A Customized Graph Neural Network Model for Guiding Analog IC Placement

Yaguang Li, Yishuang Lin, Meghna Madhusudan, Arvind Sharma, Wenbin Xu, Sachin S. Sapatnekar, Ramesh Harjani, Jiang Hu

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Analog IC placement is typically a manual process that requires strong experience and trial-and-error iterations as it produces a large impact to circuit performance in a complicated manner. Although automatic analog placement has been studied for decades, existing methods are inadequate for achieving performance comparable with manual designs. In this work, a customized graph neural network model is developed for predicting the impact of placement on circuit performance. Knowledge obtained by such a model can be transferred among different topologies of the same circuit type. Simulation results show that the proposed model is superior to a recent CNN-based work in terms of both accuracy and knowledge transfer. It also outperforms a plug-in use of graph attention network. The proposed model is further applied in analog IC placement and achieves performance similar to manual designs.

Original languageEnglish (US)
Article number9256781
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2020-November
DOIs
StatePublished - Nov 2 2020
Externally publishedYes
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: Nov 2 2020Nov 5 2020

Bibliographical note

Funding Information:
This work is supported by the DARPA ERI IDEA program. We thank Prof. Shuiwang Ji of Texas A&M University for helpful discussions.

Publisher Copyright:
© 2020 Association on Computer Machinery.

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