High-level energy macromodeling of embedded software

This paper presents an efficient and accurate high-level software energy estimation methodology using the concept of characterization-based macromodeling. In characterization-based macromodeling, a function or subroutine is characterized using an accurate lower level energy model of the target processor to construct a macromodel that relates the energy consumed in the function under consideration to various parameters that can be easily observed or calculated from a high-level programming language description. The constructed macromodels eliminate the need for significantly slower instruction-level interpretation or hardware simulation that is required in conventional approaches to software energy estimation. The authors present two different approaches to macromodeling for embedded software that offer distinct efficiency-accuracy characteristics: 1) complexity-based macromodeling, where the variables that determine the algorithmic complexity of the function under consideration are used as macromodeling parameters and 2) profiling-based macromodeling, where internal profiling statistics for the functions are used as the parameters in the energy macromodels. The authors have experimentally validated their software energy macromodeling techniques on a wide range of embedded software routines and two different target processor architectures. The experiments demonstrate that high-level macromodels constructed using the proposed techniques are able to estimate the energy consumption to within 94% accuracy on the average, while commanding speedups of one to five orders-of-magnitude over current instruction-level and architectural energy estimation techniques.