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Data type dependent energy consumption estimation

Authors: Priit Ruberg; Keijo Lass; Peeter Ellervee;

Data type dependent energy consumption estimation

Abstract

Limited energy sources in embedded systems have driven the developers to search for new ways to optimise and estimate the energy consumption. Although energy estimations are usually conducted on instruction level models we have proposed a method based on the energy consumption of a C-operation. As some energy estimation techniques are meant to be applied in the very early of the design process, our proposed method is most applicable when a substantial part of the functional program code has already been programmed. This paper will show that different data types have significant impact on the overall energy consumption on microcontrollers. We apply our energy estimation methodology on different data types using a simple image processing algorithm and show that the estimate error is less than 8% with one exception.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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