3 papers
cs.NE2026
A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks
Yechan Kang, Yongjin Kweon, Mingyeong Seo +8
Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges,…
cs.ET2025
Ovonic switches enable energy-efficient dendrite-like computing
Unhyeon Kang, Jaesang Lee, Seungmin Oh +9
Over the last decade, dendrites within individual biological neurons, which were previously thought to generally perform information pooling and networking, have now been shown to…
cs.NE2024
A More Accurate Approximation of Activation Function with Few Spikes Neurons
Dayena Jeong, Jaewoo Park, Jeonghee Jo +5
Recent deep neural networks (DNNs), such as diffusion models [1], have faced high computational demands. Thus, spiking neural networks (SNNs) have attracted lots of attention as en…