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TensorFlow XLA. https:\/\/www.tensorflow.org\/xla."},{"key":"e_1_3_2_1_6_1","unstructured":"Cited February 2021. XLA slice op. https:\/\/www.tensorflow.org\/xla\/operation_semantics#slice. Cited February 2021. XLA slice op. https:\/\/www.tensorflow.org\/xla\/operation_semantics#slice."},{"key":"e_1_3_2_1_7_1","unstructured":"January 2020. DISC RFC. https:\/\/groups.google.com\/a\/tensorflow.org\/g\/mlir\/c\/_X48poNcbDI\/m\/jCC8BWIICQAJ. January 2020. DISC RFC. https:\/\/groups.google.com\/a\/tensorflow.org\/g\/mlir\/c\/_X48poNcbDI\/m\/jCC8BWIICQAJ."},{"key":"e_1_3_2_1_8_1","volume-title":"Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283.","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3314872.3314896"},{"key":"e_1_3_2_1_10_1","volume-title":"Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen , Mu Li , Yutian Li , Min Lin , Naiyan Wang , Minjie Wang , Tianjun Xiao , Bing Xu , Chiyuan Zhang , and Zheng Zhang . 2015 . Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015). Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)."},{"key":"e_1_3_2_1_11_1","unstructured":"Tianqi Chen Thierry Moreau Ziheng Jiang Lianmin Zheng Eddie Yan Haichen Shen Meghan Cowan Leyuan Wang Yuwei Hu Luis Ceze etal 2018. {TVM}: An automated end-to-end optimizing compiler for deep learning. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18). 578--594. Tianqi Chen Thierry Moreau Ziheng Jiang Lianmin Zheng Eddie Yan Haichen Shen Meghan Cowan Leyuan Wang Yuwei Hu Luis Ceze et al. 2018. {TVM}: An automated end-to-end optimizing compiler for deep learning. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18). 578--594."},{"key":"e_1_3_2_1_12_1","volume-title":"MLIR: A compiler infrastructure for the end of Moore's law. arXiv preprint arXiv:2002.11054","author":"Lattner Chris","year":"2020","unstructured":"Chris Lattner , Jacques Pienaar , Mehdi Amini , Uday Bondhugula , River Riddle , Albert Cohen , Tatiana Shpeisman , Andy Davis , Nicolas Vasilache , and Oleksandr Zinenko . 2020 . MLIR: A compiler infrastructure for the end of Moore's law. arXiv preprint arXiv:2002.11054 (2020). Chris Lattner, Jacques Pienaar, Mehdi Amini, Uday Bondhugula, River Riddle, Albert Cohen, Tatiana Shpeisman, Andy Davis, Nicolas Vasilache, and Oleksandr Zinenko. 2020. MLIR: A compiler infrastructure for the end of Moore's law. arXiv preprint arXiv:2002.11054 (2020)."},{"key":"e_1_3_2_1_13_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019). Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)."},{"key":"e_1_3_2_1_14_1","unstructured":"Jonathan Raiman. [n.d.]. Dali: Lazy Compilation of Dynamic Computation Graphs. ([n. d.]). Jonathan Raiman. [n.d.]. Dali: Lazy Compilation of Dynamic Computation Graphs. ([n. d.])."},{"key":"e_1_3_2_1_15_1","volume-title":"Nimble: Efficiently compiling dynamic neural networks for model inference. arXiv preprint arXiv:2006.03031","author":"Shen Haichen","year":"2020","unstructured":"Haichen Shen , Jared Roesch , Zhi Chen , Wei Chen , Yong Wu , Mu Li , Vin Sharma , Zachary Tatlock , and Yida Wang . 2020 . Nimble: Efficiently compiling dynamic neural networks for model inference. arXiv preprint arXiv:2006.03031 (2020). Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, and Yida Wang. 2020. Nimble: Efficiently compiling dynamic neural networks for model inference. arXiv preprint arXiv:2006.03031 (2020)."},{"key":"e_1_3_2_1_16_1","unstructured":"Han Vanholder. 2016. Efficient inference with tensorrt. Han Vanholder. 2016. Efficient inference with tensorrt."},{"key":"e_1_3_2_1_17_1","volume-title":"Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv:1802.04730","author":"Vasilache Nicolas","year":"2018","unstructured":"Nicolas Vasilache , Oleksandr Zinenko , Theodoros Theodoridis , Priya Goyal , Zachary DeVito , William S Moses , Sven Verdoolaege , Andrew Adams , and Albert Cohen . 2018. Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv:1802.04730 ( 2018 ). Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S Moses, Sven Verdoolaege, Andrew Adams, and Albert Cohen. 2018. Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv:1802.04730 (2018)."},{"key":"e_1_3_2_1_18_1","volume-title":"Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et al.","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng , Chengfan Jia , Minmin Sun , Zhao Wu , Cody Hao Yu , Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et al. 2020 . Ansor : Generating high-performance tensor programs for deep learning. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ( {OSDI} 20). 863--879. Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et al. 2020. Ansor: Generating high-performance tensor programs for deep learning. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20). 863--879."},{"key":"e_1_3_2_1_19_1","volume-title":"Fusion-stitching: boosting memory intensive computations for deep learning workloads. arXiv preprint arXiv:2009.10924","author":"Zheng Zhen","year":"2020","unstructured":"Zhen Zheng , Pengzhan Zhao , Guoping Long , Feiwen Zhu , Kai Zhu , Wenyi Zhao , Lansong Diao , Jun Yang , and Wei Lin . 2020. Fusion-stitching: boosting memory intensive computations for deep learning workloads. arXiv preprint arXiv:2009.10924 ( 2020 ). Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, and Wei Lin. 2020. 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