GNN-Based Hierarchical Annotation for Analog Circuits

Kishor Kunal, Tonmoy Dhar, Meghna Madhusudan, Jitesh Poojary, Arvind K Sharma, Wenbin Xu, Steven M. Burns, Jiang Hu, Ramesh Harjani, Sachin S. Sapatnekar

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Analog designs consist of multiple hierarchical functional blocks. Each block can be built using one of several design topologies, where the choice of topology is based on circuit performance requirements. A major challenge in automating analog design is in the identification of these functional blocks, which enables the creation of hierarchical netlist representations. This can facilitate a variety of design automation tasks, such as circuit layout optimization, because the layout is dictated by constraints at each level, such as symmetry requirements, that depend on the topology of the hierarchical block. Traditional graph-based methods find it hard to automatically identify the large number of structural variants of each block. To overcome this limitation, this article leverages recent advances in graph neural networks (GNNs). A variety of GNN strategies is used to identify netlist elements for circuit functional blocks at higher levels of the design hierarchy, where numerous design variants are possible. At lower levels of hierarchy, where the degrees of freedom in circuit topology is limited, structures are identified using graph-based algorithms. The proposed hierarchical recognition scheme enables the identification of layout constraints, such as symmetry and matching, which enable high-quality hierarchical layouts. This method is scalable across a wide range of analog designs. An experimental evaluation shows a high degree of accuracy over a wide range of analog designs, identifying functional blocks, such as low-noise amplifiers, operational transconductance amplifiers, mixers, oscillators, and band-pass filters, in larger circuits.

Original languageEnglish (US)
Pages (from-to)2801-2814
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume42
Issue number9
DOIs
StatePublished - Sep 1 2023

Bibliographical note

Funding Information:
This work was supported in part by the DARPA IDEA Program through SPAWAR under Contract N660011824048. This article was recommended by Associate Editor H. E. Graeb

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Analog circuit annotation
  • analog design
  • analog layout automation
  • graph neural networks (GNNs)
  • hierarchical layout generation

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