IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems | 2021
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Abstract
Placement for very large-scale integrated (VLSI) circuits is one of the most important steps for design closure. We propose a novel GPU-accelerated placement framework DREAMPlace, by casting the analytical placement problem equivalently to training a neural network. Implemented on top of a widely adopted deep learning toolkit <monospace>PyTorch</monospace>, with customized key kernels for wirelength and density computations, DREAMPlace can achieve around <inline-formula> <tex-math notation= LaTeX >$40\\times $ </tex-math></inline-formula> speedup in global placement without quality degradation compared to the state-of-the-art multithreaded placer RePlAce. We believe this work shall open up new directions for revisiting classical EDA problems with advancements in AI hardware and software.