AppGNN: Approximation-aware functional reverse engineering using graph neural networks

Tim Bücher, Lilas Alrahis, Guilherme Paim, Sergio Bampi, Ozgur Sinanoglu, Hussam Amrouch

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns, such as those imposed by functional Reverse Engineering (RE), have become quintessential. RE leads to disclosure of confidential information to competitors, potentially enabling the theft of intellectual property. Traditional functional RE methods analyze a given gate-level netlist through employing pattern matching towards reconstructing the underlying basic blocks, and hence, reverse engineer the circuit s function. In this work, we are the first to demonstrate that applying Approximate Computing (AxC) principles to circuits significantly improves the resiliency against RE. This is attributed to the increased complexity in the underlying pattern-matching process. The resiliency remains effective even for Graph Neural Networks (GNNs) that are presently one of the most powerful state-of-the-art techniques in functional RE. Using AxC, we demonstrate a substantial reduction in GNN average classification accuracy- from 98% to a mere 53%. To surmount the challenges introduced by AxC in RE, we propose the highly promising AppGNN platform, which enables GNNs (still being trained on exact circuits) to: (i) perform accurate classifications, and (ii) reverse engineer the circuit functionality, notwithstanding the applied approximation technique. AppGNN accomplishes this by implementing a novel graph-based node sampling approach that mimics generic approximation methodologies, requiring zero knowledge of the targeted approximation type. We perform an extensive evaluation targeting wide-ranging adder and multiplier circuits that are approximated using various AxC techniques, including state-of-the-art evolutionary-based approaches. We show that, using our method, we can improve the classification accuracy from 53% to 81% when classifying approximate adder circuits that have been generated using evolutionary algorithms, which our method is oblivious of. Our AppGNN framework is publicly available under

Original languageEnglish (US)
Title of host publicationProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450392174
StatePublished - Oct 30 2022
Event41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 - San Diego, United States
Duration: Oct 30 2022Nov 4 2022

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Conference41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Country/TerritoryUnited States
CitySan Diego


  • Approximate computing
  • GNN
  • Graph neural networks
  • Machine learning
  • Reverse engineering
  • Security

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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