mlsanityData to 7 Oct 2026

CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis

arXiv 2407.10977 ↗First posted 6 Jun 2024cs.LG cs.AI cs.ETcited by 5

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Abstract (arXiv)

Circuit topology generation plays a crucial role in the design of electronic circuits, influencing the fundamental functionality of the circuit. In this paper, we introduce CIRCUITSYNTH, a novel approach that harnesses LLMs to facilitate the automated synthesis of valid circuit topologies. With a dataset comprising both valid and invalid circuit configurations, CIRCUITSYNTH employs a sophisticated two-phase methodology, comprising Circuit Topology Generation and Circuit Topology Refinement. Experimental results demonstrate the effectiveness of CIRCUITSYNTH compared to various fine-tuned LLM variants. Our approach lays the foundation for future research aimed at enhancing circuit efficiency and specifying output voltage, thus enabling the automated generation of circuit topologies with improved performance and adherence to design requirements.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 6 Jun 2024.

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9 of 15 references.

References · 9

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
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  6. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  7. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  8. 2022UL2: Unifying Language Learning Paradigmscited by 292
  9. 2023Talk like a Graph: Encoding Graphs for Large Language Modelscited by 175

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.