| 2024 | ASPLOS | LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models. | Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh, Minsoo Rhu |
| 2022 | ISCA | Training personalized recommendation systems from (GPU) scratch: look forward not backwards. | Youngeun Kwon, Minsoo Rhu |
| 2021 | HPCA | Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training. | Youngeun Kwon, Yunjae Lee, Minsoo Rhu |
| 2020 | ASPLOS | NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units. | Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, Minsoo Rhu |
| 2020 | ISCA | Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. | Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu |
| 2019 | MICRO | TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning. | Youngeun Kwon, Yunjae Lee, Minsoo Rhu |
| 2018 | HPCA | Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks. | Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, Stephen W. Keckler |
| 2018 | MICRO | Beyond the Memory Wall: A Case for Memory-Centric HPC System for Deep Learning. | Youngeun Kwon, Minsoo Rhu |