Compiler Analysis and Supports for Leakage Power Reduction on Microprocessors

  • Yi-Ping You
  • Chingren Lee
  • Jenq Kuen Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2481)


Power leakage constitutes an increasing fraction of the total power consumption in modern semiconductor technologies. Recent research efforts also indicate architecture, compiler, and software participations can help reduce the switching activities (also known as dynamic power) on microprocessors. This raises interests on the issues to employ architecture and compiler efforts to reduce leakage power (also known as static power) on microprocessors. In this paper, we investigate the compiler analysis techniques related to reducing leakage power. The architecture model in our design is a system with an instruction set to support the control of power gating in the component levels. Our compiler gives an analysis framework to utilize the instruction to reduce the leakage power. We present a data flow analysis framework to estimate the component activities at fixed points of programs with the consideration of pipelines of architectures. We also give the equation for the compiler to decide if the employment of the power gating instructions on given program blocks will benefit the total energy reductions. As the duration of power gating on components on given program routines is related to program branches, we propose a set of scheduling policy include Basic_Blk_Sched, MIN_Path_Sched, and AVG_Path_Sched mechanisms and evaluate the effectiveness of those schemes. Our experiment is done by incorporating our compiler analysis and scheduling policy into SUIF compiler tools [32] and by simulating the energy consumptions on Wattch toolkits [6]. Experimental results show our mechanisms are effective in reducing leakage powers on microprocessors.


Power Dissipation Schedule Policy Very Large Scale Integration Leakage Power Conditional Branch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yi-Ping You
    • 1
  • Chingren Lee
    • 1
  • Jenq Kuen Lee
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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