Paper Info
Title
PowerField: a transient temperature-to-power technique based on Markov random field theory
Abstract
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.
Year
DOI
Venue
2012
10.1145/2228360.2228474
DAC
Keywords
Field
DocType
probable power map,markov random field theory,thermal noise,transient temperature-to-power technique,consecutive thermal image,power distribution,approximated transient thermal equation,probable power distribution,thermal simulator,transient analysis,transient temperature-to-power conversion,steady-state analysis,markov processes,steady state analysis,maximum likelihood estimation,temperature measurement,power,estimation,mathematical model,steady state,random processes,heating
Thermal,Markov process,Computer science,Markov random field,Noise (electronics),Stochastic process,Electronic engineering,Transient analysis,Steady state,Temperature measurement
Conference
ISSN
Citations 
PageRank 
0738-100X
0
0.34
References 
Authors
11
5
Name
Order
Citations
PageRank
Seungwook Paek1204.70
Seok-Hwan Moon293.76
Wongyu Shin3526.24
Jaehyeong Sim4527.63
Lee-Sup Kim570798.58