This paper presents effective metrics to evaluate the power dissipation of scheduled data flow graphs (DFGs). This enables early evaluation of schedules without performing the computationally expensive resource-binding step. Our metrics correlate heavily (as high as 0.95 and > 0.75 for most test cases) with power dissipation values obtained after resource binding and rescheduling for power optimization steps. An experimental flow that integrates path-based scheduling, power optimal binding and

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... ower driven iterative rescheduling stages is constructed. The flow integrates commercial tools like Synopsys, VSS and academic compilers like SUIF in a common optimization framework. Experimental results on DFGs from MediaBench suit also demonstrate the fact that metric evaluation is on average 42.6 times faster than performing optimal binding and iterative power improvement. Hence metric based evaluation enables fast design exploration at early stages.