1 citations · 2 across the 6 of their papers we have counts for
6 papers
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,…
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…
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…
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Seongsik Park, Jeonghee Jo, Jongkil Park +9
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To tra…
Frequency-tunable nano-oscillator based on Ovonic Threshold Switch (OTS)
Seon Jeong Kim, Seong Won Cho, Hyejin Lee +11
Nano-oscillator devices are gaining more and more attention as a prerequisite for developing novel energy-efficient computing systems based on coupled oscillators. Here, we introdu…
Markov chain Hebbian learning algorithm with ternary synaptic units
Guhyun Kim, Vladimir Kornijcuk, Dohun Kim +6
In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due i…