Worst-Case Energy Consumption Analysis for Energy-Constrained Embedded Systems

Abstract

Abstract-The fact that energy is a scarce resource in many embedded real-time systems creates the need for energy-aware task schedulers, which not only guarantee timing constraints but also consider energy consumption. Unfortunately, existing approaches to analyze the worst-case execution time (WCET) of a task usually cannot be directly applied to determine its worstcase energy consumption (WCEC) due to execution time and energy consumption not being closely correlated on many stateof-the-art processors. Instead, a WCEC analyzer must take into account the particular energy characteristics of a target platform.In this paper, we present 0g, a comprehensive approach to WCEC analysis that combines different techniques to speed up the analysis and to improve results. If detailed knowledge about the energy costs of instructions on the target platform is available, our tool is able to compute upper bounds for the WCEC by statically analyzing the program code. Otherwise, a novel approach allows 0g to determine the WCEC by measurement after having identified a set of suitable program inputs based on an auxiliary energy model, which specifies the energy consumption of instructions in relation to each other. Our experiments for three target platforms show that 0g provides precise WCEC estimates.

Thinking small: next-generation sensor networks close the size gap in vertebrate biologging

“…A crucial aspect for biologging is knowledge on the runtime of the sensor nodes. Static program-code analysis methods of the mobile node are able to determine upper bounds on the nodes’ runtime 48 . However, in the context of the BATS tracking system, precise estimates for the average uptimes of the system proved to be more beneficial for the empirical studies than upper bounds for the lifetime.…”

Energy-Aware Software Engineering

“…However, this approach has been shown to lead to high over-approximation [22] in worst-case energy consumption static analysis, so this may not be a suitable option. In fact, the estimations based on such models may never be reachable in practice.…”

On the Limitations of Analyzing Worst-Case Dynamic Energy of Processing

“…Dynamic power, however, has received much less attention. Several models of how systems consume energy have characterised the dynamic power only for specific inputs, averaged over all inputs, assumed the upper bound of dynamic power for each instruction [15,41] or assumed no dynamic power at all [11].In this paper we demonstrate that for the proportion of dynamic energy that is due to switching caused by operand values, the calculation of the worst-case input to a software execution is an NP-hard problem, and further, that this quantity cannot be approximated to a useful factor. Our proof applies to processors in general, but in practice this proportion of data dependent energy may be small.…”

“…Dynamic power, however, has received much less attention. Several models of how systems consume energy have characterised the dynamic power only for specific inputs, averaged over all inputs, assumed the upper bound of dynamic power for each instruction [15,41] or assumed no dynamic power at all [11].…”