| Compression of deep convolutional neural networks for fast and low power mobile applications YD Kim, E Park, S Yoo, T Choi, L Yang, D Shin arXiv preprint arXiv:1511.06530, 2015 | 1143 | 2015 |
| Weighted-entropy-based quantization for deep neural networks E Park, J Ahn, S Yoo Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 327 | 2017 |
| Energy-efficient neural network accelerator based on outlier-aware low-precision computation E Park, D Kim, S Yoo 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018 | 219 | 2018 |
| Value-aware quantization for training and inference of neural networks E Park, S Yoo, P Vajda Proceedings of the European Conference on Computer Vision (ECCV), 580-595, 2018 | 215 | 2018 |
| Big/little deep neural network for ultra low power inference E Park, D Kim, S Kim, YD Kim, G Kim, S Yoon, S Yoo 2015 international conference on hardware/software codesign and system …, 2015 | 191 | 2015 |
| Tag2pix: Line art colorization using text tag with secat and changing loss H Kim, HY Jhoo, E Park, S Yoo Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 135 | 2019 |
| Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation H Jung, E Park, S Yoo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 126 | 2021 |
| McDRAM: Low latency and energy-efficient matrix computations in DRAM H Shin, D Kim, E Park, S Park, Y Park, S Yoo IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018 | 112 | 2018 |
| Profit: A novel training method for sub-4-bit mobilenet models E Park, S Yoo Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 108 | 2020 |
| Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models C Lee, J Jin, T Kim, H Kim, E Park Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13355 …, 2024 | 94* | 2024 |
| MEANTIME: Mixture of attention mechanisms with multi-temporal embeddings for sequential recommendation SM Cho, E Park, S Yoo Proceedings of the 14th ACM Conference on recommender systems, 515-520, 2020 | 70 | 2020 |
| McDRAM v2: In-dynamic random access memory systolic array accelerator to address the large model problem in deep neural networks on the edge S Cho, H Choi, E Park, H Shin, S Yoo IEEE Access 8, 135223-135243, 2020 | 57 | 2020 |
| Temporal dynamic quantization for diffusion models J So, J Lee, D Ahn, H Kim, E Park Advances in Neural Information Processing Systems 36, 2024 | 38 | 2024 |
| Proceedings of the IEEE/CVF international conference on computer vision H Kim, HY Jhoo, E Park, S Yoo IEEE, 2021 | 34 | 2021 |
| NIPQ: Noise proxy-based integrated pseudo-quantization J Shin, J So, S Park, S Kang, S Yoo, E Park Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 27* | 2023 |
| Accelerating graph computation with racetrack memory and pointer-assisted graph representation E Park, S Yoo, S Lee, H Li 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-4, 2014 | 21 | 2014 |
| One-shot tuner for deep learning compilers J Ryu, E Park, H Sung Proceedings of the 31st ACM SIGPLAN International Conference on Compiler …, 2022 | 20 | 2022 |
| Precision highway for ultra low-precision quantization E Park, D Kim, S Yoo, P Vajda arXiv preprint arXiv:1812.09818, 2018 | 16 | 2018 |
| INSTA-BNN: Binary neural network with instance-aware threshold C Lee, H Kim, E Park, JJ Kim Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 11 | 2023 |
| FRDiff: Feature Reuse for Universal Training-free Acceleration of Diffusion Models J So, J Lee, E Park arXiv preprint arXiv:2312.03517, 2023 | 8 | 2023 |
