MAPLE

Abstract

We propose a new multilevel framework for large-scale placement called MAPLE that respects utilization constraints, handles movable macros and guides the transition between global and detailed placement. In this framework, optimization is adaptive to current placement conditions through a new density metric. As a baseline, we leverage a recently developed at quadratic optimization that is comparable to prior multilevel frameworks in quality and runtime. A novel component called Progressive Local Refinement (ProLR) helps mitigate disruptions in wirelength that we observed in leading placers. Our placer MAPLE outperforms published empirical results --- RQL, SimPL, mPL6, NTUPlace3, FastPlace3, Kraftwerk and APlace3 -- across the ISPD 2005 and ISPD 2006 benchmarks, in terms of official metrics of the respective contests.