Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Power model validation through thermal measurements
Proceedings of the 34th annual international symposium on Computer architecture
Many-core design from a thermal perspective
Proceedings of the 45th annual Design Automation Conference
Characterizing processor thermal behavior
Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems
Post-silicon power characterization using thermal infrared emissions
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Hotspot: acompact thermal modeling methodology for early-stage VLSI design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Improved post-silicon power modeling using AC lock-in techniques
Proceedings of the 48th Design Automation Conference
| Hi-index | 0.00 |
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.