A Methodology for Power-aware Pipelining via High-Level Performance Model Evaluations

Power is one of the major constraints considered during the design of embedded software. In order to reduce power consumption without sacrificing performance, software needs to be optimized in order to run as efficiently as possible on a given platform. When attempting to optimize the mapping of a piece of software on a multiprocessor system, designers often face the chicken-and-egg problem of whether to schedule tasks first, or do memory allocation first, as either step will affect the different optimization opportunities the other may provide. Because each optimization will affect the system's power consumption, it is critically important to be able to monitor the effects these transformations have. In this paper we present a methodology that allows designers to quickly evaluate the impact each code optimization will have in the system's power. Our exploration engine relies on SystemC-based power/performance models to quickly and accurately evaluate the dynamic power due to memory accesses as well as the expected CPU power consumption.