An Embedded Software Power Model Based on Algorithm Complexity Using Back-Propagation Neural Networks

Nowadays as low carbon economy is greatly advocated worldwide, the electricity consumption caused by a huge number of embedded computer systems is gaining more and more attention. Different instruction set, software algorithm and high-level software architecture can significantly affect the system energy consumption. In this paper, we first analyze the relations between software power consumption and some software characteristics on algorithm level. Through measuring three algorithm complexity characteristics, i.e., time complexity, space complexity and input scale, we propose an embedded software power model based on algorithm complexity. Then, we design and train a back propagation neural network to fit the power model accurately based on a sample training function set and more than 400 software power data. Simulation results show that the error between the estimation values of this model and the real measured values is below 10 percent, and this model can effectively estimate the power consumption of software in an early stage of software design.