Name
Title | ||
|---|---|---|
Improving the Efficiency of Power Management Techniques by Using Bayesian Classification |
Abstract | ||
|---|---|---|
This paper presents a supervised learning based dynamic power management (DPM) framework for a multicore processor, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the state of service queue occupancy and the task arrival rate) and then uses this predicted state to look up the optimal power management action from a pre-computed policy lookup table. The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce overhead of the PM which has to recurrently determine and issue voltage-frequency setting commands to each processor core in the system. Experimental results reveal that the proposed Bayesian classification based DPM technique ensures system-wide energy savings under rapidly and widely varying workloads. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISQED.2008.4479722 | ISQED |
Keywords | Field | DocType |
bayesian classification,belief networks,supervised learning,classification,bayesian classifier,microprocessor chips,voltage-frequency setting,learning (artificial intelligence),bayesian,dpm technique,system performance state,processor core,proposed bayesian classification,multicore processor,dynamic power management,power management techniques,logic design,optimal power management action,power management technique,power management,electronic engineering computing,power manager,multicore processors,frequency,network on a chip,system performance,power dissipation,learning artificial intelligence,energy management,bayesian methods,lookup table,voltage,cmos technology,multicore processing | Lookup table,Computer science,Real-time computing,Electronic engineering,Artificial intelligence,Multi-core processor,Power management,Energy management,Naive Bayes classifier,Network on a chip,Supervised learning,Machine learning,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-0-7695-3117-5 | 9 | 0.55 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hwisung Jung | 1 | 136 | 8.34 |
| Massoud Pedram | 2 | 7801 | 1211.32 |