| Learning both weights and connections for efficient neural network S Han, J Pool, J Tran, W Dally Advances in neural information processing systems 28, 2015 | 7377 | 2015 |
| Exploring the granularity of sparsity in convolutional neural networks H Mao, S Han, J Pool, W Li, X Liu, Y Wang, WJ Dally Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 487* | 2017 |
| Dsd: Dense-sparse-dense training for deep neural networks S Han, J Pool, S Narang, H Mao, E Gong, S Tang, E Elsen, P Vajda, ... arXiv preprint arXiv:1607.04381, 2016 | 320* | 2016 |
| Compressing DMA engine: Leveraging activation sparsity for training deep neural networks M Rhu, M O'Connor, N Chatterjee, J Pool, Y Kwon, SW Keckler 2018 IEEE International Symposium on High Performance Computer Architecture …, 2018 | 220 | 2018 |
| Efficient sparse-winograd convolutional neural networks X Liu, J Pool, S Han, WJ Dally arXiv preprint arXiv:1802.06367, 2018 | 157 | 2018 |
| Accelerating Sparse Deep Neural Networks A Mishra, JA Latorre, J Pool, D Stosic, D Stosic, G Venkatesh, C Yu, ... arXiv preprint arXiv:2104.08378, 2021 | 154 | 2021 |
| Buddy compression: Enabling larger memory for deep learning and HPC workloads on gpus E Choukse, MB Sullivan, M O’Connor, M Erez, J Pool, D Nellans, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 44 | 2020 |
| Channel Permutations for N: M Sparsity J Pool, C Yu Advances in Neural Information Processing Systems 34, 2021 | 39 | 2021 |
| Energy-precision tradeoffs in mobile graphics processing units J Pool, A Lastra, M Singh 2008 IEEE International Conference on Computer Design, 60-67, 2008 | 39 | 2008 |
| An energy model for graphics processing units J Pool, A Lastra, M Singh 2010 IEEE International Conference on Computer Design, 409-416, 2010 | 33 | 2010 |
| Tiled compressed sparse matrix format MA Frumkin, J Pool, LS Chien US Patent App. 16/247,922, 2019 | 32 | 2019 |
| Sparse persistent rnns: Squeezing large recurrent networks on-chip F Zhu, J Pool, M Andersch, J Appleyard, F Xie arXiv preprint arXiv:1804.10223, 2018 | 31 | 2018 |
| Precision selection for energy-efficient pixel shaders J Pool, A Lastra, M Singh Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics, 159-168, 2011 | 26 | 2011 |
| Energy-precision tradeoffs in the graphics pipeline J Pool The University of North Carolina at Chapel Hill, 2012 | 25 | 2012 |
| Self-Supervised Generative Adversarial Compression C Yu, J Pool Advances in Neural Information Processing Systems 33, 2020 | 20* | 2020 |
| Data inspection for compression/decompression configuration and data type determination J Pool US Patent App. 15/914,291, 2019 | 19 | 2019 |
| Lossless compression of variable-precision floating-point buffers on GPUs J Pool, A Lastra, M Singh Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and …, 2012 | 16 | 2012 |
| Accelerating sparsity in the nvidia ampere architecture J Pool GTC 2020, 2020 | 15 | 2020 |
| Structurally sparsified backward propagation for faster long short-term memory training M Zhu, J Clemons, J Pool, M Rhu, SW Keckler, Y Xie arXiv preprint arXiv:1806.00512, 2018 | 14 | 2018 |
| Managing data sparsity for neural networks J Pool, G Venkatesh, JA Latorre, J Choquette, R Krashinsky, J Tran, F Xie, ... US Patent 11,392,829, 2022 | 11 | 2022 |
