English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) forperformance in learning and predicting on large-scale data present in socialnetworks, biology, etc. Since integrated circuits (ICs) can naturally berepresented as graphs, there has been a tremendous surge in employing GNNs formachine learning (ML)-based methods for various aspects of IC design. Giventhis trajectory, there is a timely need to review and discuss some powerful andversatile GNN approaches for advancing IC design.  In this paper, we propose a generic pipeline for tailoring GNN models towardsolving challenging problems for IC design. We outline promising options foreach pipeline element, and we discuss selected and promising works, likeleveraging GNNs to break SOTA logic obfuscation. Our comprehensive overview ofGNNs frameworks covers (i) electronic design automation (EDA) and IC design ingeneral, (ii) design of reliable ICs, and (iii) design as well as analysis ofsecure ICs. We provide our overview and related resources also in the GNN4IChub at https://github.com/DfX-NYUAD/GNN4IC. Finally, we discuss interestingopen problems for future research.

Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs