| Fp-agl: Filter pruning with adaptive gradient learning for accelerating deep convolutional neural networks NJ Kim, H Kim IEEE Transactions on Multimedia, 2022 | 23 | 2022 |
| Mask-soft filter pruning for lightweight CNN inference NJ Kim, H Kim 2020 International SoC Design Conference (ISOCC), 316-317, 2020 | 5 | 2020 |
| Trunk Pruning: Highly Compatible Channel Pruning for Convolutional Neural Networks without Fine-Tuning NJ Kim, H Kim IEEE Transactions on Multimedia, 2023 | 1 | 2023 |
| Hardware-friendly Activation Functions for HybridViT Models BJ Kang, NJ Kim, JH Lee, H Kim 2023 20th International SoC Design Conference (ISOCC), 147-148, 2023 | 1 | 2023 |
| Repsgd: Channel pruning using reparamerization for accelerating convolutional neural networks NJ Kim, H Kim 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2023 | 1 | 2023 |
| Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques H Hong, D Choi, N Kim, H Lee, B Kang, H Kang, H Kim Journal of Real-Time Image Processing 21 (3), 1-21, 2024 | | 2024 |
| GPIL: Gradient with PseudoInverse Learning for High Accuracy Fine-Tuning G Lee, NJ Kim, H Kim 2023 IEEE 5th International Conference on Artificial Intelligence Circuits …, 2023 | | 2023 |
| AGT: Channel Pruning Using Adaptive Gradient Training for Accelerating Convolutional Neural Networks NJ Kim, H Kim 2023 International Conference on Electronics, Information, and Communication …, 2023 | | 2023 |
