Performance estimation of embedded software with instruction cache modeling
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Run-time power estimation in high performance microprocessors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Run-time modeling and estimation of operating system power consumption
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The Performance and Energy Consumption of Embedded Real-Time Operating Systems
IEEE Transactions on Computers
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Energy macromodeling of embedded operating systems
ACM Transactions on Embedded Computing Systems (TECS)
A Hopfield neural network approach for power optimization of real-time operating systems
Neural Computing and Applications
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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.