Name
Title | ||
|---|---|---|
Predictable Coupling Effect Model for Global Placement Using Generative Adversarial Networks With an Ordinary Differential Equation Solver |
Abstract | ||
|---|---|---|
One of the most important issues in physical design is coupling capacitance. However, the issue is typically addressed during the routing stage, which necessitates the execution of a time-consuming algorithm. Based on the generative adversarial networks (GAN) model, we propose a coupling-free global placement (CFGP) model with different orders of ordinary differential equations (ODE) solver. Experiments on the ISPD’11/DAC’12 contest benchmark revealed that using the ODE-GAN architecture, our coupling effect estimator (CEE) model can achieve 0.91X similarity to the ground-truth image and a 50X speedup over traditional global routers such as NCTUgr. Compared to the original framework without the CEE model, the CFGP implemented using DREAMPlace results in a 41% reduction in coupling effect. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSII.2021.3136084 | IEEE Transactions on Circuits and Systems II: Express Briefs |
Keywords | DocType | Volume |
Coupling,global placement,physical design,ordinary differential equation (ODE),generative adversarial network (GAN),electronic design automation (EDA) | Journal | 69 |
Issue | ISSN | Citations |
4 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yung-Yi Lee | 1 | 0 | 0.34 |
| Shanq-Jang Ruan | 2 | 375 | 55.44 |
| Pin-Chang Chen | 3 | 0 | 0.68 |