Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs

TY - JOUR

T1 - Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs

AU - Walker, Matthew J.

AU - Diestelhorst, Stephan

AU - Hansson, Andreas

AU - Das, Anup K.

AU - Yang, Sheng

AU - Al-Hashimi, Bashir

AU - Merrett, Geoff V.

PY - 2017/1

Y1 - 2017/1

N2 - 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 nonideal 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 paper with online resources including software tools, documentation, raw data and further results.

AB - 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 nonideal 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 paper with online resources including software tools, documentation, raw data and further results.

KW - Embedded systems

KW - performance monitoring counters (PMCs)

KW - PMC event selection

KW - power modeling and estimation

U2 - 10.1109/TCAD.2016.2562920

DO - 10.1109/TCAD.2016.2562920

M3 - Article

AN - SCOPUS:85013001225

SN - 0278-0070

VL - 36

SP - 106

EP - 119

JO - Ieee transactions on computer-Aided design of integrated circuits and systems

JF - Ieee transactions on computer-Aided design of integrated circuits and systems

IS - 1

M1 - 7464834

ER -