PowerQuest: Trace Driven Data Mining for Power Optimization

Author

Babighian, Pietro ; Kamhi, Gila ; Vardi, Moshe

Author_Institution

Intel Corp., Leixlip

fYear

2007

fDate

16-20 April 2007

Firstpage

1

Lastpage

6

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Design, Automation & Test in Europe Conference & Exhibition, 2007. DATE '07

Conference_Location

Nice

Print_ISBN

978-3-9810801-2-4

Type

conf

DOI

10.1109/DATE.2007.364437

Filename

4211947