| 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 | 1351 | 2023 |
| Glam: Efficient scaling of language models with mixture-of-experts N Du, Y Huang, AM Dai, S Tong, D Lepikhin, Y Xu, M Krikun, Y Zhou, ... International Conference on Machine Learning, 5547-5569, 2022 | 462 | 2022 |
| A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms YE Wang, GY Wei, D Brooks the 3rd MLSys Conference, 2020 | 68* | 2020 |
| Predicting New Workload or CPU Performance by Analyzing Public Datasets YE Wang, V Lee, GY Wei, D Brooks ACM Transactions on Architecture and Code Optimization 15 (4), 53, 2019 | 35 | 2019 |
| Exploiting parallelism opportunities with deep learning frameworks YE Wang, CJ Wu, X Wang, K Hazelwood, D Brooks ACM Transactions on Architecture and Code Optimization (TACO) 18 (1), 1-23, 2020 | 32 | 2020 |
| A flexible approach to autotuning multi-pass machine learning compilers PM Phothilimthana, A Sabne, N Sarda, KS Murthy, Y Zhou, ... 2021 30th International Conference on Parallel Architectures and Compilation …, 2021 | 27 | 2021 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 18 | 2024 |
| Exploring the limits of Concurrency in ML Training on Google TPUs S Kumar, Y Wang, C Young, J Bradbury, N Kumar, D Chen, A Swing Proceedings of Machine Learning and Systems 3, 81-92, 2021 | 18 | 2021 |
| AutoDistill: an end-to-end framework to explore and distill hardware-efficient language models X Zhang, Z Zhou, D Chen, YE Wang arXiv preprint arXiv:2201.08539, 2022 | 10 | 2022 |
| Demystifying Bayesian Inference Workloads YE Wang, Y Zhu, GG Ko, B Reagen, GY Wei, D Brooks IEEE International Symposium on Performance Analysis of Systems and Software, 2019 | 10 | 2019 |
| A Learning Algorithm for Bayesian Networks and Its Efficient Implementation on GPUs Y Wang, W Qian, S Zhang, X Liang, B Yuan IEEE Transactions on Parallel and Distributed Systems 27 (1), 17-30, 2016 | 10 | 2016 |
| Yazhou Zu, Yuanzhong Xu, and Andy Swing. Exploring the limits of concurrency in ml training on google TPUs S Kumar, J Bradbury, C Young, YE Wang, A Levskaya, B Hechtman, ... arXiv preprint arXiv:2011.03641 1 (1), 1.2, 2020 | 8 | 2020 |
| Exploring hardware profile-guided green datacenter scheduling W Tang, Y Wang, H Liu, T Zhang, C Li, X Liang 2015 44th International Conference on Parallel Processing, 11-20, 2015 | 5 | 2015 |
| Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization CJS Schaefer, N Lambert-Shirzad, X Zhang, C Chou, T Jablin, J Li, E Guo, ... arXiv preprint arXiv:2306.04879, 2023 | 2 | 2023 |
| Mixed precision post training quantization of neural networks with sensitivity guided search CJS Schaefer, E Guo, C Stanton, X Zhang, T Jablin, N Lambert-Shirzad, ... arXiv preprint arXiv:2302.01382, 2023 | 1 | 2023 |
| Workload scheduling using queues with different priorities Y Wang, TB Jablin, CK Stanton US Patent US20240118920A1, 2024 | | 2024 |
| Deploying optimization profiles for compiling computer programs in data centers Y Wang, D Chen, PM Phothilimthana US Patent US20240118875A1, 2024 | | 2024 |
| Caching compilation outputs using optimization profiles H Kim, X Yu, Y Wang, PM PHOTHILIMTHANA US Patent WO2023234952A1, 2023 | | 2023 |
| Hadamard Domain Training with Integers for Class Incremental Quantized Learning M Schiemer, CJS Schaefer, JP Vap, MJ Horeni, YE Wang, J Ye, S Joshi arXiv preprint arXiv:2310.03675, 2023 | | 2023 |
| Sparsely Activated Language Models are Efficient In-Context Learners A Yu, A Dai, C Cui, DD Lepikhin, E Wang, K Meier-Hellstern, K Webster, ... | | 2022 |
David BrooksHaley Family Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
