Local search algorithms for timing-driven placement under arbitrary delay models

We present local search algorithms for timing-driven placement optimization. They find local slack optima for cells under arbitrary delay models and can be applied late in the design flow. The key ingredients are an implicit path straightening and a clustering of neighboring cells. Cell clusters are moved jointly to speed up the algorithm and escape suboptimal solutions, in which single cell algorithms are trapped, particularly in the presence of layer assignments. Given a cell cluster, we initially perform a line search for maximum slack on the straight line segment connecting the most critical upstream and downstream cells of the cluster. Thereby, the Euclidean path length is minimized. An iterative application will implicitly straighten the path. Later, slacks are improved further by applying ascent steps in estimated super-gradient direction. The benefit of our algorithms is demonstrated experimentally within an industrial microprocessor design flow, and on recent ICCAD benchmarks circuits.