Hardware-friendly Log-scale Quantization for CNNs with Activation Functions Containing Negative Values. - Dahun Choi; Hyun Kim 001 (ISOCC, 2021)

Hardware-Friendly Logarithmic Quantization with Mixed-Precision for MobileNetV2. - Dahun Choi; Hyun Kim 001 (AICAS, 2022)

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

GDS: Gradient Distribution Scaling-based Gradient Quantization for Low-complexity and Hardware-friendly Training of Instance Segmentation Models. - Dahun Choi; Hyun Kim 001 (ICEIC, 2024)

Mixed Precision Quantization with Hardware-Friendly Activation Functions for Hybrid ViT Models. - Beom Jin Kang; Dahun Choi; Hyun Kim 001 (ICEIC, 2024)

ARC: Adaptive Rounding and Clipping Considering Gradient Distribution for Deep Convolutional Neural Network Training. - Dahun Choi; Hyun Kim 001 (ISCAS, 2024)

HLQ: Hardware-Friendly Logarithmic Quantization Aware Training for Power-Efficient Low-Precision CNN Models. - Dahun Choi; Juntae Park; Hyun Kim 001 (IEEE Access, 2024)

Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques. - Hyeonseok Hong; Dahun Choi; Namjoon Kim; Haein Lee; Beom Jin Kang; Huibeom Kang; Hyun Kim 001 (J. Real Time Image Process., 2024)

Mobile-X: Dedicated FPGA Implementation of the MobileNet Accelerator Optimizing Depthwise Separable Convolution. - Hyeonseok Hong; Dahun Choi; Namjoon Kim; Hyun Kim 001 (IEEE Trans. Circuits Syst. II Express Briefs, 2024)

GradQ-ViT: Robust and Efficient Gradient Quantization for Vision Transformers. - Dahun Choi; Hyun Kim 001 (AAAI, 2025)