mlsanityData to 7 Oct 2026

DeepGate: Learning Neural Representations of Logic Gates

arXiv 2111.14616 ↗First posted 26 Nov 2021cs.LG cs.AIcited by 37

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Abstract (arXiv)

Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on "How to obtain a general and effective neural representation of circuits?" has not been answered yet. In this work, we take the first step towards solving this problem. We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 26 Nov 2021.

Builds on

20 of 32 references.

References · 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 8; 2025: 17; 2026: 6.

37 papers.

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  1. 2023LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automationcited by 40
  2. 2023DeepGate2: Functionality-Aware Circuit Representation Learningcited by 33
  3. 2024DeepGate3: Towards Scalable Circuit Representation Learningcited by 26
  4. 2025DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysiscited by 21
  5. 2025DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scalecited by 12
  6. 2022DeepSAT: An EDA-Driven Learning Framework for SATcited by 11
  7. 2024Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimizationcited by 10
  8. 2022TAG: Learning Circuit Spatial Embedding From Layoutscited by 9
  9. 2025NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graphcited by 9
  10. 2025Revolution or Hype? Seeking the Limits of Large Models in Hardware Designcited by 9
  11. 2023DeepSeq: Deep Sequential Circuit Learningcited by 7
  12. 2025MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMscited by 6
25 more
  1. 2024PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modelingcited by 5
  2. 2024Circuit Transformer: A Transformer That Preserves Logical Equivalencecited by 5
  3. 2024DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representationscited by 5
  4. 2023Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designscited by 4
  5. 2024OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesiscited by 3
  6. 2025DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learningcited by 3
  7. 2025DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretrainingcited by 2
  8. 2024SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generationcited by 1
  9. 2025GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignmentcited by 1
  10. 2025FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networkscited by 1
  11. 2025Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learningcited by 1
  12. 2025BDD2Seq: Enabling Scalable Reversible-Circuit Synthesis via Graph-to-Sequence Learningcited by 1
  13. 2025STELLAR: Structure-guided LLM Assertion Retrieval and Generation for Formal Verificationcited by 1
  14. 2024SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Accelerationcited by 0
  15. 2025Learning Library Cell Representations in Vector Spacecited by 0
  16. 2025Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Networkcited by 0
  17. 2025TRACE: Learning to Compute on Circuit Graphscited by 0
  18. 2025BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reductioncited by 0
  19. 2025InF-ATPG: Intelligent FFR-Driven ATPG with Advanced Circuit Representation Guided Reinforcement Learningcited by 0
  20. 2026GNN-based Path-aware multi-view Circuit Learning for Technology Mappingcited by 0
  21. 2026KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuitscited by 0
  22. 2026Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automationcited by 0
  23. 2026Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuitscited by 0
  24. 2026LevelSyn: Physical-Aware Logic Synthesis via Level-Asynchronous Graph Neural Networkscited by 0
  25. 2026ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networkscited by 0