1 citations · 2 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…
A Hardware-aware Hopfield Network with a Nonlinear Memristor Array for Robust Associative Memory with Superlinear Capacity
Younghyun Lee, Hakseung Rhee, Unhyeon Kang +9
Associative memory retrieves complete patterns from partial or corrupted inputs and constitutes a primitive form of generative inference. Classical Hopfield networks (CHN) provide…
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…