Parameter Sensitivity Analysis of the Energy/Frequency Convexity Rule for Nanometer-scale Application Processors

TL;DR

This paper validates the Energy/Frequency Convexity Rule for nanometer-scale processors through theoretical analysis and extensive experiments, revealing a minimum energy point in relation to processor frequency and analyzing parameter sensitivities.

Contribution

It introduces an analytical model for the energy-frequency relationship and provides a parameter sensitivity analysis, offering guidelines for energy-efficient system design.

Findings

Energy consumption depends on processor frequency with a clear minimum.

The power system components significantly influence the energy minimum.

Parameter sensitivity analysis guides energy management strategies.

Abstract

Both theoretical and experimental evidence are presented in this work in order to validate the existence of an Energy/Frequency Convexity Rule, which relates energy consumption and microprocessor frequency for nanometer-scale microprocessors. Data gathered during several month-long experimental acquisition campaigns, supported by several independent publications, suggest that energy consumed is indeed depending on the microprocessor's clock frequency, and, more interestingly, the curve exhibits a clear minimum over the processor's frequency range. An analytical model for this behavior is presented and motivated, which fits well with the experimental data. A parameter sensitivity analysis shows how parameters affect the energy minimum in the clock frequency space. The conditions are discussed under which this convexity rule can be exploited, and when other methods are more effective, with the aim of improving the computer system's energy management efficiency. We show that the power requirements of the computer system, besides the microprocessor, and the overhead affect the location of the energy minimum the most. The sensitivity analysis of the Energy/Frequency Convexity Rule puts forward a number of simple guidelines especially for by low-power systems, such as battery-powered and embedded systems, and less likely by high-performance computer systems.