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Domain-specific optimizing compilers have demonstrated significant performance and portability benefits, but require programs to be represented in their specialized IRs.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Microsoft COCO: common objects in context
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Siu Kwan Lam, Antoine Pitrou, and Stanley Seibert · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Chris Leary and Todd Wang · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Glow: Graph lowering compiler techniques for neural networks
Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, James Hegeman, Roman Levenstein, Bert Maher, Nadathur Satish, Jakob Olesen, Jongsoo Park, Artem Rakhov, and Misha Smelyanskiy · 2018
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TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning
Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, et al · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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PyTorch: An imperative style, high-performance deep learning library
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TVM: end-to-end optimization stack for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Q. Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy · 2018
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Automatic full compilation of Julia programs and ml models to Cloud TPUs
Keno Fischer and Elliot Saba · 2018
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Compiling machine learning programs via high-level tracing
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Taichi: An open-source computer graphics library
Yuanming Hu · 2018
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Unsupervised word discovery with segmental neural language models
Kazuya Kawakami, Chris Dyer, and Phil Blunsom · 2018
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Autograph: Imperative-style coding with graph-based performance
Dan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, and Alexander B. Wiltschko · 2018
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TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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Chainer: A deep learning framework for accelerating the research cycle, 2019
Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, and Hiroyuki Yamazaki Vincent · 2019
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JAX: composable transformations of Python + NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2020
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Array programming with NumPy
Charles R Harris, K Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al · 2020
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A domain-specific supercomputer for training deep neural networks
Norman Jouppi, Doe Yoon, George Kurian, Sheng Li, Nishant Patil, James Laudon, Cliff Young, and David Patterson · 2020
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HuggingFace’s Transformers: State-of-the-art natural language processing, 2020
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Swift for TensorFlow: A portable, flexible platform for deep learning
Brennan Saeta, Denys Shabalin, Marc Rasi, Brad Larson, Xihui Wu, Parker Schuh, Michelle Casbon, Daniel Zheng, Saleem Abdulrasool, Aleksandr Efremov, Dave Abrahams, Chris Lattner, and Richard Wei · 2021
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