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LCTES 2019
Sat 22 - Fri 28 June 2019 Phoenix, Arizona, United States
co-located with PLDI 2019
Sun 23 Jun 2019 15:15 - 15:30 at 105A - Session 3: Applications Chair(s): Wanli Chang

Approximate computing, a technique that forgoes quantifiable output accuracy in favor of performance gains, is useful in improving the energy efficiency of error-resilient software, especially in the embedded setting. The identification of program components that can tolerate error plays a crucial role in balancing the energy vs. accuracy trade off in approximate computing. Manual analysis for approximability is not scalable and therefore automated tools which employ static or dynamic analysis have been proposed. However, static techniques are often coarse in their approximations while dynamic efforts incur high overhead. In this work we present ApproxSymate, a framework for automatically identifying program approximations using symbolic execution. ApproxSymate first statically computes symbolic error expressions for program components, and then uses a dynamic sensitivity analysis to compute their approximability. A unique feature of this tool is that it explores the previously not considered dimension of program path for approximation which enables safer transformations. Our evaluation shows that ApproxSymate averages about 96% accuracy in identifying the same approximations found in manually annotated benchmarks, outperforming existing automated techniques.

Sun 23 Jun

14:45 - 15:30: LCTES 2019 - Session 3: Applications at 105A
Chair(s): Wanli ChangUniversity of York
LCTES-2019-papers14:45 - 15:00
Luke HsiaoStanford University, Sen WuStanford University, Nicholas ChiangGunn High School, Christopher RĂ©, Philip LevisStanford University
LCTES-2019-papers15:00 - 15:15
Xinyi LiChang'an University, Lei ZhangNorth Carolina State University, Xipeng ShenNorth Carolina State University
LCTES-2019-papers15:15 - 15:30
Himeshi Praveeni De Silva, Andrew SantosaNational University of Singapore, Nhut Minh HoNational University of Singapore, Weng-Fai WongNational University of Singapore