CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866916325439307776 |
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| author | Vijayaraghavan, Prashanth Shi, Luyao Degan, Ehsan Zhang, Xin |
| author_facet | Vijayaraghavan, Prashanth Shi, Luyao Degan, Ehsan Zhang, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis Vijayaraghavan, Prashanth Shi, Luyao Degan, Ehsan Zhang, Xin Machine Learning Artificial Intelligence Emerging Technologies 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. |
| title | CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis |
| topic | Machine Learning Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2407.10977 |