am\'elie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Sa\"ighi
arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.
By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua
arXiv:2512. 01906v3 Announce Type: replace Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing.
By Sanja Karilanova, Subhrakanti Dey, Ay\c{c}a \"Oz\c{c}elikkale
arXiv:2606. 00120v1 Announce Type: cross Abstract: This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs).
By Liwen Jing, Yisha Lu, Tingting Yang, Li Sun, Yuxuan Shi, Yuwei Wang, Mengfan Zheng, Leiyang Xu
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
arXiv:2606. 23761v1 Announce Type: cross Abstract: Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still underperforms classical artificial neural network (ANN)-based approaches owing to binary activations and the lack of well-designed network architectures.
By Taiyu Meng, Wenbin Jiang, Haoyi Zhang, Yuhan Zhou, Haibing Yin