학술논문
Xplace: An Extremely Fast and Extensible Placement Framework
Document Type
Periodical
Author
Source
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on. 43(6):1872-1885 Jun, 2024
Subject
Language
ISSN
0278-0070
1937-4151
1937-4151
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.