{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:40:41Z","timestamp":1771951241021,"version":"3.50.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"OOPSLA","license":[{"start":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T00:00:00Z","timestamp":1570665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"crossref","award":["HR0011-18-C-0122"],"award-info":[{"award-number":["HR0011-18-C-0122"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Program. Lang."],"published-print":{"date-parts":[[2019,10,10]]},"abstract":"<jats:p>We propose ApproxHPVM, a compiler IR and system designed to enable accuracy-aware performance and energy tuning on heterogeneous systems with multiple compute units and approximation methods. ApproxHPVM automatically translates end-to-end application-level quality metrics into accuracy requirements for individual operations. ApproxHPVM uses a hardware-agnostic accuracy-tuning phase to do this translation that provides greater portability across heterogeneous hardware platforms and enables future capabilities like accuracy-aware dynamic scheduling and design space exploration.<\/jats:p>\n          <jats:p>ApproxHPVM incorporates three main components: (a) a compiler IR with hardware-agnostic approximation metrics, (b) a hardware-agnostic accuracy-tuning phase to identify error-tolerant computations, and (c) an accuracy-aware hardware scheduler that maps error-tolerant computations to approximate hardware components. As ApproxHPVM does not incorporate any hardware-specific knowledge as part of the IR, it can serve as a portable virtual ISA that can be shipped to all kinds of hardware platforms.<\/jats:p>\n          <jats:p>We evaluate our framework on nine benchmarks from the deep learning domain and five image processing benchmarks. Our results show that our framework can offload chunks of approximable computations to special-purpose accelerators that provide significant gains in performance and energy, while staying within user-specified application-level quality metrics with high probability. Across the 14 benchmarks, we observe from 1-9x performance speedups and 1.1-11.3x energy reduction for very small reductions in accuracy.<\/jats:p>","DOI":"10.1145\/3360612","type":"journal-article","created":{"date-parts":[[2019,10,11]],"date-time":"2019-10-11T14:53:33Z","timestamp":1570805613000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["ApproxHPVM: a portable compiler IR for accuracy-aware optimizations"],"prefix":"10.1145","volume":"3","author":[{"given":"Hashim","family":"Sharif","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Prakalp","family":"Srivastava","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Muhammad","family":"Huzaifa","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Maria","family":"Kotsifakou","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Keyur","family":"Joshi","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Yasmin","family":"Sarita","sequence":"additional","affiliation":[{"name":"Cornell University, USA"}]},{"given":"Nathan","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Vikram S.","family":"Adve","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Sasa","family":"Misailovic","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Sarita","family":"Adve","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1542476.1542481"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2190025.2190056"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806596.1806620"},{"key":"e_1_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Brett Boston Adrian Sampson Dan Grossman and Luis Ceze. 2015. Probability type inference for flexible approximate programming. In OOPSLA. ACM 470\u2013487.  Brett Boston Adrian Sampson Dan Grossman and Luis Ceze. 2015. Probability type inference for flexible approximate programming. In OOPSLA. ACM 470\u2013487.","DOI":"10.1145\/2858965.2814301"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2738600.2738630"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2509136.2509546"},{"key":"e_1_2_2_8_1","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Meghan Cowan , Haichen Shen , Leyuan Wang , Yuwei Hu , Luis Ceze , Carlos Guestrin , and Arvind Krishnamurthy . 2018 . TVM: An Automated End-to-end Optimizing Compiler for Deep Learning . In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918) . USENIX Association, Berkeley, CA, USA, 579\u2013594. http:\/\/dl.acm.org\/citation.cfm?id=3291168.3291211 Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An Automated End-to-end Optimizing Compiler for Deep Learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918). USENIX Association, Berkeley, CA, USA, 579\u2013594. http:\/\/dl.acm.org\/citation.cfm?id=3291168.3291211"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2014.58"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.40"},{"key":"e_1_2_2_11_1","volume-title":"cuDNN: Efficient Primitives for Deep Learning. CoRR abs\/1410.0759","author":"Chetlur Sharan","year":"2014","unstructured":"Sharan Chetlur , Cliff Woolley , Philippe Vandermersch , Jonathan Cohen , John Tran , Bryan Catanzaro , and Evan Shelhamer . 2014. cuDNN: Efficient Primitives for Deep Learning. CoRR abs\/1410.0759 ( 2014 ). arXiv: 1410.0759 http:\/\/arxiv.org\/abs\/ 1410.0759 Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. 2014. cuDNN: Efficient Primitives for Deep Learning. CoRR abs\/1410.0759 (2014). arXiv: 1410.0759 http:\/\/arxiv.org\/abs\/ 1410.0759"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2737924.2737969"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2012.48"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.383"},{"key":"e_1_2_2_16_1","unstructured":"Dustin Franklin. 2018. NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge. NVIDIA Developer Blog. (2018). https:\/\/devblogs.nvidia.com\/jetson-tx2-delivers-twice-intelligence-edge  Dustin Franklin. 2018. NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge. NVIDIA Developer Blog. (2018). https:\/\/devblogs.nvidia.com\/jetson-tx2-delivers-twice-intelligence-edge"},{"key":"e_1_2_2_17_1","unstructured":"Yonatan Geifman. 2019. VGG16 models for CIFAR-10 and CIFAR-100 using Keras. https:\/\/github.com\/geifmany\/cifar-vgg . (2019).  Yonatan Geifman. 2019. VGG16 models for CIFAR-10 and CIFAR-100 using Keras. https:\/\/github.com\/geifmany\/cifar-vgg . (2019)."},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2694344.2694351"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2018.2867275"},{"key":"e_1_2_2_20_1","volume-title":"Deep Learning with Keras","author":"Gulli Antonio","unstructured":"Antonio Gulli and Sujit Pal . 2017. Deep Learning with Keras . Packt Publishing . Antonio Gulli and Sujit Pal. 2017. Deep Learning with Keras. Packt Publishing."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2017.8091072"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1950365.1950390"},{"key":"e_1_2_2_24_1","volume-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 ( 2017 ). arXiv: 1704.04861 http:\/\/arxiv.org\/abs\/1704.04861 Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 (2017). arXiv: 1704.04861 http:\/\/arxiv.org\/abs\/1704.04861"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628072"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750374"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178487.3178493"},{"key":"e_1_2_2_29_1","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems -","volume":"1","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey Hinton . 2012 . ImageNet Classification with Deep Convolutional Neural Networks . In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS \u201912). Curran Associates Inc., USA, 1097\u20131105. http:\/\/dl.acm.org\/citation.cfm?id=2999134.2999257 Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS \u201912). Curran Associates Inc., USA, 1097\u20131105. http:\/\/dl.acm.org\/citation.cfm?id=2999134.2999257"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/977395.977673"},{"key":"e_1_2_2_31_1","volume-title":"Proceedings of the 2nd International Conference on Neural Information Processing Systems (NIPS \u201989)","author":"LeCun Yann","unstructured":"Yann LeCun , Bernhard Boser , John S. Denker , Donnie Henderson , Richard E. Howard , Wayne Hubbard , and Lawrence D. Jackel . 1989. Handwritten Digit Recognition with a Back-propagation Network . In Proceedings of the 2nd International Conference on Neural Information Processing Systems (NIPS \u201989) . MIT Press, Cambridge, MA, USA, 396\u2013404. http: \/\/dl.acm.org\/citation.cfm?id=2969830.2969879 Yann LeCun, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and Lawrence D. Jackel. 1989. Handwritten Digit Recognition with a Back-propagation Network. In Proceedings of the 2nd International Conference on Neural Information Processing Systems (NIPS \u201989). MIT Press, Cambridge, MA, USA, 396\u2013404. http: \/\/dl.acm.org\/citation.cfm?id=2969830.2969879"},{"key":"e_1_2_2_32_1","volume-title":"Burges","author":"LeCun Yann","year":"1998","unstructured":"Yann LeCun , Corinna Cortes , and Christopher J. C . Burges . 1998 . The MNIST database of handwritten digits. (1998). http:\/\/yann.lecun.com\/exdb\/mnist Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. 1998. The MNIST database of handwritten digits. (1998). http:\/\/yann.lecun.com\/exdb\/mnist"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2007.4284808"},{"key":"e_1_2_2_34_1","volume-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning -","volume":"48","author":"Lin Darryl D.","unstructured":"Darryl D. Lin , Sachin S. Talathi , and V. Sreekanth Annapureddy . 2016. Fixed Point Quantization of Deep Convolutional Networks . In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML\u201916). JMLR.org, 2849\u20132858. http:\/\/dl.acm.org\/citation.cfm?id=3045390.3045690 Darryl D. Lin, Sachin S. Talathi, and V. Sreekanth Annapureddy. 2016. Fixed Point Quantization of Deep Convolutional Networks. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML\u201916). JMLR.org, 2849\u20132858. http:\/\/dl.acm.org\/citation.cfm?id=3045390.3045690"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.42"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2009.5160991"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2010.5470469"},{"key":"e_1_2_2_38_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Micikevicius Paulius","year":"2018","unstructured":"Paulius Micikevicius , Sharan Narang , Jonah Alben , Gregory F. Diamos , Erich Elsen , David Garc\u00eda , Boris Ginsburg , Michael Houston , Oleksii Kuchaiev , Ganesh Venkatesh , and Hao Wu. 2018. Mixed Precision Training . In 6th International Conference on Learning Representations, ICLR 2018 , Vancouver, BC , Canada, April 30 - May 3, 2018, Conference Track Proceedings . https:\/\/openreview.net\/forum?id=r1gs9JgRZ Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David Garc\u00eda, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. 2018. Mixed Precision Training. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. https:\/\/openreview.net\/forum?id=r1gs9JgRZ"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660193.2660231"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2465787.2465790"},{"key":"e_1_2_2_41_1","volume-title":"Proceedings of the 18th International Conference on Static Analysis (SAS\u201911)","author":"Misailovic Sasa","year":"2041","unstructured":"Sasa Misailovic , Daniel M. Roy , and Martin C. Rinard . 2011. Probabilistically Accurate Program Transformations . In Proceedings of the 18th International Conference on Static Analysis (SAS\u201911) . Springer-Verlag, Berlin, Heidelberg, 316\u2013333. http:\/\/dl.acm.org\/citation.cfm?id= 2041 552.2041576 Sasa Misailovic, Daniel M. Roy, and Martin C. Rinard. 2011. Probabilistically Accurate Program Transformations. In Proceedings of the 18th International Conference on Static Analysis (SAS\u201911). Springer-Verlag, Berlin, Heidelberg, 316\u2013333. http:\/\/dl.acm.org\/citation.cfm?id=2041552.2041576"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806799.1806808"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2414729.2414738"},{"key":"e_1_2_2_44_1","volume-title":"PTX: Parallel thread execution ISA version 2.3. NVIDIA COMPUTE Programmer\u2019s Manual 3","author":"NVIDIA.","year":"2010","unstructured":"NVIDIA. 2010 . PTX: Parallel thread execution ISA version 2.3. NVIDIA COMPUTE Programmer\u2019s Manual 3 (2010). http: \/\/developer.download.nvidia.com\/compute\/DevZone\/docs\/html\/C\/doc\/ptx_isa_2.3.pdf NVIDIA. 2010. PTX: Parallel thread execution ISA version 2.3. NVIDIA COMPUTE Programmer\u2019s Manual 3 (2010). http: \/\/developer.download.nvidia.com\/compute\/DevZone\/docs\/html\/C\/doc\/ptx_isa_2.3.pdf"},{"key":"e_1_2_2_45_1","unstructured":"NVIDIA. 2018. NVIDIA Jetson TX2 Developer Kit. (2018). https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embeddedsystems\/jetson-tx2  NVIDIA. 2018. NVIDIA Jetson TX2 Developer Kit. (2018). https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embeddedsystems\/jetson-tx2"},{"key":"e_1_2_2_46_1","unstructured":"NVIDIA Developer Forums. 2018. Power Monitoring on Jetson TX2. (2018). https:\/\/devtalk.nvidia.com\/default\/topic\/ 1000830\/jetson-tx2\/jetson-tx2-ina226-power-monitor-with-i2c-interface  NVIDIA Developer Forums. 2018. Power Monitoring on Jetson TX2. (2018). https:\/\/devtalk.nvidia.com\/default\/topic\/ 1000830\/jetson-tx2\/jetson-tx2-ina226-power-monitor-with-i2c-interface"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1183401.1183447"},{"key":"e_1_2_2_48_1","volume-title":"Glow: Graph Lowering Compiler Techniques for Neural Networks. CoRR abs\/1805.00907","author":"Rotem Nadav","year":"2018","unstructured":"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 . Glow: Graph Lowering Compiler Techniques for Neural Networks. CoRR abs\/1805.00907 (2018). arXiv: 1805.00907 http:\/\/arxiv.org\/abs\/1805.00907 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. Glow: Graph Lowering Compiler Techniques for Neural Networks. CoRR abs\/1805.00907 (2018). arXiv: 1805.00907 http:\/\/arxiv.org\/abs\/1805.00907"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2503296"},{"key":"e_1_2_2_50_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning -","volume":"70","author":"Sakr Charbel","year":"2017","unstructured":"Charbel Sakr , Yongjune Kim , and Naresh Shanbhag . 2017 . Analytical Guarantees on Numerical Precision of Deep Neural Networks . In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML \u201917). 3007\u20133016. http:\/\/dl.acm.org\/citation.cfm?id=3305890.3305992 Charbel Sakr, Yongjune Kim, and Naresh Shanbhag. 2017. Analytical Guarantees on Numerical Precision of Deep Neural Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML \u201917). 3007\u20133016. http:\/\/dl.acm.org\/citation.cfm?id=3305890.3305992"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2541940.2541948"},{"key":"e_1_2_2_52_1","volume-title":"ACCEPT: A Programmer-Guided Compiler Framework for Practical Approximate Computing. In U. Washington, Tech. Rep. UW-CSE-15-01-01. https:\/\/dada.cs.washington.edu\/research\/tr\/2015\/01\/UW-CSE-15-01-01.pdf","author":"Sampson Adrian","year":"2015","unstructured":"Adrian Sampson , Andre Baixo , Benjamin Ransford , Thierry Moreau , Joshua Yip , Luis Ceze , and Mark Oskin . 2015 . ACCEPT: A Programmer-Guided Compiler Framework for Practical Approximate Computing. In U. Washington, Tech. Rep. UW-CSE-15-01-01. https:\/\/dada.cs.washington.edu\/research\/tr\/2015\/01\/UW-CSE-15-01-01.pdf Adrian Sampson, Andre Baixo, Benjamin Ransford, Thierry Moreau, Joshua Yip, Luis Ceze, and Mark Oskin. 2015. ACCEPT: A Programmer-Guided Compiler Framework for Practical Approximate Computing. In U. Washington, Tech. Rep. UW-CSE-15-01-01. https:\/\/dada.cs.washington.edu\/research\/tr\/2015\/01\/UW-CSE-15-01-01.pdf"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/1993498.1993518"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/HOTCHIPS.2013.7478287"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594291.2594302"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025133"},{"key":"e_1_2_2_57_1","volume-title":"Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 ( 2014 ). arXiv: 1409.1556 http:\/\/arxiv.org\/abs\/1409.1556 Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014). arXiv: 1409.1556 http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00015"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2014.6853213"},{"key":"e_1_2_2_60_1","volume-title":"Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. CoRR abs\/1809.05859","author":"Stanley-Marbell Phillip","year":"2018","unstructured":"Phillip Stanley-Marbell , Armin Alaghi , Michael Carbin , Eva Darulova , Lara Dolecek , Andreas Gerstlauer , Ghayoor Gillani , Djordje Jevdjic , Thierry Moreau , Mattia Cacciotti , Alexandros Daglis , Natalie D. Enright Jerger , Babak Falsafi , Sasa Misailovic , Adrian Sampson , and Damien Zufferey . 2018. Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. CoRR abs\/1809.05859 ( 2018 ). arXiv: 1809.05859 http:\/\/arxiv.org\/abs\/1809.05859 Phillip Stanley-Marbell, Armin Alaghi, Michael Carbin, Eva Darulova, Lara Dolecek, Andreas Gerstlauer, Ghayoor Gillani, Djordje Jevdjic, Thierry Moreau, Mattia Cacciotti, Alexandros Daglis, Natalie D. Enright Jerger, Babak Falsafi, Sasa Misailovic, Adrian Sampson, and Damien Zufferey. 2018. Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. CoRR abs\/1809.05859 (2018). arXiv: 1809.05859 http:\/\/arxiv.org\/abs\/1809.05859"},{"key":"e_1_2_2_61_1","volume-title":"XLA: Domain-specific compiler for linear algebra that optimizes TensorFlow computations. https: \/\/github.com\/tensorflow\/tensorflow\/blob\/master\/tensorflow\/compiler\/xla\/g3doc\/overview.md .","author":"Team The XLA","year":"2019","unstructured":"The XLA Team . 2019 . XLA: Domain-specific compiler for linear algebra that optimizes TensorFlow computations. https: \/\/github.com\/tensorflow\/tensorflow\/blob\/master\/tensorflow\/compiler\/xla\/g3doc\/overview.md . (2019). The XLA Team. 2019. XLA: Domain-specific compiler for linear algebra that optimizes TensorFlow computations. https: \/\/github.com\/tensorflow\/tensorflow\/blob\/master\/tensorflow\/compiler\/xla\/g3doc\/overview.md . (2019)."},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.860338"},{"key":"e_1_2_2_63_1","volume-title":"VideoChef: Efficient Approximation for Streaming Video Processing Pipelines. In 2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Xu Ran","year":"2018","unstructured":"Ran Xu , Jinkyu Koo , Rakesh Kumar , Peter Bai , Subrata Mitra , Sasa Misailovic , and Saurabh Bagchi . 2018 . VideoChef: Efficient Approximation for Streaming Video Processing Pipelines. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) . USENIX Association, Boston, MA, 43\u201356. https:\/\/www.usenix.org\/conference\/atc18\/presentation\/xu-ran Ran Xu, Jinkyu Koo, Rakesh Kumar, Peter Bai, Subrata Mitra, Sasa Misailovic, and Saurabh Bagchi. 2018. VideoChef: Efficient Approximation for Streaming Video Processing Pipelines. In 2018 USENIX Annual Technical Conference (USENIX ATC 18). USENIX Association, Boston, MA, 43\u201356. https:\/\/www.usenix.org\/conference\/atc18\/presentation\/xu-ran"},{"key":"e_1_2_2_64_1","unstructured":"Wei Yang. 2019. Classification on CIFAR-10\/100 and ImageNet with PyTorch. https:\/\/github.com\/bearpaw\/pytorchclassification\/blob\/master\/models\/cifar\/alexnet.py . (2019).  Wei Yang. 2019. Classification on CIFAR-10\/100 and ImageNet with PyTorch. https:\/\/github.com\/bearpaw\/pytorchclassification\/blob\/master\/models\/cifar\/alexnet.py . (2019)."},{"key":"e_1_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2103656.2103710"}],"container-title":["Proceedings of the ACM on Programming Languages"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3360612","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3360612","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3360612","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:22:59Z","timestamp":1750202579000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3360612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,10]]},"references-count":64,"journal-issue":{"issue":"OOPSLA","published-print":{"date-parts":[[2019,10,10]]}},"alternative-id":["10.1145\/3360612"],"URL":"https:\/\/doi.org\/10.1145\/3360612","relation":{},"ISSN":["2475-1421"],"issn-type":[{"value":"2475-1421","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,10]]},"assertion":[{"value":"2019-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}