Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks. - Se Jung Kwon; Dongsoo Lee; Byeongwook Kim; Parichay Kapoor; Baeseong Park; Gu-Yeon Wei (CoRR, 2019)

Structured Compression by Weight Encryption for Unstructured Pruning and Quantization. - Se Jung Kwon; Dongsoo Lee; Byeongwook Kim; Parichay Kapoor; Baeseong Park; Gu-Yeon Wei (CVPR, 2020)

Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation. - Insoo Chung; Byeongwook Kim; Yoonjung Choi; Se Jung Kwon; Yongkweon Jeon; Baeseong Park; Sangha Kim 002; Dongsoo Lee (EMNLP (Findings), 2020)

FleXOR: Trainable Fractional Quantization. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Yongkweon Jeon; Baeseong Park; Jeongin Yun (NeurIPS, 2020)

BiQGEMM: matrix multiplication with lookup table for binary-coding-based quantized DNNs. - Yongkweon Jeon; Baeseong Park; Se Jung Kwon; Byeongwook Kim; Jeongin Yun; Dongsoo Lee (SC, 2020)

Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization. - Byeongwook Kim; Dongsoo Lee; Yeonju Ro; Yongkweon Jeon; Se Jung Kwon; Baeseong Park; Daehwan Oh (CoRR, 2021)

Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity. - Baeseong Park; Se Jung Kwon; Dongsoo Lee; Daehwan Oh; Byeongwook Kim; Yongkweon Jeon; Yeonju Ro (CoRR, 2021)

Modulating Regularization Frequency for Efficient Compression-Aware Model Training. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Jeongin Yun; Baeseong Park; Yongkweon Jeon (CoRR, 2021)

AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models. - Se Jung Kwon; Jeonghoon Kim; Jeongin Bae; Kang Min Yoo; Jin-Hwa Kim; Baeseong Park; Byeongwook Kim; Jung-Woo Ha 001; Nako Sung; Dongsoo Lee (EMNLP (Findings), 2022)

Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression. - Baeseong Park; Se Jung Kwon; Daehwan Oh; Byeongwook Kim; Dongsoo Lee (ICLR, 2022)

nuQmm: Quantized MatMul for Efficient Inference of Large-Scale Generative Language Models. - Gunho Park; Baeseong Park; Se Jung Kwon; Byeongwook Kim; Youngjoo Lee; Dongsoo Lee (CoRR, 2022)

TF-MVP: Novel Sparsity-Aware Transformer Accelerator with Mixed-Length Vector Pruning. - Eunji Yoo; Gunho Park; Jung Gyu Min; Se Jung Kwon; Baeseong Park; Dongsoo Lee; Youngjoo Lee (DAC, 2023)

Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic. - Yulhwa Kim; Jaeyong Jang; Jehun Lee; Jihoon Park; Jeonghoon Kim; Byeongwook Kim; Baeseong Park; Se Jung Kwon; Dongsoo Lee; Jae-Joon Kim (ICLR, 2023)

Sparsity-Aware Memory Interface Architecture using Stacked XORNet Compression for Accelerating Pruned-DNN Models. - Younghoon Byun; Seungsik Moon; Baeseong Park; Se Jung Kwon; Dongsoo Lee; Gunho Park; Eunji Yoo; Jung Gyu Min; Youngjoo Lee (MLSys, 2023)

LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models. - Gunho Park; Baeseong Park; Minsub Kim; Sungjae Lee 002; Jeonghoon Kim; Beomseok Kwon; Se Jung Kwon; Byeongwook Kim; Youngjoo Lee; Dongsoo Lee (ICLR, 2024)

DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. - Sunghyeon Woo; Baeseong Park; Byeongwook Kim; Minjung Jo; Se Jung Kwon; Dongsuk Jeon; Dongsoo Lee (NeurIPS, 2024)

DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. - Sunghyeon Woo; Baeseong Park; Byeongwook Kim; Minjung Jo; Sejung Kwon; Dongsuk Jeon; Dongsoo Lee (CoRR, 2024)

HyperCLOVA X Technical Report. - Kang Min Yoo; Jaegeun Han; Sookyo In; Heewon Jeon; Jisu Jeong; Jaewook Kang; Hyunwook Kim; Kyung-Min Kim; Munhyong Kim; Sungju Kim; Donghyun Kwak; Hanock Kwak; et al. (CoRR, 2024)

FIGLUT: An Energy-Efficient Accelerator Design for FP-INT GEMM Using Look-Up Tables. - Gunho Park; Hyeokjun Kwon; Jiwoo Kim; Jeongin Bae; Baeseong Park; Dongsoo Lee; Youngjoo Lee (HPCA, 2025)