Data dependent energy modeling for worst case energy consumption analysis

James Pallister, Steve Kerrison, Jeremy Morse, Kerstin I Eder

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)
249 Downloads (Pure)

Abstract

Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate the impact of instruction operand values upon energy consumption in cacheless embedded processors. Existing instruction-level energy models typically use measurements from random input data, providing estimates unsuitable for safe WCEC analysis.

We examine probabilistic energy distributions of instructions and propose a model for composing instruction sequences using distributions, enabling WCEC analysis on program basic blocks. The worst case is predicted with statistical analysis. Further, we verify that the energy of embedded benchmarks can be characterised as a distribution, and compare our proposed technique with other methods of estimating energy consumption.
Original languageEnglish
Title of host publicationSCOPES '17
Subtitle of host publicationProceedings of the 20th International Workshop on Software and Compilers for Embedded Systems
EditorsSander Stuijk
PublisherAssociation for Computing Machinery (ACM)
Pages51-59
Number of pages9
ISBN (Electronic)9781450350396
DOIs
Publication statusPublished - 12 Jun 2017

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