Name
Abstract | ||
|---|---|---|
In advanced technology nodes, physical design engineers must estimate whether a standard-cell placement is routable (before invoking the router) in order to maintain acceptable design turnaround time. Modern SoC designs consume multiple compute servers, memory, tool licenses and other resources for several days to complete routing. When the design is unroutable, resources are wasted, which increases the design cost. In this work, we develop machine learning-based models that predict whether a placement solution is routable without conducting trial or early global routing. We also use our models to accurately predict iso-performance Pareto frontiers of utilization, aspect ratio and number of layers in the back-end-of-line (BEOL) stack. Furthermore, using data mining and machine learning techniques, we develop new methodologies to generate training examples given very few placements. We conduct validation experiments in three foundry technologies (28nm FDSOI, 28nm LP and 45nm GS), and demonstrate accuracy ≥ 85.9% in predicting routability of a placement. Our predictions of Pareto frontiers in the three technologies are pessimistic by at most 2% with respect to the maximum achievable utilization for a given design in a given BEOL stack. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICCD.2016.7753259 | 2016 IEEE 34th International Conference on Computer Design (ICCD) |
Keywords | Field | DocType |
BEOL stack-aware routability prediction,data mining,standard-cell placement,SoC designs,compute servers,tool licenses,machine learning-based models,placement solution,early global routing,iso-performance Pareto frontiers,back-end-of-line,foundry technologies | Data mining,Computer science,Server,Real-time computing,Turnaround time,Physical design,Router,Pareto principle | Conference |
ISSN | ISBN | Citations |
1063-6404 | 978-1-5090-5143-4 | 3 |
PageRank | References | Authors |
0.38 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Ting Jonas Chan | 1 | 69 | 6.70 |
| Yang Du | 2 | 136 | 11.99 |
| Andrew B. Kahng | 3 | 7582 | 859.06 |
| Siddhartha Nath | 4 | 240 | 15.01 |
| Kambiz Samadi | 5 | 817 | 43.11 |