mlsanityData to 14 Oct 2026

DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning

arXiv 2502.06816 ↗First posted 5 Feb 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

We introduce DeepCell, a novel circuit representation learning framework that effectively integrates multiview information from both And-Inverter Graphs (AIGs) and Post-Mapping (PM) netlists. At its core, DeepCell employs a self-supervised Mask Circuit Modeling (MCM) strategy, inspired by masked language modeling, to fuse complementary circuit representations from different design stages into unified and rich embeddings. To our knowledge, DeepCell is the first framework explicitly designed for PM netlist representation learning, setting new benchmarks in both predictive accuracy and reconstruction quality. We demonstrate the practical efficacy of DeepCell by applying it to critical EDA tasks such as functional Engineering Change Orders (ECO) and technology mapping. Extensive experimental results show that DeepCell significantly surpasses state-of-the-art open-source EDA tools in efficiency and performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

17 of 49 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2020Reformer: The Efficient Transformercited by 1,668
  5. 2018Learning a SAT Solver from Single-Bit Supervisioncited by 200
  6. 2021DeepGate: Learning Neural Representations of Logic Gatescited by 37
  7. 2023DeepGate2: Functionality-Aware Circuit Representation Learningcited by 33
  8. 2024RTLRewriter: Methodologies for Large Models aided RTL Code Optimizationcited by 31
  9. 2023Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networkscited by 27
  10. 2024DeepGate3: Towards Scalable Circuit Representation Learningcited by 26
  11. 2024The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Modelscited by 12
  12. 2025DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scalecited by 12
5 more
  1. 2022DeepTPI: Test Point Insertion with Deep Reinforcement Learningcited by 10
  2. 2023DeepSeq: Deep Sequential Circuit Learningcited by 7
  3. 2024Logic Optimization Meets SAT: A Novel Framework for Circuit-SAT Solvingcited by 4
  4. 2024MapTune: Advancing ASIC Technology Mapping via Reinforcement Learning Guided Library Tuningcited by 4
  5. 2024Practical Boolean Decomposition for Delay-driven LUT Mappingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Revolution or Hype? Seeking the Limits of Large Models in Hardware Designcited by 9
  2. 2025Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Networkcited by 0
  3. 2026FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Predictioncited by 0