Abstract
Traditionally, analytic placement has used linear or quadratic wirelength objective functions. Minimizing either formulation attracts cells sharing common signals (nets) together. The result is a placement with a great deal of overlap among the cells. To reduce cell overlap, the methodology iterates between global optimization and repartitioning of the placement area. In this work, we added new attractive and repulsive forces to the traditional formulation so that overlap among cells is diminished without repartitioning the placement area. The superiority of our approach stems from the fact that our new formulations are convex and no hard constraints are required. A preliminary version of the new placement method is tested using a set of MCNC benchmarks and, on average, the new method achieved 3.96% and 7.6% reduction in wirelength and CPU time compared to TimberWolf v7.0 in the hierarchical mode.
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