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.
| Original language | English |
|---|---|
| Pages (from-to) | 1872-1885 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
| Volume | 43 |
| Issue number | 6 |
| Early online date | 25 Dec 2023 |
| DOIs | |
| Publication status | Published - Jun 2024 |
Scopus Subject Areas
- Software
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
User-Defined Keywords
- GPU acceleration
- neural network
- physical design
- placement
- routability optimization