# Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism

arXiv: https://arxiv.org/abs/2505.21988 · HTML: https://mlsanity.com/paper/2505.21988/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 12 of 12)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Neural Subgraph Matching](https://mlsanity.com/paper/2007.03092/) (2020)
- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis](https://mlsanity.com/paper/2110.11292/) (2021)
- [Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks](https://mlsanity.com/paper/2303.08256/) (2023)
- [DeepGate3: Towards Scalable Circuit Representation Learning](https://mlsanity.com/paper/2407.11095/) (2024)
- [DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale](https://mlsanity.com/paper/2502.01681/) (2025)
- [ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA](https://mlsanity.com/paper/2505.02016/) (2025)
- [Representation Learning for Frequent Subgraph Mining](https://mlsanity.com/paper/2402.14367/) (2024)
- [Taming Subgraph Isomorphism for RDF Query Processing](https://mlsanity.com/paper/1506.01973/) (2015)
- [Attacking Split Manufacturing from a Deep Learning Perspective](https://mlsanity.com/paper/2007.03989/) (2020)

## Built on by (most cited first, 1 of 1)

- [Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL](https://mlsanity.com/paper/2603.09161/) (2026)

## Abstract (arXiv)

Subgraph matching in logic circuits is foundational for numerous Electronic Design Automation (EDA) applications, including datapath optimization, arithmetic verification, and hardware trojan detection. However, existing techniques rely primarily on structural graph isomorphism and thus fail to identify function-related subgraphs when synthesis transformations substantially alter circuit topology. To overcome this critical limitation, we introduce the concept of functional subgraph matching, a novel approach that identifies whether a given logic function is implicitly present within a larger circuit, irrespective of structural variations induced by synthesis or technology mapping. Specifically, we propose a two-stage multi-modal framework: (1) learning robust functional embeddings across AIG and post-mapping netlists for functional subgraph detection, and (2) identifying fuzzy boundaries using a graph segmentation approach. Evaluations on standard benchmarks (ITC99, OpenABCD, ForgeEDA) demonstrate significant performance improvements over existing structural methods, with average 93.8% accuracy in functional subgraph detection and a dice score of 91.3% in fuzzy boundary identification. The source code and implementation details can be found at https://github.com/zyzheng17/FunctionalSubgraphMatching-Neurips25.