An Empirical Comparison of Algorithmic, Instruction, and Architectural Power Prediction Models for High Peformance Embedded DSP Processors

This paper presents a comparison of statisticallyderived power prediction models at the algorithmic, instruction, and architectural levels for embedded high performance DSP processors. The approach is general enough to be applied to any embedded DSP processor. Results from 168 power measurements of DSP code show that power can be predicted at instruction and architecture levels with less than 2% error. This result is important for developing a general methodology for power characterization of embedded DSP software since low power is critical to complex DSP applications in many cost sensitive markets.