Global placement roughly decides the location of units in the very
large-scale integrated (VLSI) and fundamentally determines the quality
of physical design. Thus, it’s desirable to find an efficient method to
solve the global placement problem. Global placement solves the problem
by minimizing the total half-perimeter wirelength (HPWL) under density
constraints. However, the non-differentiability of HPWL prevents
advanced gradient-based methods from being applied to global placement.
Therefore, smooth wirelength models have been proposed to approximate
HPWL. Among all the models, weighted-average wirelength (WAWL) performs
the best. In this letter, we propose an improved self-adaptive
weighted-average wirelength (SaWAWL) model to further fit the HPWL.
Instead of setting a generic γ for all nets in the design, the new model
enables each net to adaptively adjust their respective γ according to
their real length, thus can better approximate HPWL to achieve
higher-quality placement results. Based on the SaWAWL and the framework
of DREAMPlace, a global placer is implemented. Experimental results show
that HPWL on open-source benchmarks is reduced by up to 6.56% with an
average of 3.74%, which proves that our model can achieve better
performance than the current state-of-the-art WAWL.