Late Breaking Results: Parallelizing Net Routing with cGANs

Obstacle-avoiding multiterminal net routing approach is proposed. The approach is inspired by deep learning image processing. The key idea is based on training a conditional generative adversarial network (cGAN) to interpret a routing task as a graphical bitmap and consequently map it to an optimal routing solution represented by another bitmap. The system is implemented in Python/Keras, trained on synthetically generated data, evaluated on typical high-resolution benchmarks, and compared with state-of-the-art traditional deterministic and deep learning solutions. The proposed system yields between 10.75x and 83.33x speedup over the traditional router without wirelength overhead due to effective parallelization on GPU hardware.