Power Optimal Module Allocation and Binding

In this paper, we investigate the problem of minimizing the total power consumption during the binding of operations to functional units in a scheduled data path with functional pipelining and conditional branching. We first present a technique to estimate the power consumption in a functionally pipelined data path and then formulate the power optimization problem as a max-cost multi-commodity flow problem and solve it optimally. Our proposed method can augment most high-level synthesis algorithms as a post-processing step for reducing power after the optimizations for area or speed have been completed. An average power savings of 28% has been observed after we apply our method to pipelined designs that have been optimized using conventional techniques.