GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification
This addresses the problem of automating analog design for engineers by introducing a novel LLM-based approach, though it appears incremental as it builds on existing LLM capabilities for a specific domain.
The paper tackles analog subcircuit identification by proposing GENIE-ASI, a training-free LLM-based method that uses in-context learning to generate instructions and executable code, achieving F1-scores of 1.0 on simple structures, 0.81 on moderate abstractions, and 0.31 on complex subcircuits.
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.