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 accuracyaware 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 showthat 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 original (exact) kernel implementations while preserving important reliability guarantees.
|Original language||English (US)|
|Title of host publication||Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA|
|Publisher||Association for Computing Machinery|
|Number of pages||20|
|State||Published - Oct 15 2014|
|Event||2014 ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2014 - Portland, United States|
Duration: Oct 20 2014 → Oct 24 2014
|Name||Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA|
|Other||2014 ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2014|
|Period||10/20/14 → 10/24/14|
- Approximate Computing
ASJC Scopus subject areas