PowerField: A transient temperature-to-power technique based on Markov random field theory

Seungwook Paek, Seok Hwan Moon, Wongyu Shin, Jaehyeong Sim, Lee Sup Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Transient temperature-to-power conversion is as important as steady-state analysis since power distributions tend to change dynamically. In this work, we propose PowerField framework to find the most probable power distribution from consecutive thermal images. Since the transient analysis is vulnerable to spatio-temporal thermal noise, we adopted a maximum-a-posteriori Markov random field framework to enhance the noise immunity. The most probable power map is obtained by minimizing the energy function which is calculated using an approximated transient thermal equation. Experimental results with a thermal simulator shows that PowerField outperforms the previous method in transient analysis reducing the error by half on average. We also applied our method to a real silicon achieving 90.7% accuracy.

Original languageEnglish
Title of host publicationProceedings of the 49th Annual Design Automation Conference, DAC '12
Number of pages6
StatePublished - 2012
Event49th Annual Design Automation Conference, DAC '12 - San Francisco, CA, United States
Duration: 3 Jun 20127 Jun 2012

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Conference49th Annual Design Automation Conference, DAC '12
Country/TerritoryUnited States
CitySan Francisco, CA


  • Markov random field
  • post-silicon verification
  • power
  • thermal imaging


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