Dongseok Kwon
Orcid: 0000-0001-7676-8938
According to our database1,
Dongseok Kwon
authored at least 24 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
Toward Optimized In-Memory Reinforcement Learning: Leveraging 1/<i>f</i> Noise of Synaptic Ferroelectric Field-Effect-Transistors for Efficient Exploration.
Adv. Intell. Syst., June, 2024
SNNSim: Investigation and Optimization of Large-Scale Analog Spiking Neural Networks Based on Flash Memory Devices.
Adv. Intell. Syst., April, 2024
Si-Based Dual-Gate Field-Effect Transistor Array for Low-Power On-Chip Trainable Hardware Neural Networks.
Adv. Intell. Syst., January, 2024
Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design, 2024
2023
Analog Synaptic Devices Based on IGZO Thin-Film Transistors with a Metal-Ferroelectric-Metal-Insulator-Semiconductor Structure for High-Performance Neuromorphic Systems.
Adv. Intell. Syst., December, 2023
1/<i>f</i> Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network.
Adv. Intell. Syst., June, 2023
2022
Novel, parallel and differential synaptic architecture based on NAND flash memory for high-density and highly-reliable binary neural networks.
Neurocomputing, 2022
Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding.
IEEE Access, 2022
IEEE Access, 2022
Proceedings of the IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits 2022), 2022
2021
On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm.
Neural Comput. Appl., 2021
Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality.
Neurocomputing, 2021
CoRR, 2021
Direct Gradient Calculation: Simple and Variation-Tolerant On-Chip Training Method for Neural Networks.
Adv. Intell. Syst., 2021
Spiking Neural Networks With Time-to-First-Spike Coding Using TFT-Type Synaptic Device Model.
IEEE Access, 2021
Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment.
IEEE Access, 2021
2020
Efficient precise weight tuning protocol considering variation of the synaptic devices and target accuracy.
Neurocomputing, 2020
Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding.
CoRR, 2020
Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems.
IEEE Access, 2020
NAND Flash Based Novel Synaptic Architecture for Highly Robust and High-Density Quantized Neural Networks With Binary Neuron Activation of (1, 0).
IEEE Access, 2020
2019
Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices.
Neural Comput. Appl., 2019
Investigation of Neural Networks Using Synapse Arrays Based on Gated Schottky Diodes.
Proceedings of the International Joint Conference on Neural Networks, 2019
Proceedings of the 49th European Solid-State Device Research Conference, 2019
2018
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018