On the limitations of analysing worst-case dynamic energy of processing

Jeremy Morse, Steven Kerrison, Kerstin Eder

Research output: Contribution to journalArticle (Academic Journal)

5 Citations (Scopus)
170 Downloads (Pure)

Abstract

This article examines dynamic energy consumption caused by data during software execution on deeply embedded microprocessors, which can be significant on some devices. In worst-case energy consumption analysis, energy models are used to find the most costly execution path. Taking each instruction’s worst-case energy produces a safe but overly pessimistic upper bound. Algorithms for safe and tight bounds would be desirable. We show that finding exact worst-case energy is NP-hard, and that tight bounds cannot be approximated with guaranteed safety. We conclude that any energy model targeting tightness must either sacrifice safety or accept overapproximation proportional to data-dependent energy.

Original languageEnglish
Article number59
Number of pages22
JournalACM Transactions on Embedded Computing Systems
Volume17
Issue number3
Early online date20 Feb 2018
DOIs
Publication statusPublished - Feb 2018

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Keywords

  • Complexity
  • Energy transparency
  • Worst case energy consumption

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