GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915465858646016 |
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| author | Pham, Phuoc Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Uhlich, Stefan Leibl, Markus Hofmann, Simon Ohbuchi, Eisaku Servadei, Lorenzo Schlichtmann, Ulf Wille, Robert |
| author_facet | Pham, Phuoc Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Uhlich, Stefan Leibl, Markus Hofmann, Simon Ohbuchi, Eisaku Servadei, Lorenzo Schlichtmann, Ulf Wille, Robert |
| contents | Analog subcircuit identification is a core task in analog design, essential for simulation, sizing, and layout. Traditional methods often require extensive human expertise, rule-based encoding, or large labeled datasets. To address these challenges, we propose GENIE-ASI, the first training-free, large language model (LLM)-based methodology for analog subcircuit identification. GENIE-ASI operates in two phases: it first uses in-context learning to derive natural language instructions from a few demonstration examples, then translates these into executable Python code to identify subcircuits in unseen SPICE netlists. In addition, to evaluate LLM-based approaches systematically, we introduce a new benchmark composed of operational amplifier netlists (op-amps) that cover a wide range of subcircuit variants. Experimental results on the proposed benchmark show that GENIE-ASI matches rule-based performance on simple structures (F1-score = 1.0), remains competitive on moderate abstractions (F1-score = 0.81), and shows potential even on complex subcircuits (F1-score = 0.31). These findings demonstrate that LLMs can serve as adaptable, general-purpose tools in analog design automation, opening new research directions for foundation model applications in analog design automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification Pham, Phuoc Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Uhlich, Stefan Leibl, Markus Hofmann, Simon Ohbuchi, Eisaku Servadei, Lorenzo Schlichtmann, Ulf Wille, Robert Hardware Architecture Machine Learning Analog subcircuit identification is a core task in analog design, essential for simulation, sizing, and layout. Traditional methods often require extensive human expertise, rule-based encoding, or large labeled datasets. To address these challenges, we propose GENIE-ASI, the first training-free, large language model (LLM)-based methodology for analog subcircuit identification. GENIE-ASI operates in two phases: it first uses in-context learning to derive natural language instructions from a few demonstration examples, then translates these into executable Python code to identify subcircuits in unseen SPICE netlists. In addition, to evaluate LLM-based approaches systematically, we introduce a new benchmark composed of operational amplifier netlists (op-amps) that cover a wide range of subcircuit variants. Experimental results on the proposed benchmark show that GENIE-ASI matches rule-based performance on simple structures (F1-score = 1.0), remains competitive on moderate abstractions (F1-score = 0.81), and shows potential even on complex subcircuits (F1-score = 0.31). These findings demonstrate that LLMs can serve as adaptable, general-purpose tools in analog design automation, opening new research directions for foundation model applications in analog design automation. |
| title | GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification |
| topic | Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2508.19393 |