Name
Title | ||
|---|---|---|
Overcoming the power wall by exploiting inexactness and emerging COTS architectural features: Trading precision for improving application quality |
Abstract | ||
|---|---|---|
Energy and power consumption are major limitations to continued scaling of computing systems. Inexactness where the quality of the solution can be traded for energy savings has been proposed as a counterintuitive approach to overcoming those limitation. However, in the past, inexactness has been necessitated the need for highly customized or specialized hardware. In order to move away from customization, in earlier work [1], it was shown that by interpreting precision in the computation to be the parameter to trade to achieve inexactness, weather prediction and page rank could both benefit in terms of yielding energy savings through reduced precision, while preserving the quality of the application. However, this required representations of numbers that were not readily available on commercial off-the-shelf (COTS) processors. In this paper, we provide opportunities for extending the notion of trading precision for energy savings into the world COTS. We provide a model and analyze the opportunities and behavior of all three IEEE compliant precision values available on COTS processors: (i) double (ii) single, and (iii) half. Through measurements, we show through a limit study energy savings in going from double precision to half precision are a factor of 3.98. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SOCC.2016.7905477 | 2016 29th IEEE International System-on-Chip Conference (SOCC) |
Keywords | Field | DocType |
IEEE compliant precision values,commercial off-the-shelf processors,quality preservation,precision reduction,page rank,weather prediction,inexactness,energy savings,computing system scaling,power consumption,energy consumption,COTS architectural features | Page rank,Weather prediction,Double-precision floating-point format,Engineering,Reliability engineering,Computing systems,Embedded system,Power consumption,Personalization,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5090-1368-5 | 2 | 0.39 |
References | Authors | |
10 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mike Fagan | 1 | 2 | 0.39 |
| Jeremy Schlachter | 2 | 73 | 7.24 |
| Kazutomo Yoshii | 3 | 249 | 18.53 |
| Sven Leyffer | 4 | 1437 | 121.55 |
| Krishna Palem | 5 | 1147 | 99.85 |
| M. Snir | 6 | 3984 | 520.82 |
| Stefan M. Wild | 7 | 481 | 31.93 |
| Christian C. Enz | 8 | 458 | 89.62 |