Novel modeling techniques for RTL power estimation

Michael Eiermann; Walter Stechele

doi:10.1109/lpe.2002.146763

Novel modeling techniques for RTL power estimation

Michael Eiermann, Walter Stechele

Chair of Integrated Systems

Technical University of Munich

Research output: Contribution to conference › Paper › peer-review

8 Scopus citations

Abstract

In this work, we propose efficient macromodeling techniques for RTL power estimation, based only on word and bit level switching information of the module inputs. We present practicable combinations of these two properties for the construction of power macromodels. It is demonstrated, that our developed models reduce the estimation error compared to the Hamming-distance model at least by 64%. The total average errors (compared to PowerMill) achieved over a wide range of test modules and input stimuli are less than 4.6%. This is comparable to complex models, which however, have to make use of several more signal properties.

Original language	English
Pages	323-328
Number of pages	6
DOIs	https://doi.org/10.1109/lpe.2002.146763
State	Published - 2002
Event	Proceedings of the 2002 International Symposium on Low Power Electronics and Design - Monterey, CA, United States Duration: 12 Aug 2002 → 14 Aug 2002

Conference

Conference	Proceedings of the 2002 International Symposium on Low Power Electronics and Design
Country/Territory	United States
City	Monterey, CA
Period	12/08/02 → 14/08/02

Keywords

Low power
Power estimation
Power modeling
RTL macromodels

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10.1109/lpe.2002.146763

Cite this

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AU - Eiermann, Michael

AU - Stechele, Walter

PY - 2002

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AB - In this work, we propose efficient macromodeling techniques for RTL power estimation, based only on word and bit level switching information of the module inputs. We present practicable combinations of these two properties for the construction of power macromodels. It is demonstrated, that our developed models reduce the estimation error compared to the Hamming-distance model at least by 64%. The total average errors (compared to PowerMill) achieved over a wide range of test modules and input stimuli are less than 4.6%. This is comparable to complex models, which however, have to make use of several more signal properties.

KW - Low power

KW - Power estimation

KW - Power modeling

KW - RTL macromodels

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DO - 10.1109/lpe.2002.146763

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T2 - Proceedings of the 2002 International Symposium on Low Power Electronics and Design

Y2 - 12 August 2002 through 14 August 2002

ER -

Novel modeling techniques for RTL power estimation

Abstract

Conference

Keywords

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