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A Design-Time Method for Building Cost-Effective Run-Time Power Monitoring.

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Title: A Design-Time Method for Building Cost-Effective Run-Time Power Monitoring.
Authors: Najem, Mohamad1, Benoit, Pascal1, El Ahmad, Mohamad1, Sassatelli, Gilles1, Torres, Lionel1
Source: IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems. Jul2017, Vol. 36 Issue 7, p1153-1166. 14p.
Subjects: Power (Mechanics), Power transmission, Industrial efficiency, Decision making, Data mining
Abstract: The emergence of power as a first-class design constraint has fueled the proposal of a growing number of optimization techniques, seeking the best tradeoff to reach the maximum energy efficiency. Effective adaptation strategies depend critically on the monitoring method as an incorrect assessment of the system’s state will result in poor decision making. Yet it is indeed a fundamental issue: how to get a precise estimation of the system’s state, and especially in a cost-effective way? We address this question for the self-observation of the power consumption. We develop a method that combines several data mining algorithms to monitor the toggling activity on a few relevant signals selected at the register transfer-level. Our approach is based on a generic flow that is able to produce a power model for any register transfer level (RTL) circuit on any technology. This contribution is evaluated on a system on chip RTL model implemented on an field-programmable gate array technology. The experiments demonstrate that the proposed method achieves the accuracy of analog power sensors (error lower than 1%) at a finer granularity and in a cost-effective way. [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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PubType: Academic Journal
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  Data: The emergence of power as a first-class design constraint has fueled the proposal of a growing number of optimization techniques, seeking the best tradeoff to reach the maximum energy efficiency. Effective adaptation strategies depend critically on the monitoring method as an incorrect assessment of the system’s state will result in poor decision making. Yet it is indeed a fundamental issue: how to get a precise estimation of the system’s state, and especially in a cost-effective way? We address this question for the self-observation of the power consumption. We develop a method that combines several data mining algorithms to monitor the toggling activity on a few relevant signals selected at the register transfer-level. Our approach is based on a generic flow that is able to produce a power model for any register transfer level (RTL) circuit on any technology. This contribution is evaluated on a system on chip RTL model implemented on an field-programmable gate array technology. The experiments demonstrate that the proposed method achieves the accuracy of analog power sensors (error lower than 1%) at a finer granularity and in a cost-effective way. [ABSTRACT FROM AUTHOR]
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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.2016.2614347
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        Text: English
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        PageCount: 14
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      – SubjectFull: Power (Mechanics)
        Type: general
      – SubjectFull: Power transmission
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      – SubjectFull: Industrial efficiency
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      – SubjectFull: Decision making
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      – SubjectFull: Data mining
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              Text: Jul2017
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