| OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator J Park, H Yoon, D Ahn, J Choi, JJ Kim Proceedings of Machine Learning and Systems 2, 363-378, 2020 | 41 | 2020 |
| Input-splitting of large neural networks for power-efficient accelerator with resistive crossbar memory array Y Kim, H Kim, D Ahn, JJ Kim Proceedings of the International Symposium on Low Power Electronics and …, 2018 | 36 | 2018 |
| Viterbi-based pruning for sparse matrix with fixed and high index compression ratio D Lee, D Ahn, T Kim, PI Chuang, JJ Kim International Conference on Learning Representations, 2018 | 22 | 2018 |
| Double Viterbi: Weight encoding for high compression ratio and fast on-chip reconstruction for deep neural network D Ahn, D Lee, T Kim, JJ Kim International Conference on Learning Representations, 2019 | 13 | 2019 |
| Temporal Dynamic Quantization for Diffusion Models J So, J Lee, D Ahn, H Kim, E Park arXiv preprint arXiv:2306.02316, 2023 | 10 | 2023 |
| Balancing computation loads and optimizing input vector loading in LSTM accelerators J Park, W Yi, D Ahn, J Kung, JJ Kim IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2019 | 10 | 2019 |
| V-LSTM: An efficient LSTM accelerator using fixed nonzero-ratio viterbi-based pruning T Kim, D Ahn, D Lee, JJ Kim IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023 | 7 | 2023 |
| Energy-Efficient In-Memory Binary Neural Network Accelerator Design Based on 8T2C SRAM Cell H Oh, H Kim, D Ahn, J Park, Y Kim, I Lee, JJ Kim IEEE Solid-State Circuits Letters 5, 70-73, 2022 | 6 | 2022 |
| Maximizing Parallel Activation of Word-Lines in MRAM-Based Binary Neural Network Accelerators D Ahn, H Oh, H Kim, Y Kim, JJ Kim IEEE Access 9, 141961 - 141969, 2021 | 6 | 2021 |
| Time-step interleaved weight reuse for LSTM neural network computing N Park, Y Kim, D Ahn, T Kim, JJ Kim Proceedings of the ACM/IEEE International Symposium on Low Power Electronics …, 2020 | 6 | 2020 |
| Squeezing Large-Scale Diffusion Models for Mobile J Choi, M Kim, D Ahn, T Kim, Y Kim, D Jo, H Jeon, JJ Kim, H Kim arXiv preprint arXiv:2307.01193, 2023 | 3 | 2023 |
| SPRITE: Sparsity-Aware Neural Processing Unit with Constant Probability of Index-Matching S Ryu, Y Oh, T Kim, D Ahn, JJ Kim 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 663-666, 2021 | 3 | 2021 |
| Energy-efficient charge sharing-based 8T2C SRAM in-memory accelerator for binary neural networks in 28nm CMOS H Oh, H Kim, D Ahn, J Park, Y Kim, I Lee, JJ Kim 2021 IEEE Asian Solid-State Circuits Conference (A-SSCC), 1-3, 2021 | 2 | 2021 |
| Searching for Robust Binary Neural Networks via Bimodal Parameter Perturbation D Ahn, H Kim, T Kim, E Park, JJ Kim Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 1 | 2023 |
| Workload-Balanced Graph Attention Network Accelerator with Top-K Aggregation Candidates N Park, D Ahn, JJ Kim Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided …, 2022 | 1 | 2022 |
| QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference T Kim, J Lee, D Ahn, S Kim, J Choi, M Kim, H Kim arXiv preprint arXiv:2402.10076, 2024 | | 2024 |
| Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis Y Kim, D Jo, H Jeon, T Kim, D Ahn, H Kim, JJ Kim Thirty-seventh Conference on Neural Information Processing Systems, 2023 | | 2023 |
