基于可布线性预测的AI模型训练及布局优化方法OA

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.