survey

A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Authors:

Daniela Sánchez,

Lorenzo Servadei,

Gamze Naz Kiprit,

Wolfgang EckerAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems, Volume 28, Issue 2

Article No.: 15, Pages 1 - 27

https://doi.org/10.1145/3543853

Published: 21 February 2023 Publication History

Abstract

Driven by Moore’s law, the chip design complexity is steadily increasing. Electronic Design Automation (EDA) has been able to cope with the challenging very large-scale integration process, assuring scalability, reliability, and proper time-to-market. However, EDA approaches are time and resource demanding, and they often do not guarantee optimal solutions. To alleviate these, Machine Learning (ML) has been incorporated into many stages of the design flow, such as in placement and routing. Many solutions employ Euclidean data and ML techniques without considering that many EDA objects are represented naturally as graphs. The trending Graph Neural Networks (GNNs) are an opportunity to solve EDA problems directly using graph structures for circuits, intermediate Register Transfer Levels, and netlists. In this article, we present a comprehensive review of the existing works linking the EDA flow for chip design and GNNs. We map those works to a design pipeline by defining graphs, tasks, and model types. Furthermore, we analyze their practical implications and outcomes. We conclude by summarizing challenges faced when applying GNNs within the EDA design flow.

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cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 28, Issue 2

March 2023

409 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3573314

Editor:
X. Sharon Hu
University of Notre Dame, USA

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 21 February 2023

Online AM: 14 June 2022

Accepted: 27 May 2022

Revised: 16 May 2022

Received: 10 March 2022

Published in TODAES Volume 28, Issue 2

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Fan GMa TSun XShao LLow K(2025)Graph Attention Network-Based Unified TCAD Modeling Enabling Fast Design Technology Co-Optimization Through Transfer LearningIEEE Transactions on Electron Devices10.1109/TED.2024.349385472:1(474-481)Online publication date: Jan-2025
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