This paper examines the impact of  operand values upon instruction level energy models of embedded processors, to explore whether the requirements for safe worst case energy consumption (WCEC) analysis can be met. WCEC is similar to worst case execution time (WCET) analysis, but seeks to determine whether a task can be completed within an energy budget rather than within a deadline. Existing energy models that underpin such analysis typically use energy measurements from random input data, providing average or otherwise unbounded estimates not necessarily suitable for worst case analysis. We examine energy consumption distributions of two benchmarks under a range of input data on two cache-less embedded architectures, AVR and XS1-L. We find that the worst case can be predicted with a distribution created from random data. We propose a model to obtain energy distributions for instruction sequences that can be composed , enabling WCEC analysis on program basic blocks. Data dependency between instructions is also examined, giving a case where dependencies create a bimodal energy distribution. The worst case energy prediction remains safe. We conclude that worst-case energy models based on a probabilistic approach are suitable for safe WCEC analysis .
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 .
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This paper examines
Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate
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instruction
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instruction level energy models of embedded processors, to explore whether the requirements for safe worst case energy consumption (WCEC) analysis can be met. WCEC is similar to worst case execution time (WCET) analysis, but seeks to determine whether a task can be completed within an energy budget rather than within a deadline. Existing
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models that underpin such analysis typically use energy
consumption in cacheless embedded processors. Existing instruction-level energy models typically use
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average or otherwise unbounded estimates not necessarily suitable for worst case
estimates unsuitable for safe WCEC
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energy consumption distributions of two benchmarks under a range of input data on two cache-less embedded architectures, AVR and XS1-L. We find that the worst case can be predicted with a distribution created from random data. We
probabilistic energy distributions of instructions and
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to obtain energy distributions for instruction sequences that can be composed
for composing instruction sequences using distributions
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Data dependency between instructions is also examined, giving a case where dependencies create a bimodal energy distribution.
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energy prediction remains safe. We conclude that worst-case energy models based on a probabilistic approach are suitable for safe WCEC analysis
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