Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio. - Dongsoo Lee; Daehyun Ahn; Taesu Kim; Pierce I-Jen Chuang; Jae-Joon Kim (ICLR (Poster), 2018)

Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array. - Yulhwa Kim; Hyungjun Kim; Daehyun Ahn; Jae-Joon Kim (ISLPED, 2018)

Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network. - Daehyun Ahn; Dongsoo Lee; Taesu Kim; Jae-Joon Kim (ICLR (Poster), 2019)

V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning. - Taesu Kim; Daehyun Ahn; Jae-Joon Kim (FPGA, 2020)

Time-step interleaved weight reuse for LSTM neural network computing. - Naebeom Park; Yulhwa Kim; Daehyun Ahn; Taesu Kim; Jae-Joon Kim (ISLPED, 2020)

OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator. - Junki Park; Hyunsung Yoon; Daehyun Ahn; Jungwook Choi; Jae-Joon Kim (MLSys, 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)

Energy-efficient charge sharing-based 8T2C SRAM in-memory accelerator for binary neural networks in 28nm CMOS. - Hyunmyung Oh; Hyungjun Kim; Daehyun Ahn; Jihoon Park; Yulhwa Kim; Inhwan Lee; Jae-Joon Kim (A-SSCC, 2021)

SPRITE: Sparsity-Aware Neural Processing Unit with Constant Probability of Index-Matching. - Sungju Ryu; Youngtaek Oh; Taesu Kim; Daehyun Ahn; Jae-Joon Kim (DATE, 2021)

Maximizing Parallel Activation of Word-Lines in MRAM-Based Binary Neural Network Accelerators. - Daehyun Ahn; Hyunmyung Oh; Hyungjun Kim; Yulhwa Kim; Jae-Joon Kim (IEEE Access, 2021)

Workload-Balanced Graph Attention Network Accelerator with Top-K Aggregation Candidates. - Naebeom Park; Daehyun Ahn; Jae-Joon Kim (ICCAD, 2022)

Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis. - Yulhwa Kim; Dongwon Jo; Hyesung Jeon; Taesu Kim; Daehyun Ahn; Hyungjun Kim; Jae-Joon Kim (NeurIPS, 2023)

Temporal Dynamic Quantization for Diffusion Models. - Junhyuk So; Jungwon Lee; Daehyun Ahn; Hyungjun Kim; Eunhyeok Park (NeurIPS, 2023)

Searching for Robust Binary Neural Networks via Bimodal Parameter Perturbation. - Daehyun Ahn; Hyungjun Kim; Taesu Kim; Eunhyeok Park; Jae-Joon Kim (WACV, 2023)

Squeezing Large-Scale Diffusion Models for Mobile. - Jiwoong Choi; Minkyu Kim; Daehyun Ahn; Taesu Kim; Yulhwa Kim; Dongwon Jo; Hyesung Jeon; Jae-Joon Kim; Hyungjun Kim (CoRR, 2023)

V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning. - Taesu Kim; Daehyun Ahn; Dongsoo Lee; Jae-Joon Kim (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

RepTor: Re-parameterizable Temporal Convolution for Keyword Spotting via Differentiable Kernel Search. - Eunik Park; Daehyun Ahn; Hyungjun Kim (INTERSPEECH, 2024)

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference. - Taesu Kim; Jongho Lee; Daehyun Ahn; Sarang Kim; Jiwoong Choi; Minkyu Kim; Hyungjun Kim (CoRR, 2024)

GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning. - Yeonjoon Jung; Daehyun Ahn; Hyungjun Kim; Taesu Kim; Eunhyeok Park (CoRR, 2025)