🔌 Hardware and Architecture
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
- Array Programming with NumPyCharles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.2020 · 23,591 citations
- PyTorch: An Imperative Style, High-Performance Deep Learning LibraryAdam Paszke, Sam Gross, Francisco Massa, et al.2019 · 18,392 citations
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsMartín Abadi, Ashish Agarwal, Paul Barham, et al.2016 · 9,453 citations
- Billion-scale similarity search with GPUsJeff Johnson, Matthijs Douze, Hervé Jégou2017 · 4,687 citations
- Caffe: Convolutional Architecture for Fast Feature EmbeddingYangqing Jia, Evan Shelhamer, Jeff Donahue, et al.2014 · 4,064 citations
- Energy and Policy Considerations for Deep Learning in NLPEmma Strubell, Ananya Ganesh, Andrew McCallum2019 · 4,049 citations
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed SystemsTianqi Chen, Mu Li, Yutian Li, et al.2015 · 1,981 citations
- TVM: An Automated End-to-End Optimizing Compiler for Deep LearningTianqi Chen, Thierry Moreau, Ziheng Jiang, et al.2018 · 1,371 citations
- Triton: An Intermediate Language and Compiler for Tiled Neural Network ComputationsPhilippe Tillet, H. T. Kung, David Cox2019 · 728 citations
- SGLang: Efficient Execution of Structured Language Model ProgramsLianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al.2023 · 710 citations
- HybridFlow: A Flexible and Efficient RLHF FrameworkGuangming Sheng, Chi Zhang, Zilingfeng Ye, et al.2024 · 598 citations
- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language ModelShaden Smith, Mostofa Patwary, Brandon Norick, et al.2022 · 595 citations
- Parallel Implementations of Word Alignment ToolQin Gao, Stephan Vogel2008 · 566 citations
- DyNet: The Dynamic Neural Network ToolkitGraham Neubig, Chris Dyer, Yoav Goldberg, et al.2017 · 485 citations
- FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUYing Sheng, Lianmin Zheng, Binhang Yuan, et al.2023 · 399 citations
- PyTorch Distributed: Experiences on Accelerating Data Parallel TrainingShen Li, Yanli Zhao, Rohan Varma, et al.2020 · 380 citations
- A Watermark for Large Language ModelsJohn Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.2023 · 374 citations
- Green AIRoy Schwartz, Jesse Dodge, Noah A. Smith, et al.2019 · 370 citations
- Quantifying the Carbon Emissions of Machine LearningAlexandre Lacoste, Alexandra Luccioni, Victor Schmidt, et al.2019 · 324 citations
- Carbon Emissions and Large Neural Network TrainingDavid Patterson, Joseph Gonzalez, Quoc Le, et al.2021 · 304 citations
- ZeRO-Offload: Democratizing Billion-Scale Model TrainingJie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, et al.2021 · 302 citations
- KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache QuantizationColeman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.2024 · 293 citations
- From Words to Watts: Benchmarking the Energy Costs of Large Language Model InferenceSiddharth Samsi, Dan Zhao, Joseph McDonald, et al.2023 · 288 citations
- Reducing Activation Recomputation in Large Transformer ModelsVijay Korthikanti, Jared Casper, Sangkug Lym, et al.2022 · 270 citations
- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI ScaleSamyam Rajbhandari, Conglong Li, Zhewei Yao, et al.2022 · 254 citations
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