Power Optimal Module Allocation and Binding

Here 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 for data intensive applications. 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 desigens that have been optimized using conventional techniques. The published version of this work appears in [ChPe96a].

The summary and contributions of this work are as follows.

• We presented a new method to calculate the switching activity of modules in a functionally pipelined data-path with conditional branches based on the assumption that a large number of primary input data vectors are given.
• A power consumption model for a functionally pipelined data-path was presented and its properties were explored .
• The power optimization problem for functionally pipelined data-path was formulated as a multi-commodity flow problem and solved with out increasing the area or delay of the data-path or the controller complexity.
• Experimental results demonstrated that the above methods can reduce the power consumption substantially.