@inproceedings{e5ae7b028f8d4c1aa68acfbcde88dbdd,
title = "Phase-aware optimization in approximate computing",
abstract = "This paper shows that many applications exhibit execution-phase-specific sensitivity towards approximation of the internal subcomputations. Therefore, approximation in certain phases can be more beneficial than others. Further, this paper presents Opprox, a novel system for application's execution-phase-aware approximation. For a user provided error budget and target input parameters, Opprox identifies different program phases and searches for profitable approximation settings for each phase of the application execution. Our evaluation with five benchmarks and four existing transformations show that our phase-aware optimization on average does 14% less work for a 5% error tolerance bound and 42% less work for a 20% tolerance bound.",
keywords = "Approximate Computing, Execution Phases",
author = "Subrata Mitra and Gupta, {Manish K.} and Sasa Misailovic and Saurabh Bagchi",
note = "Funding Information: This work is supported by National Science Foundation Grants (CCF-1629431, CNS-1527262 and CNS-1513197). Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Symposium on Code Generation and Optimization, CGO 2017 ; Conference date: 04-02-2017 Through 08-02-2017",
year = "2017",
month = feb,
day = "23",
doi = "10.1109/CGO.2017.7863739",
language = "English (US)",
series = "CGO 2017 - Proceedings of the 2017 International Symposium on Code Generation and Optimization",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "185--196",
editor = "Reddi, {Vijay Janapa} and Aaron Smith and Lingjia Tang",
booktitle = "CGO 2017 - Proceedings of the 2017 International Symposium on Code Generation and Optimization",
address = "United States",
}