UnetPro: Combining Attention with Skip Connection in Unet for Efficient IR Drop Prediction

IR drop analysis plays a key role in chip design. Unlike conventional time-consuming numerical analysis methods, which involve solving large-scale linear circuit equations, predicting the IR drop with machine learning shows great potential to significantly reduce computation time. However, applying machine learning for accurate prediction is non-trivial since achieving effective feature extraction poses significant challenges. Furthermore, as the number of layers in the neural network model increases, the loss of information in the transmission process gradually increases, leading to inaccurate prediction results. In this paper, we propose UnetPro, an innovative ma-chine learning model to resolve these challenges. We leverage an attention mechanism that combines both global and local information and a multi-scale convolution module to make the model sufficiently perceive the various regions of the feature map, enhancing the feature extraction ability of the model. Moreover, we ensure the coherence of information by introducing skip connection. We also introduce the dropout mechanism to ensure the stability of model with information transfer. Compared with conventional Unet model, the error and correlation of our proposed algorithm are lower than it by 2.5e-4 and higher by 10.34%, respectively.