Review of candidate devices for neuromorphic applications. - Jong-Ho Lee 002; Yoohyun Noh; Hyeongsu Kim; Jangsaeng Kim; Jong-Ho Bae; Sung Yun Woo; Sungtae Lee; Suhwan Lim; Won-Mook Kang; Young-Tak Seo; Soochang Lee; Dongseok Kwon; et al. (ESSDERC, 2019)

Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding. - Seongbin Oh; Dongseok Kwon; Gyuho Yeom; Won-Mook Kang; Soochang Lee; Sung Yun Woo; Jangsaeng Kim; Min Kyu Park; Jong-Ho Lee 002 (CoRR, 2020)

Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment. - Won-Mook Kang; Dongseok Kwon; Sung Yun Woo; Soochang Lee; Honam Yoo; Jangsaeng Kim; Byung-Gook Park; Jong-Ho Lee 002 (IEEE Access, 2021)

Direct Gradient Calculation: Simple and Variation-Tolerant On-Chip Training Method for Neural Networks. - Hyungyo Kim; Joon Hwang; Dongseok Kwon; Jangsaeng Kim; Min-Kyu Park; Jiseong Im; Byung-Gook Park; Jong-Ho Lee 002 (Adv. Intell. Syst., 2021)

Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality. - Jangsaeng Kim; Dongseok Kwon; Sung Yun Woo; Won-Mook Kang; Soochang Lee; Seongbin Oh; Chul-Heung Kim; Jong-Ho Bae; Byung-Gook Park; Jong-Ho Lee 002 (Neurocomputing, 2021)

On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm. - Jangsaeng Kim; Dongseok Kwon; Sung Yun Woo; Won-Mook Kang; Soochang Lee; Seongbin Oh; Chul-Heung Kim; Jong-Ho Bae; Byung-Gook Park; Jong-Ho Lee 002 (Neural Comput. Appl., 2021)

On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding. - Jiseong Im; Jaehyeon Kim; Honam Yoo; Jong-Won Baek; Dongseok Kwon; Seongbin Oh; Jangsaeng Kim; Joon Hwang; Byung-Gook Park; Jong-Ho Lee 002 (IEEE Access, 2022)

Toward Optimized In-Memory Reinforcement Learning: Leveraging 1/f Noise of Synaptic Ferroelectric Field-Effect-Transistors for Efficient Exploration. - Jangsaeng Kim; Wonjun Shin; Jiyong Yim; Dongseok Kwon; Daewoong Kwon; Jong-Ho Lee 002 (Adv. Intell. Syst., 2024)