--- title: "Who cites In-Datacenter Performance — 426 citing papers" description: "426 papers of the Elamur Research library cite «In-Datacenter Performance Analysis of a Tensor Processing Unit», found in their…" canonical: "https://research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [In-Datacenter Performance Analysis of a Tensor…](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/) / cited by # Who cites «In-Datacenter Performance Analysis of a Tensor Processing Unit» 426 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «In-Datacenter Performance Analysis of a Tensor Processing Unit»](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/) 1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 2. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 2,032 citations* 3. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning arXiv:1802.04799**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,384 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/) 4. [**Mastering chess and shogi by self-play with a general reinforcement learning algorithm***2017 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/al-2017-mastering-chess-shogi-self-play/) 5. **[PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Jason Ansel, Edward Yang, Horace He, et al.*2024 · 946 citations* 6. **[TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://md.research.elamur.ai/free/en/papers/jouppi-2023-tpu-v4-optically-reconfigurable-supercomputer/) [Proceedings - International Symposium on Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-computer-architecture-proceedings/)**Norman P. Jouppi; George Kurian; Sheng Li; Peter Ma; Rahul Nagarajan; Lifeng Nai; Nishant Patil; Suvinay Subramanian; Andy Swing; Brian Towles; Cliff Young; Xiang Zhou; Zongwei Zhou;, David Patterson*2023 · 615 citations* 7. **[Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://md.research.elamur.ai/free/en/papers/lee-2018-deterministic-non-autoregressive-neural-sequence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jason Lee, Elman Mansimov, Kyunghyun Cho*2018 · 568 citations* 8. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges arXiv:1907.05019**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 564 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/) 9. **[The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/chen-2018-best-both-worlds-combining-recent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mia Xu Chen, Orhan Firat, Ankur Bapna, et al.*2018 · 559 citations* 10. **[SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning](https://md.research.elamur.ai/free/en/papers/wang-2020-spatten-efficient-sparse-attention-architecture/) [Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-high-performance-computer-architecture/)**Hanrui Wang, Zhekai Zhang, Song Han*2020 · 534 citations* 11. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision arXiv:2108.10904**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/) 12. [**Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions arXiv:1802.04730**Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, et al.*2018 · 323 citations*](https://md.research.elamur.ai/free/en/papers/vasilache-2018-tensor-comprehensions-framework-agnostic-high/) 13. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) 14. [**Multimodal Few-Shot Learning with Frozen Language Models arXiv:2106.13884**Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, et al.*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/tsimpoukelli-2021-multimodal-few-shot-learning-frozen/) 15. **[Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://md.research.elamur.ai/free/en/papers/byrne-2019-taskmaster-1-realistic-diverse-dialog/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, et al.*2019 · 247 citations* 16. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, et al.*2020 · 153 citations* 17. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/) 409 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/) — In-Datacenter Performance Analysis of a Tensor Processing Unit