DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement

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 PyTorch, with customized key kernels for wirelength and density computations, DREAMPlace can achieve around 40× speedup in global placement without quality degradation compared to the state-of-the-art multi-threaded placer RePlAce. We believe this work shall open up new directions for revisiting classical EDA problems with advancements in AI hardware and software.


Yibo Lin (Peking University)
Zixuan Jiang (UT-Austin)
Jiaqi Gu (UT-Austin)
Wuxi Li (Xilinx)
Shounak Dhar (Intel)
David Z. Pan (UT-Austin)

Publication Date


2021 IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award