| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 419 | 2023 |
| Deep neural network approximation for custom hardware: Where we've been, where we're going E Wang, JJ Davis, R Zhao, HC Ng, X Niu, W Luk, PYK Cheung, ... ACM Computing Surveys (CSUR) 52 (2), 1-39, 2019 | 213 | 2019 |
| Optimizing CNN-based object detection algorithms on embedded FPGA platforms R Zhao, X Niu, Y Wu, W Luk, Q Liu Applied Reconfigurable Computing: 13th International Symposium, ARC 2017 …, 2017 | 91 | 2017 |
| Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi WT Tang, R Zhao, M Lu, Y Liang, HP Huyng, X Li, RSM Goh 2015 IEEE/ACM International Symposium on Code Generation and Optimization …, 2015 | 77 | 2015 |
| Towards Efficient Convolutional Neural Network for Domain-Specific Applications on FPGA R Zhao, HC Ng, W Luk, X Niu 28th International Conference on Field Programmable Logic and Application (FPL), 2018 | 50 | 2018 |
| Polygeist: Raising C to Polyhedral MLIR WS Moses, L Chelini, R Zhao, O Zinenko PACT, 2021 | 46 | 2021 |
| Hardware acceleration for machine learning R Zhao, W Luk, X Niu, H Shi, H Wang 2017 IEEE computer society annual symposium on VLSI (ISVLSI), 645-650, 2017 | 41 | 2017 |
| Automatic Optimising CNN with Depthwise Separable Convolution on FPGA: (Abstact Only) R Zhao, X Niu, W Luk Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018 | 40 | 2018 |
| On-chip FPGA debug instrumentation for machine learning applications D Holanda Noronha, R Zhao, J Goeders, W Luk, SJE Wilton Proceedings of the 2019 ACM/SIGDA International Symposium on Field …, 2019 | 23 | 2019 |
| Efficient Structured Pruning and Architecture Searching for Group Convolution R Zhao, W Luk Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019 | 19 | 2019 |
| Hardware Compilation of Deep Neural Networks: An Overview R Zhao, S Liu, HC Ng, E Wang, JJ Davis, X Niu, X Wang, H Shi, ... 29th IEEE International Conference on Application-specific Systems …, 2018 | 17 | 2018 |
| Scale-free sparse matrix-vector multiplication on many-core architectures Y Liang, WT Tang, R Zhao, M Lu, HP Huynh, RSM Goh IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2017 | 16 | 2017 |
| POLSCA: Polyhedral High-Level Synthesis with Compiler Transformations R Zhao, J Cheng, W Luk, GA Constantinides 32nd International Conference on Field Programmable Logic and Applications, 2022 | 15 | 2022 |
| Phism: polyhedral high-level synthesis in MLIR R Zhao, J Cheng arXiv preprint arXiv:2103.15103, 2021 | 11 | 2021 |
| An overlay for rapid fpga debug of machine learning applications DH Noronha, R Zhao, Z Que, J Goeders, W Luk, S Wilton 2019 International Conference on Field-Programmable Technology (ICFPT), 135-143, 2019 | 10 | 2019 |
| Polygeist: Affine C in MLIR W Moses, L Chelini, R Zhao, O Zinenko 11th International Workshop on Polyhedral Compilation Techniques (IMPACT), 2021 | 7 | 2021 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 6 | 2024 |
| Adaptive loss scaling for mixed precision training R Zhao, B Vogel, T Ahmed arXiv preprint arXiv:1910.12385, 2019 | 6 | 2019 |
| On the challenges in programming mixed-precision deep neural networks R Zhao, W Luk, C Xiong, X Niu, KH Tsoi Proceedings of the 4th ACM SIGPLAN International Workshop on Machine …, 2020 | 5 | 2020 |
| Reducing Underflow in Mixed Precision Training by Gradient Scaling R Zhao, B Vogel, T Ahmed, W Luk Proceedings of the Twenty-Ninth International Joint Conference on Artificial …, 2020 | 5 | 2020 |
Wayne LukProfessor of Computer Engineering, Imperial College LondonVerified email at imperial.ac.uk
