Name
Abstract | ||
|---|---|---|
This paper tackles the problem of dynamic power management (DPM) in nanoscale CMOS design technologies that are typically affected by increasing levels of process and temperature variations and fluctuations due to the randomness in the behavior of silicon structure. This uncertainty undermines the accuracy and effectiveness of traditional DPM approaches. This paper presents a stochastic framework to improve the accuracy of decision making during dynamic power management, while considering manufacturing process and/or environment induced uncertainties. More precisely, variability and uncertainty at the system level are captured by a partially observable semi-Markov decision process with interval-based definition of states while the policy optimization problem is formulated as a mathematical program based on this model. Experimental results with a RISC processor in 65-nm technology demonstrate the effectiveness of the technique and show that the proposed uncertainty-aware power management technique ensures system-wide energy savings under statistical circuit parameter variations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TVLSI.2008.2009014 | IEEE Trans. VLSI Syst. |
Keywords | Field | DocType |
observable domain,risc processor,cmos integrated circuits,proposed uncertainty-aware power management,power aware computing,microprocessor chips,traditional dpm approach,size 65 nm,decision making,dynamic power management (dpm),nanoscale cmos design technology,silicon structure,uncertainty,policy optimization problem,semimarkov decision process,65-nm technology,pomdp,dynamic power management,stochastic framework,silicon,observable semi-markov decision process,environment induced uncertainty,stochastic control,si,manufacturing process,uncertainty-aware dynamic power management,markov processes,nanoelectronics,energy management,fluctuations,stochastic processes,technology management,temperature,optimization problem,mathematical programming,cmos technology | Energy management,Power management,Markov process,Computer science,Partially observable Markov decision process,Decision support system,Real-time computing,Electronic engineering,Optimization problem,Technology management,Stochastic control | Journal |
Volume | Issue | ISSN |
17 | 7 | 1063-8210 |
Citations | PageRank | References |
4 | 0.41 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hwisung Jung | 1 | 136 | 8.34 |
| Massoud Pedram | 2 | 7801 | 1211.32 |