This paper 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.
@article{arxiv.1603.02580,
title = {On the limitations of analysing worst-case dynamic energy of processing},
author = {Jeremy Morse and Steve Kerrison and Kerstin Eder},
journal= {arXiv preprint arXiv:1603.02580},
year = {2018}
}