Complex network knowledge-based field programmable gate arrays routing congestion prediction

Abstract

Routing congestion occurs due to the unprecedented complexity of FPGA (field programmable gate array) designs. Accurately predicting the congestion of today’s FPGAs at an early stage helps to reduce the burden of later design and optimization. This paper proposes an innovative complex network knowledge-based approach to predict FPGA routing congestion during the placement stage. The complex network features and circuit features highly correlated with routing congestion are mapped into RGB (red-green-blue) images according to the pre-perceived importance of the features to feed to the proposed model. A patched EDM (elucidating the design space of a diffusion-based generative model) with a patch transformation is introduced to focus on the most significant features.

Experimental results show the remarkable achievements of the approach with an average SSIM (structural similarity) of 85.01 %, PSNR (peak signal-to-noise Ratio) of 27.85 dB (decibels), NRMS (normalized root mean square) of 12.91 %, and PIX (pixel accuracy) of 18.73 %, outperforming the recent state-of-the-art models like pix2pix, pix2pixHD, FCN (fully convolutional networks), and Lay-Net, improved by 4.87 %, 2.83 %, 5.77 %, and 18.56 % on key metric SSIM, respectively. The ablation validation highlights the efficiency of complex network features in routing congestion prediction. The outcome enables the identification of potential routing congestion in early design stages, facilitating the optimization solution of subsequent tractable routing problems.