--- title: "Hardware and Architecture — papers" description: "Hardware and Architecture: 36 papers of the Elamur Research library · page 1 of 2 · 1–25, the most cited first — our summary of each, its ideas…" canonical: "https://research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) / Hardware and Architecture # 🔌 Hardware and Architecture 36 papers · page 1 of 2 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Hardware and Architecture is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Parallel Computing and Optimization Techniques», «Energy Efficiency in Computing», «Embedded Systems Design Techniques». ## Papers **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/page/2/) 1. [**Array Programming with NumPy**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,591 citations*](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) 2. [**PyTorch: An Imperative Style, High-Performance Deep Learning Library**Adam Paszke, Sam Gross, Francisco Massa, et al.*2019 · 18,392 citations*](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/) 3. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,453 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/) 4. [**Billion-scale similarity search with GPUs**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/) 5. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,064 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/) 6. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,049 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/) 7. [**MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems**Tianqi Chen, Mu Li, Yutian Li, et al.*2015 · 1,981 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-mxnet-flexible-efficient-machine-learning/) 8. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/) 9. [**Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations**Philippe Tillet, H. T. Kung, David Cox*2019 · 728 citations*](https://md.research.elamur.ai/free/en/papers/tillet-2019-triton-intermediate-language-compiler-tiled/) 10. [**SGLang: Efficient Execution of Structured Language Model Programs**Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al.*2023 · 710 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/) 11. [**HybridFlow: A Flexible and Efficient RLHF Framework**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 598 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/) 12. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 595 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/) 13. [**Parallel Implementations of Word Alignment Tool**Qin Gao, Stephan Vogel*2008 · 566 citations*](https://md.research.elamur.ai/free/en/papers/gao-2008-parallel-implementations-word-alignment-tool/) 14. [**DyNet: The Dynamic Neural Network Toolkit**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/) 15. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 399 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/) 16. [**PyTorch Distributed: Experiences on Accelerating Data Parallel Training**Shen Li, Yanli Zhao, Rohan Varma, et al.*2020 · 380 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-pytorch-distributed-experiences-accelerating-data/) 17. [**A Watermark for Large Language Models**John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.*2023 · 374 citations*](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-watermark-large-language-models/) 18. [**Green AI**Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.*2019 · 370 citations*](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/) 19. [**Quantifying the Carbon Emissions of Machine Learning**Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, et al.*2019 · 324 citations*](https://md.research.elamur.ai/free/en/papers/lacoste-2019-quantifying-carbon-emissions-machine-learning/) 20. [**Carbon Emissions and Large Neural Network Training**David Patterson, Joseph Gonzalez, Quoc Le, et al.*2021 · 304 citations*](https://md.research.elamur.ai/free/en/papers/patterson-2021-carbon-emissions-large-neural-network/) 21. [**ZeRO-Offload: Democratizing Billion-Scale Model Training**Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, et al.*2021 · 302 citations*](https://md.research.elamur.ai/free/en/papers/ren-2021-zero-offload-democratizing-billion-scale/) 22. [**KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization**Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.*2024 · 293 citations*](https://md.research.elamur.ai/free/en/papers/hooper-2024-kvquant-towards-10-million-context/) 23. [**From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference**Siddharth Samsi, Dan Zhao, Joseph McDonald, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/samsi-2023-words-watts-benchmarking-energy-costs/) 24. [**Reducing Activation Recomputation in Large Transformer Models**Vijay Korthikanti, Jared Casper, Sangkug Lym, et al.*2022 · 270 citations*](https://md.research.elamur.ai/free/en/papers/korthikanti-2022-reducing-activation-recomputation-large-transformer/) 25. [**DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale**Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/) **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/page/2/) Updated 23 October 2026 - [← previous subfield Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) - [next subfield → Human-Computer Interaction](https://md.research.elamur.ai/free/en/shelves/computer-science/human-computer-interaction/) - [↑ back to Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/)