PowerQuest: Trace Driven Data Mining for Power Optimization

Pietro Babighian, Gila Kamhi, Moshe Vardi
2007 2007 Design, Automation & Test in Europe Conference & Exhibition  
We introduce 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 powerreduction problem through detection of gating conditions. PowerQuest adopts machine-learning techniques for data mining. The advantages of PowerQuest in comparison with other state-ofthe-art Dynamic Power Management (DPM) techniques are: 1) Quality of ODC conditions for gating 2) Minimization of extra logic
more » ... dded for gating. We demonstrate the validity of our approach in reducing power through experimental results using ITC99 benchmarks and real-life microprocessor test cases. We present up to 22.7 % power reduction in comparison with other DPM techniques.
doi:10.1109/date.2007.364437 fatcat:r7c23lungngohb5u5ond76suyi