Publication Type:

Conference Proceedings

Source:

ACM International Conference on Computing Frontiers (CF), ACM Digital Library, Como, Italy, p.370-375 (2016)

URL:

https://www.scopus.com/record/display.uri?eid=2-s2.0-84978485830&origin=resultslist&sort=plf-f&src=s&st1=Predictive+Modeling+based+Power+Estimation+for+Embedded+Multicore+Systems&st2=&sid=351319744A648BAF6E025864B421BDDC.wsnAw8kcdt7IPYLO0V48gA%3a20&sot=b&

Keywords:

Embedded systems, Energy Modeling, Linear regression, Multicore

Abstract:

The increasing number of cores in embedded devices results in improved performance compared to single-core systems. Further, the unique characteristics of these systems provide numerous opportunities for power management which require models for power estimation. In this work, a statistical approach that models the impact of the individual cores and memory hierarchy on overall power consumed by Chip Multiprocessors is developed using Performance Counters. In particular, we construct a per-core based power model using SPLASH2 benchmarks by leveraging concurrency for multicore systems. Our model is simple and technology independent and as a result executes faster incurring lesser overhead. Evaluation of the model shows a strong correlation between core-level activity and power consumption and that the model predicts power consumption for newer observations with minimal errors. In addition, we discuss a few applications where the model can be utilized towards estimating power consumption.

Cite this Research Publication

S. Sankaran, “Predictive Modeling based Power Estimation for Embedded Multicore Systems”, ACM International Conference on Computing Frontiers (CF). ACM Digital Library, Como, Italy, pp. 370-375, 2016.

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