Late Breaking Results: Parallelizing Net Routing with cGANs
TimeWednesday, December 8th6:00pm - 7:00pm PST
LocationLevel 2 - Lobby
Event Type
Late Breaking Results Poster
Networking Reception
Virtual Programs
Presented In-Person
DescriptionObstacle-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.