GeCo: classification restricted Boltzmann machine hardware for on-chip learning. - Wooseok Yi; Junki Park; Jae-Joon Kim (RSP, 2017)

Maximizing system performance by balancing computation loads in LSTM accelerators. - Junki Park; Jaeha Kung; Wooseok Yi; Jae-Joon Kim (DATE, 2018)

BitBlade: Area and Energy-Efficient Precision-Scalable Neural Network Accelerator with Bitwise Summation. - Sungju Ryu; Hyungjun Kim; Wooseok Yi; Jae-Joon Kim (DAC, 2019)

Effect of Device Variation on Mapping Binary Neural Network to Memristor Crossbar Array. - Wooseok Yi; Yulhwa Kim; Jae-Joon Kim (DATE, 2019)

A 44.1TOPS/W Precision-Scalable Accelerator for Quantized Neural Networks in 28nm CMOS. - Sungju Ryu; Hyungjun Kim; Wooseok Yi; Jongeun Koo; Eunhwan Kim; Yulhwa Kim; Taesu Kim; Jae-Joon Kim (CICC, 2020)

Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators. - Junki Park; Wooseok Yi; Daehyun Ahn; Jaeha Kung; Jae-Joon Kim (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020)

GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian Inference. - Wooseok Yi; Junki Park; Jae-Joon Kim (IEEE Trans. Neural Networks Learn. Syst., 2020)

BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks. - Sungju Ryu; Hyungjun Kim; Wooseok Yi; Eunhwan Kim; Yulhwa Kim; Taesu Kim; Jae-Joon Kim (IEEE J. Solid State Circuits, 2022)

37.3 Monolithic in-Memory Computing Microprocessor for End-to-End DNN Inferencing in MRAM-Embedded 28nm CMOS Technology with 1.1Mb Weight Storage. - Soonwan Kwon; Sungmeen Myung; Jangho An; Hyunsoo Kim; Minjae Kim; Hyungwoo Lee; Wooseok Yi; Seungchul Jung; Daekun Yoon; Shinhee Han; Saeyoon Chung; Kilho Lee; et al. (ISSCC, 2025)