In this paper, we study the interdependencies between system's leakage
and on-chip temperature. We show that the temperature variation caused by
on-chip heat accumulation has a large impact in estimating the system's
leakage energy. More importantly, we propose an online temperature-aware
leakage minimization technique to demonstrate how to incorporate the
temperature information to reduce energy consumption at real time.
The basic idea is to run when the system is cool and the workload is high
and to put the system to sleep when it is hot and the workload is light.
The online algorithm has low run-time complexity and achieves significant
leakage energy saving. In fact, we are able to get about 25% leakage
reduction on both real life and artificial benchmarks.
Comparing to our optimal offline algorithm, the above online
algorithm provides similar energy savings with similar decisions on how
to put the system to sleep and how to wake it up.
Finally, our temperature-aware leakage minimization techniques can be
combined with existing DVS methods to improve the total energy
efficiency by further saving on leakage