| 摘要(英) |
With the advancement of semiconductor manufacturing techniques, more transistors can be accommodated in the same area. As the results, the complexity of Integrated Circuits (ICs) increases. Consequently, designing ICs has become notably time consuming. Among the various stages, routing, in particular, is one of the most time-consuming steps in the physical design process, often taking several hours or even days. In some cases, issues such as routing congestion or Design Rule Violations (DRVs) occur. When these issues arise, it necessitates returning to earlier stages of the process to adjust the chip design to fix these issues. This iterative process may occur multiple times throughout the chip design cycle. Hence, many studies employ machine learning algorithms to construct models that can predict potential issues in previous stages. Engineers can adjust chip design before actually performing routing, reducing the repeated execution of the routing process. However, due to the process of chip design is time consuming, it’s hard to collect a vast amount of training data. Moreover, Neural Network (NN) base model needs a lot of data for model training, insufficient training data might lead to overfitting. Therefore, this paper utilizes the ensemble learning algorithm, Extreme Gradient Boosting (XGBoost). XGBoost is a highly efficient algorithm, and compared to traditional gradient boosting methods, XGBoost incorporates regularization term into its objective function. This regularization term can prevent overfitting, enabling the model to predict routing outcomes accurately with limited training data. Additionally, XGBoost is based on a decision tree structure, offering better interpretability compared to neural networks. Leveraging these advantages of XGBoost, this paper analyzes the importance of input features, filtering out redundant features to reduce training time and boost model efficiency during inference. During model training, we extract circuit features in placement stage and use the routing demand after global routing as the prediction target. |
| 參考文獻 |
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