Name
Abstract | ||
|---|---|---|
Local nets are by default ignored during global routing but can contribute to a high percentage (up to 30%) of total number of nets in the design. Prior work proposed simple models for how local nets are routed and showed benefits such as better congestion analysis post-placement, integration with global routing, and better track assignment. In this work we study local net modeling using machine learning. Our model predicts utilization by local routes inside each global cell. We model this as a regression problem and as reference use local route utilization data from the detailed routing stage using a commercial tool. To solve the problem we identify suitable machine learning algorithms. Within our modeling, we study and rank different features which utilize various layout attributes. We identify the most beneficial features and show our model performs superior to prior work which were based on pin density and Steiner tree models. Our model also performs better for the subset of local nets which are routed in more than one global cell.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3194554.3194579 | ACM Great Lakes Symposium on VLSI |
Keywords | Field | DocType |
Machine Learning, Local Nets, Global Routing | Computer science,Steiner tree problem,Artificial intelligence,Regression problems,Machine learning | Conference |
ISSN | ISBN | Citations |
1066-1395 | 978-1-4503-5724-1 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jackson Melchert | 1 | 5 | 1.46 |
| boyu zhang | 2 | 71 | 17.54 |
| Azadeh Davoodi | 3 | 362 | 34.99 |