| Resiliency of deep neural networks under quantization W Sung, S Shin, K Hwang arXiv preprint arXiv:1511.06488, 2015 | 194 | 2015 |
| FPGA-based low-power speech recognition with recurrent neural networks M Lee, K Hwang, J Park, S Choi, S Shin, W Sung 2016 IEEE International Workshop on Signal Processing Systems (SiPS), 230-235, 2016 | 104 | 2016 |
| Fixed-point performance analysis of recurrent neural networks S Shin, K Hwang, W Sung 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 90 | 2016 |
| Dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks S Shin, W Sung 2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2274-2277, 2016 | 76 | 2016 |
| Fully neural network based speech recognition on mobile and embedded devices J Park, Y Boo, I Choi, S Shin, W Sung Advances in neural information processing systems 31, 2018 | 53 | 2018 |
| Fixed-point optimization of deep neural networks with adaptive step size retraining S Shin, Y Boo, W Sung 2017 IEEE International conference on acoustics, speech and signal …, 2017 | 46 | 2017 |
| Stochastic precision ensemble: self-knowledge distillation for quantized deep neural networks Y Boo, S Shin, J Choi, W Sung Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6794-6802, 2021 | 32 | 2021 |
| Knowledge distillation for optimization of quantized deep neural networks S Shin, Y Boo, W Sung arXiv preprint arXiv, 2019 | 23* | 2019 |
| Quantized neural networks: Characterization and holistic optimization Y Boo, S Shin, W Sung 2020 IEEE Workshop on Signal Processing Systems (SiPS), 1-6, 2020 | 15 | 2020 |
| Generative Knowledge Transfer for Neural Language Models S Shin, K Hwang, W Sung arXiv preprint arXiv:1608.04077, arXiv preprint arXiv:1608.04077, 2016 | 11 | 2016 |
| Memorization capacity of deep neural networks under parameter quantization Y Boo, S Shin, W Sung ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 10 | 2019 |
| Hlhlp: Quantized neural networks training for reaching flat minima in loss surface S Shin, J Park, Y Boo, W Sung Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5784-5791, 2020 | 6 | 2020 |
| Sqwa: Stochastic quantized weight averaging for improving the generalization capability of low-precision deep neural networks S Shin, Y Boo, W Sung ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 4 | 2021 |
| S-SGD: Symmetrical stochastic gradient descent with weight noise injection for reaching flat minima W Sung, I Choi, J Park, S Choi, S Shin arXiv preprint arXiv:2009.02479, 2020 | 4 | 2020 |
| Workload-aware automatic parallelization for multi-GPU DNN training S Shin, Y Jo, J Choi, S Venkataramani, V Srinivasan, W Sung ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 3 | 2019 |
| Quantized neural network design under weight capacity constraint S Shin, K Hwang, W Sung arXiv preprint arXiv:1611.06342, 2016 | 3 | 2016 |
| 2.4 ATOMUS: A 5nm 32TFLOPS/128TOPS ML System-on-Chip for Latency Critical Applications CH Yu, HE Kim, S Shin, K Bong, H Kim, Y Boo, J Bae, M Kwon, K Charfi, ... 2024 IEEE International Solid-State Circuits Conference (ISSCC) 67, 42-44, 2024 | 1 | 2024 |
| LightTrader: World’s first AI-enabled High-Frequency Trading Solution with 16 TFLOPS/64 TOPS Deep Learning Inference Accelerators H Kim, S Yoo, J Bae, K Bong, Y Boo, K Charfi, HE Kim, HS Kim, J Kim, ... 2022 IEEE Hot Chips 34 Symposium (HCS), 1-10, 2022 | 1 | 2022 |
| Neural network training method and apparatus S Shin, S Wonyong, BOO Yoonho US Patent App. 17/526,221, 2022 | | 2022 |
| Quantization of Deep Neural Networks for Improving the Generalization Capability 신성호 서울대학교 대학원, 2020 | | 2020 |
Wonyong SungProfessor, IEEE Life Fellow, Graduate School of Engineering Practice, Seoul National UniversityE-mail megerősítve itt: snu.ac.kr