2 citations · 3 across the 5 of their papers we have counts for
5 papers
Electrolyte Bonding Engineering for Highly Uniform GeTe-based CBRAM and Parallel Hebbian Learning in Selector-free Hopfield Networks
Jiin Bang, Jingyeong Hwang, Unhyeon Kang +10
Hopfield networks offer a hardware-friendly framework for energy-efficient associative memory, yet their practical realization in memristor crossbar arrays is critically hindered b…
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,…
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…
Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
Mohammed A. Zidan, YeonJoo Jeong, Jong Hong Shin +3
For decades, advances in electronics were directly driven by the scaling of CMOS transistors according to Moore's law. However, both the CMOS scaling and the classical computer arc…