Title :
PowerQuest: Trace Driven Data Mining for Power Optimization
Author :
Babighian, Pietro ; Kamhi, Gila ; Vardi, Moshe
Author_Institution :
Intel Corp., Leixlip
Abstract :
The paper introduced a general framework, called PowerQuest, with the primary goal of extracting "interesting" dynamic invariants from a given simulation-trace database, and applying it to the power-reduction problem through detection of gating conditions. PowerQuest adopts machine-learning techniques for data mining. The advantages of PowerQuest in comparison with other state-of-the-art dynamic power management (DPM) techniques are: 1) quality of ODC conditions for gating; 2) minimization of extra logic added for gating. The validity of the approach was demonstrated in reducing power through experimental results using ITC99 benchmarks and real-life microprocessor test cases. Power reduction of up to 22.7 % was presented, in comparison with other DPM techniques
Keywords :
data mining; electronic engineering computing; logic design; power aware computing; PowerQuest; data mining; dynamic invariants; dynamic power management; gating condition detection; logic minimization; machine-learning techniques; microprocessor test cases; power optimization; power-reduction problem; simulation-trace database; Benchmark testing; Clocks; Data mining; Databases; Energy consumption; Energy management; Logic; Microprocessors; Minimization; Power system management;
Conference_Titel :
Design, Automation & Test in Europe Conference & Exhibition, 2007. DATE '07
Conference_Location :
Nice
Print_ISBN :
978-3-9810801-2-4
DOI :
10.1109/DATE.2007.364437
