Name
Abstract | ||
|---|---|---|
This paper proposes a novel dynamic thermal management approach named Adaptive Periodic Thermal Management (APTM) for multi-core pipelined processors with hard real-time constraints. During online execution, the approach optimizes the peak temperature of the processor by dynamically calculating and deploying new adaptive periodic thermal management schemes, which periodically switch the stages to sleep state until next adapting. At each such instant of adapting, new APTM schemes which optimize the peak temperature under real-time constraints are calculated according to the extended pay-burst-only-once principle. To reduce the overhead of online computation, our approach utilizes thermal properties obtained from offline experiments in searching the ATPM scheme minimizing the temperature. We evaluate our APTM with simulation on the Intel SCC chip as well as real experiments on an Intel I7 processor. Compared to existing approaches, our approach reduces the peak temperature by up to 8 K for the Intel I7 processor, and 7.5 K for the Intel SCC using four to sixteen processor cores. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2935339 | IEEE ACCESS |
Keywords | DocType | Volume |
Pay-burst-only-once, real-time systems, temperature optimization, thermal management | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Cheng | 1 | 4 | 3.12 |
| Kai Huang | 2 | 468 | 45.69 |
| Gang Chen | 3 | 134 | 15.54 |
| Zhenshan Bing | 4 | 17 | 5.51 |
| Alois Knoll Knoll | 5 | 1700 | 271.32 |