Probabilistic Congestion Prediction with Partial Blockages

Fast and accurate routing congestion estimation is essential for optimizations such as floorplanning, placement, buffering, and physical synthesis that need to avoid routing congestion. Using a probabilistic technique instead of a global router has the advantage of speed and easy updating. Previously proposed probabilistic models (Lou, et al., 2001) (Westra et al., 2004) do not account for wiring that may already be fixed in the design, e.g., due to macro blocks or power rails. These "partial wiring blockages" certainly influence the global router, so they should also influence a probabilistic routing prediction algorithm. This work proposes a probabilistic congestion prediction metric that extends the work of (Westra et al., 2004) to model partial wiring blockages. The authors also show a new fast algorithm to efficiently generate the congestion map and demonstrate the effectiveness of our methods on real routing problems