Accurate and stable run-time power modeling for mobile and embedded CPUs


Walker, Matthew, Diestelhorst, Stephan, Hansson, Andreas, Das, Anup, Yang, Sheng, Al-Hashimi, Bashir M. and Merrett, Geoff V. (2016) Accurate and stable run-time power modeling for mobile and embedded CPUs IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-14. (doi:10.5258/SOTON/393673).

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Description/Abstract

Modern mobile and embedded devices are required to be increasingly energy-efficient while running more sophisticated tasks, causing the CPU design to become more complex and employ more energy-saving techniques. This has created a greater need for fast and accurate power estimation frameworks for both run-time CPU energy management and design-space exploration. We present a statistically rigorous and novel methodology for building accurate run-time power models using Performance Monitoring Counters (PMCs) for mobile and embedded devices, and demonstrate how our models make more efficient use of limited training data and better adapt to unseen scenarios by uniquely considering stability. Our robust model formulation reduces multicollinearity, allows separation of static and dynamic power, and allows a 100× reduction in experiment time while sacrificing only 0.6% accuracy. We present a statistically detailed evaluation of our model, highlighting and addressing the problem of heteroscedasticity in power modeling. We present software implementing our methodology and build power models for ARM Cortex-A7 and Cortex-A15 CPUs, with 3.8% and 2.8% average error, respectively. We model the behavior of the non- ideal CPU voltage regulator under dynamic CPU activity to improve modeling accuracy by up to 5.5% in situations where the voltage cannot be measured. To address the lack of research utilizing PMC data from real mobile devices, we also present our data acquisition method and experimental platform software. We support this work with online resources including software tools, documentation, raw data and further results.

Item Type: Article
Digital Object Identifier (DOI): doi:10.5258/SOTON/393673
Related URLs:
Keywords: power modeling and estimation, embedded systems, performance monitoring counters, PMC event selection
Organisations: Electronic & Software Systems
ePrint ID: 393728
Date :
Date Event
19 April 2016Accepted/In Press
Date Deposited: 03 May 2016 08:26
Last Modified: 17 Apr 2017 03:17
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/393728

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