Predicting routability of FPGA design by learning complex network images

Abstract

Accurately and efficiently predicting the routability of modern FPGAs in the early stages is significant for achieving ultimate optimization. We propose a novel approach for FPGA routability prediction by the CBAM-CNN model incorporating an attention mechanism to learn complex network images. For an FPGA placement design, we innovatively extract the circuit-related and complex network-related features associated with routing congestion and map them into a complex network image. The approach demonstrates a remarkable performance in routability prediction, surpassing the recent machine learning models in accuracy, precision, sensitivity, and specificity. Notably, the Matthews correlation coefficient has improved by 6.15% compared with the state-of-the-art convolutional neural network model (Alhyari et al., 2019). The additional ROC (Receiver Operating Characteristic Curve) analysis highlights the model’s classification efficiency. We estimate the significance of complex network features in predicting FPGA design routability, presenting in descending order of Degree, Strength, Eigenvector, and Betweenness.