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

Jeremy Morse, Steven Kerrison, Kerstin Eder

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
230 Downloads (Pure)


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
Issue number3
Early online date20 Feb 2018
Publication statusPublished - Feb 2018


  • Complexity
  • Energy transparency
  • Worst case energy consumption

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