Chisel: Reliability- and Accuracy-Aware Optimization of Approximate Computational Kernels
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Rinard_Chisel Reliability.pdf
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1.78 MB
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Author(s)
Misailovic, Sasa
Achour, Sara
Qi, Zichao
Rinard, Martin C.
Carbin, Michael James
Date Issued
October 2014
Journal
Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Sasa Misailovic, Michael Carbin, Sara Achour, Zichao Qi, and Martin C. Rinard. 2014. Chisel: reliability- and accuracy-aware optimization of approximate computational kernels. In Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA '14). ACM, New York, NY, USA, 309-328.
Version
Author's final manuscript
Abstract
The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy.
We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification.
We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2660193.2660231
