Early Quality Assessment for Low Power Behavioral Synthesis

Abstract

Fast and effective exploration at the early stages of the design flow can yield significant improvement in the quality of the design and substantial reduction in design time. In this paper, we present an efficient technique to evaluate the power dissipation of scheduled Data Flow Graphs (DFGs). Scheduling dictates the compatibility of operations with respect to their assignments to functional units. Generally for scheduled DFGs, this relation is captured in the form of a comparability graph. As a consequence, the topology of the comparability graph determines the solution space available to the subsequent binding stage. In this work, our main contribution is a technique to assess the inherent flexibility of the schedules we start with. We developed early evaluation metrics in order to assess the degree of flexibility inherent in an initial schedule that will eventually affect the quality of the binding solution. Every schedule is associated with a compatibility graph that represents the conflicts and compatibilities among operations with respect to possible binding decisions. Our metric based evaluation technique is based on several properties (such as edge connectivity, edge weight distribution, etc.) of these compatibility graphs. These metrics essentially reflect the amount of freedom that is provided to the binding stage, which enables early assessment and relative comparison of different possible schedules without actually performing the resource-binding step. Our experimental framework integrates scheduling, early metric-based power evaluation, low power binding and power driven iterative rescheduling stages. The correlation between early evaluation and the power measurements after binding is as high as 0.95 and greater than 0.75 for majority of test cases. Experimental results on DFGs from MediaBench suite demonstrate the fact that metric evaluation is on average 42.6 times faster than performing optimal binding and iterative power improvement. Our results show that low power schedule selection is fast and effective. On average, the schedules selected by metric evaluation have 43% less power dissipation than schedules with iterative power improvement, based on a study set of 320 schedules. We also examined the thermal profile of the corresponding solutions. We observed that schedules selected with our metric evaluation technique have on average 12 C lower temperature, and the maximum on-chip temperatures are lower by 18 C compared to the overall average of all schedules. These thermal profiles are obtained using a functional unit-level thermal simulator after block-level floorplanning.