GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification

• URL: http://arxiv.org/abs/2508.19393v1
• Date: Tue, 26 Aug 2025 19:39:10 GMT
• Title: GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification
• Abstract summary: We propose GENIE-ASI, the first training-free, large language model (LLM)-based methodology for analog subcircuit identification.<n> 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.<n> Experimental results show that GENIE-ASI matches rule-based performance on simple structures, remains competitive on moderate abstractions, and shows potential even on complex subcircuits.
• Score: 10.596711617836887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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