| 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 | 5176 | 2015 |
| cudnn: Efficient primitives for deep learning S Chetlur, C Woolley, P Vandermersch, J Cohen, J Tran, B Catanzaro, ... arXiv preprint arXiv:1410.0759, 2014 | 1771 | 2014 |
| 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 | 173 | 2016 |
| All-frequency interactive relighting of translucent objects with single and multiple scattering R Wang, J Tran, D Luebke ACM Transactions on Graphics (TOG) 24 (3), 1202-1207, 2005 | 117 | 2005 |
| All-Frequency Relighting of Non-Diffuse Objects using Separable BRDF Approximation. R Wang, J Tran, DP Luebke Rendering Techniques, 345-354, 2004 | 113 | 2004 |
| DSD: regularizing deep neural networks with dense-sparse-dense training flow S Han, J Pool, S Narang, H Mao, S Tang, E Elsen, B Catanzaro, J Tran, ... | 83 | 2016 |
| All-frequency relighting of glossy objects R Wang, J Tran, D Luebke ACM Transactions on Graphics (TOG) 25 (2), 293-318, 2006 | 50 | 2006 |
| Rhythm: Harnessing data parallel hardware for server workloads SR Agrawal, V Pistol, J Pang, J Tran, D Tarjan, AR Lebeck ACM SIGPLAN Notices 49 (4), 19-34, 2014 | 49 | 2014 |
| Parallel support vector machines in practice S Tyree, JR Gardner, KQ Weinberger, K Agrawal, J Tran arXiv preprint arXiv:1404.1066, 2014 | 28 | 2014 |
| New challenges for cellular automata simulation on the GPU J Tran, D Jordan, D Luebke SIGGRAPH, Los Angeles. ACM. Poster, 2004 | 26 | 2004 |
| Indirectly accessing sample data to perform multi-convolution operations in a parallel processing system JC WOOLLEY, J Tran US Patent 10,255,547, 2019 | 16 | 2019 |
| Sense amp design in SOI M Golden, J Tran, B McGee, B Kuo 2005 IEEE International SOI Conference Proceedings, 118-120, 2005 | 8 | 2005 |
| Proceedings of Eurographics Symposium on Rendering L Wang, J Tran, D Luebke | 5 | 2004 |
| IOAgent: Leveraging the Application Analysis of Workload Effects S Gómez-Villamor, J Tran, S Rees, V Muntés-Mulero, JL Larriba-Pey Technical Report UPC-DAC-RR-2005-49, Department of Computer Architecture …, 2005 | 2 | 2005 |
| Inline data inspection for workload simplification JM Pool, A Kerr, J Tran, MY Siu, S Oberman US Patent 10,503,507, 2019 | 1 | 2019 |
| IOAgent: A parallel I/O workload generator S Gómez-Villamor, V Muntés-Mulero, M Pérez-Casany, J Tran, S Rees, ... European Conference on Parallel Processing, 3-14, 2006 | 1 | 2006 |
| 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 | | 2022 |
| Kernel fusion for machine learning A Kerr, M Murphy, M Hagog, J Demouth, J Tran US Patent App. 16/591,306, 2021 | | 2021 |
| Inline data inspection for workload simplification JM Pool, A Kerr, J Tran, MY Siu, S Oberman US Patent App. 16/708,261, 2020 | | 2020 |
| Indirectly accessing sample data to perform multi-convolution operations in a parallel processing system JC WOOLLEY, J Tran US Patent App. 16/365,634, 2019 | | 2019 |
