电子与封装2026，Vol.26Issue(6)：49-53,5.DOI:10.16257/j.cnki.1681-1070.2026.0064
基于可布线性预测的AI模型训练及布局优化方法
AI Model Training and Placement Optimization Method Based on Routeability Prediction
虞健 1惠峰 1姜姗 1谢达1
作者信息
- 1. 无锡中微亿芯有限公司,江苏无锡 214072
- 折叠
摘要
Abstract
Currently,the mainstream FPGA design scale in the industry has exceeded 100 million gates,and traditional placement algorithm optimization has been unable to meet the design needs of users for ultra-large-scale circuits because the optimization objectives cannot be completely consistent with the actual routing requirements.A placement method based on improved ResNet50 is proposed and a data mapping model between placement parameters and routing completion rate is established.Meanwhile,a collaborative optimization method for placement and routing is proposed.The placement parameters are used as the training input to the AI model,and the routing result is used as the training target for the model.The trained model is then used to optimize the placement.According to the test results,this method effectively improves routing completion rate.关键词
布局优化/可布线性预测/ResNet50/FPGAKey words
placement optimization/routability prediction/ResNet50/FPGA分类
信息技术与安全科学引用本文复制引用
虞健,惠峰,姜姗,谢达..基于可布线性预测的AI模型训练及布局优化方法[J].电子与封装,2026,26(6):49-53,5.
