Obstacle Avoidance Rectilinear Steiner Minimal Tree Length Estimation Using Deep Learning

Obstacle avoidance rectilinear Steiner minimal tree (OARSMT) connects multiple pins belonging to a net using minimal wire length and avoids the obstacles present on the grid, and this is an essential part of the placement/routing phases of VLSI physical design. High-level tasks such as floor-planning and placement use estimators to determine the quality of solutions. The use of OARSMT can provide better estimations. In this work we propose to use deep learning (DL) to quickly predict the length of the OARSMT of a net with pins located anywhere on the routing grid, where the routing grid's dimension and the obstacles remain fixed. The proposed method consists of a data encoder and a DL model of three convolutional layers and an output layer. The encoder generates a low-dimensional representation of the problem data, and the DL model extracts features and predicts the wire length. We used the industrial test problems to train and test the proposed system.