Name
Abstract | ||
|---|---|---|
This paper proposes an automatic flow to repair IR-drop violations by Engineering Change Order (ECO). Our ECO technique provides cell move and downsize solutions. We use machine learning to predict IR-drop so that we can prevent over-fixing. We use a commercial tool to predict timing so that this is a timing-aware ECO. With the above two predictions, we propose a novel multi-round bipartite matching to optimize the ECO resource utilization. Experimental results show that for a 5M gate real design, our proposed method repairs 2,504 (22%) violation cells out of the original 11,555 violation cells and repairs 36,272mV (37%) total excessive IR out of the original 98,674mV total excessive IR. We are able to perform ECO on seven thousand cells within 13 hours, so our ECO flow is practical and can be applied to large industrial designs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ITC-Asia51099.2020.00013 | 2020 IEEE International Test Conference in Asia (ITC-Asia) |
Keywords | DocType | ISBN |
IR-drop,ECO,Machine learning | Conference | 978-1-7281-8944-4 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng-Yi Lin | 1 | 1 | 0.39 |
| Yen-Chun Fang | 2 | 1 | 0.39 |
| Shi-Tang Liu | 3 | 1 | 0.39 |
| Jia-Xian Chen | 4 | 1 | 0.39 |
| James Chien-Mo Li | 5 | 187 | 27.16 |
| Eric Jia-Wei Fang | 6 | 8 | 1.60 |