SRU-Q: Hardware-friendly Stochastic Rounding Unit-based Gradient Quantization for CNN Training. - Sangbeom Jeong; Dahun Choi; Hyun Kim 001 (AICAS, 2024)

A survey of FPGA and ASIC designs for transformer inference acceleration and optimization. - Beom Jin Kang; Haein Lee; Seok Kyu Yoon; Young Chan Kim; Sangbeom Jeong; Seong Jun O; Hyun Kim 001 (J. Syst. Archit., 2024)

Vision transformer models for mobile/edge devices: a survey. - Seung Il Lee; Kwanghyun Koo; Jong Ho Lee; Gilha Lee; Sangbeom Jeong; Seongjun O; Hyun Kim 001 (Multim. Syst., 2024)

LowGradQ: Adaptive Gradient Quantization for Low-Bit CNN Training via Kernel Density Estimation-Guided Thresholding and Hardware-Efficient Stochastic Rounding Unit. - Sangbeom Jeong; Seungil Lee; Hyun Kim 001 (DATE, 2025)

GASQ: Hardware-Friendly Gradient Distribution-Aware Split Quantization for Low-Bit CNN Training. - Sangbeom Jeong; Kwanghyun Koo; Hyun Kim 001 (ICEIC, 2025)