| Deep learning for flow sculpting: Insights into efficient learning using scientific simulation data D Stoecklein, KG Lore, M Davies, S Sarkar, B Ganapathysubramanian Scientific reports 7 (1), 46368, 2017 | 87 | 2017 |
| Hierarchical feature extraction for efficient design of microfluidic flow patterns KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar Proceedings of The 1st International Workshop on “Feature Extraction: Modern …, 2015 | 36 | 2015 |
| A deep learning framework for causal shape transformation KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar Neural Networks 98, 305-317, 2018 | 30 | 2018 |
| Automated design for microfluid flow sculpting: multiresolution approaches, efficient encoding, and CUDA implementation D Stoecklein, M Davies, N Wubshet, J Le, B Ganapathysubramanian Journal of Fluids Engineering 139 (3), 031402, 2017 | 17* | 2017 |
| The Mozart reuse exposed dataflow processor for AI and beyond: Industrial product K Sankaralingam, T Nowatzki, V Gangadhar, P Shah, M Davies, ... Proceedings of the 49th Annual International Symposium on Computer …, 2022 | 12 | 2022 |
| FlowSculpt: Software for efficient design of inertial flow sculpting devices D Stoecklein, M Davies, JM de Rutte, CY Wu, D Di Carlo, ... Lab on a Chip 19 (19), 3277-3291, 2019 | 12 | 2019 |
| Optimizing isotope substitution in graphene for thermal conductivity minimization by genetic algorithm driven molecular simulations M Davies, B Ganapathysubramanian, G Balasubramanian Applied Physics Letters 110 (13), 2017 | 10 | 2017 |
| LookupFFN: making transformers compute-lite for CPU inference Z Zeng, M Davies, P Pulijala, K Sankaralingam, V Singh International Conference on Machine Learning, 40707-40718, 2023 | 5 | 2023 |
| A Journey of a 1,000 Kernels Begins with a Single Step: A Retrospective of Deep Learning on GPUs M Davies, I McDougall, S Anandaraj, D Machchhar, R Jain, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 3 | 2024 |
| Deep action sequence learning for causal shape transformation KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar arXiv preprint arXiv:1605.05368, 2016 | 3 | 2016 |
| ARMOR: A recompilation and instrumentation-free monitoring architecture for detecting memory exploits A Grieve, M Davies, PH Jones, J Zambreno IEEE Transactions on Computers 67 (8), 1092-1104, 2018 | 2 | 2018 |
| Violet: Architecturally Exposed Orchestration, Movement, and Placement for Generalized Deep Learning M Davies, K Sankaralingam arXiv preprint arXiv:2112.02204, 2021 | 1 | 2021 |
| Assigning workloads to physical resources in spatial architectures DCB Fanny Nina Paravecino, Michael Eric Davies, Abhishek Dilip Kulkarni, Md ... | | 2024 |
| Composable Architecture Primitives for the Era of Efficient Generalization ME Davies The University of Wisconsin-Madison, 2024 | | 2024 |
| BRYT: Data Rich Analytics Based Computer Architecture for A New Paradigm of Chip Design to Supplant Moore's Law I McDougall, S Wadle, H Batchu, M Davies, K Sankaralingam arXiv preprint arXiv:2312.13428, 2023 | | 2023 |
| Understanding the Limits of Conventional Hardware Architectures for Deep-Learning. M Davies, A Labiosa, K Sankaralingam CoRR, 2021 | | 2021 |
| Space-Time Finite Elements M Davies | | 2017 |
| Deep Learning for Engineering Big Data Analytics KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar Big Data Analytics: Tools and Technology for Effective Planning, 151, 2017 | | 2017 |
| Deep Action Sequence Learning for Causal Shape Transformation K Gwn Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar arXiv e-prints, arXiv: 1605.05368, 2016 | | 2016 |
| FlowSculpt: software for efficient design of inertial flow sculpting devices Supplementary Information D Stoecklein, M Davies, J de Rutteb, CY Wub, D Di Carlo, ... | | |
Baskar GanapathysubramanianNSF/USDA AI Institute, Iowa State University確認したメール アドレス: iastate.edu
