Late Breaking Results: A Neural Network that Routes ICs

Abstract

A global router is proposed that learns from routed circuits and autonomously routes unseen layouts. The uniqueness of this approach is in redefining the global routing as a classical image-to-image processing problem. The imaging problem is efficiently solved with a deep learning system, comprising a variational autoencoder and custom loss function. This fundamentally new routing method provides a natural way for global routing parallelization. The deep router is designed, trained, and tested on an unseen 64×64 ISPD'98 benchmark circuit. The test results yield 3.2% decrease in routability and over 5X speedup in runtime as compared with the state-of-the-art FastRoute router.