Xplace: An Extremely Fast and Extensible Placement Framework

Abstract

Placement serves as a fundamental step in VLSI physical design. Recently, GPU-based placer DREAMPlace 1 demonstrated its superiority over CPU-based placers. In this work, we develop an extremely fast GPU-accelerated placer Xplace which considers factors at operator-level optimization. Xplace achieves around 2x speedup with better solution quality compared to DREAMPlace. We also plug a novel Fourier neural network into Xplace as an extension. Besides, we enable Xplace to handle the detailed-routability-driven placement problem and demonstrate its superiority in terms of quality and performance. We believe this work not only proposes an extremely fast and extensible placement framework but also illustrates a possibility of incorporating a neural network component into a GPU-accelerated analytical placer. The source code of Xplace is released on GitHub.