Name
Abstract | ||
|---|---|---|
Dynamic Thermal and Power Management methods require efficient monitoring techniques. Based on a set of data collected by sensors, embedded models estimate online the power consumption: this task is a real challenge, since models must be both accurate and low cost, but they also have to be robust to variations. In this paper, we investigate a self-aware approach using the performance events and the external temperature. We present a solution (PESel) for the selection of the relevant information. This method is two times faster than existing solutions and provides better results compared to related works. The power models achieve a 96% accuracy with a temporal resolution of 100 ms, and negligible performance/energy overheads (less than 1%). Moreover, we show that our estimations are not sensitive to external temperature variations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NORCHIP.2015.7364364 | 2015 Nordic Circuits and Systems Conference (NORCAS): NORCHIP & International Symposium on System-on-Chip (SoC) |
Keywords | Field | DocType |
adaptive power monitoring,self-aware embedded systems,dynamic thermal and power management methods | Particle detector,Power management,Adaptive system,Computer science,Real-time computing,Power demand,Self aware,Temporal resolution,Overhead (business),Embedded system,Power consumption | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| mohamad el ahmad | 1 | 0 | 0.34 |
| Najem, M. | 2 | 12 | 3.73 |
| P. Benoit | 3 | 74 | 12.39 |
| Gilles Sassatelli | 4 | 583 | 83.50 |
| Lionel Torres | 5 | 346 | 53.92 |