We have developed a function-level power estimation methodology for predicting the power dissipation of embedded software. For a given microprocessor core, we empirically build the "power data bank", which stores the power information of the built-in library functions and basic instructions. To estimate the average power of an embedded software on this core, we first decompose the machine code into library functions and user-defined functions. We then use program profiling/tracing tools to get the execution information of the target software. Next, we evaluate the total energy consumption and execution time based on the "power data bank", and their ratio is taken as the average power. High efficiency is achieved because no power simulator is used once the "power data bank" is built. We apply this method to a commercial microprocessor core and get power estimates with an average error of 3%. Using this method, microprocessor vendors can provide users the "power data bank" without releasing details of the core to help users get early power estimates and eventually guide power optimization.
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering