The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design

Distinct from traditional heuristic solvers, this paper on one hand proposes an RL-based model for mixed-size macro placement, which differs from existing learning-based placers that often consider the macro by coarse grid-based mask. While the standard cells are placed via gradient-based GPU acceleration. On the other hand, a one-shot conditional generative routing model, which is composed of a special-designed input-size-adapting generator and a bi-discriminator, is devised to perform one-shot routing to the pins within each net, and the order of nets to route is adaptively learned.