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. This paper proposes 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 over 30× 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 advancement in AI hardware and software.

Authors: 
Yibo Lin (UT Austin)
Shounak Dhar (UT Austin)
Wuxi Li (UT Austin)
David Z. Pan (UT Austin)
Publication Date: 
Sunday, June 2, 2019
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