Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

Conference Paper

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This work addresses this challenge by proposing a graph attention network method with customization, called Net2, to estimate individual net length before cell placement. Its accuracyoriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than 1000 faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.

Full Text

Duke Authors

Cited Authors

  • Xie, Z; Liang, R; Xu, X; Hu, J; Duan, Y; Chen, Y

Published Date

  • January 18, 2021

Published In

  • Proceedings of the Asia and South Pacific Design Automation Conference, Asp Dac

Start / End Page

  • 671 - 677

International Standard Book Number 13 (ISBN-13)

  • 9781450379991

Digital Object Identifier (DOI)

  • 10.1145/3394885.3431562

Citation Source

  • Scopus