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Publication Type : Conference Proceedings
Thematic Areas : Amrita Center for Cybersecurity Systems and Networks
Publisher : ACM International Conference on Computing Frontiers (CF), ACM Digital Library, Como, Italy.
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
Campus : Amritapuri
School : Centre for Cybersecurity Systems and Networks, Department of Computer Science and Engineering
Center : Cyber Security
Department : cyber Security
Year : 2016
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.