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

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Title: Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs.
Authors: Walker, Matthew J.1, Diestelhorst, Stephan2, Hansson, Andreas2, Das, Anup K.1, Yang, Sheng1, Al-Hashimi, Bashir M.1, Merrett, Geoff V.1
Source: IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems. Jan2017, Vol. 36 Issue 1, p106-119. 14p.
Subjects: Counters (Computer science), Central processing units, Run time systems (Computer science), Multicollinearity, Acquisition of data
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\times 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. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 120414484
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs.
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  Data: 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\times 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. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TCAD.2016.2562920
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      – SubjectFull: Central processing units
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              Text: Jan2017
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