Toward Optimized In-Memory Reinforcement Learning: Leveraging 1/<i>f</i> Noise of Synaptic Ferroelectric Field-Effect-Transistors for Efficient Exploration.

[BibT_eX]

[DOI]

Jangsaeng Kim

Wonjun Shin

Adv. Intell. Syst., June, 2024

2022

On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding.

[BibT_eX]

[DOI]

IEEE Access, 2022

2021

On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2021

Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality.

[BibT_eX]

[DOI]

Neurocomputing, 2021

Direct Gradient Calculation: Simple and Variation-Tolerant On-Chip Training Method for Neural Networks.

[BibT_eX]

[DOI]

Adv. Intell. Syst., 2021

Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment.

[BibT_eX]

[DOI]

IEEE Access, 2021

2020

Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Review of candidate devices for neuromorphic applications.

[BibT_eX]

[DOI]

Proceedings of the 49th European Solid-State Device Research Conference, 2019

Jangsaeng Kim

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