LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
<|reference_start|>LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation: Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimization (BO)-based techniques for analog layout synthesis, despite their potential for automation, suffer from slow convergence and extensive data needs, limiting their practical application. This paper presents the \texttt{LLANA} framework, a novel approach that leverages Large Language Models (LLMs) to enhance BO by exploiting the few-shot learning abilities of LLMs for more efficient generation of analog design-dependent parameter constraints. Experimental results demonstrate that \texttt{LLANA} not only achieves performance comparable to state-of-the-art (SOTA) BO methods but also enables a more effective exploration of the analog circuit design space, thanks to LLM's superior contextual understanding and learning efficiency. The code is available at https://github.com/dekura/LLANA.<|reference_end|>
@article{chen2024llm-enhanced,
    title={LLM-Enhanced Bayesian Optimization for Efficient Analog Layout
  Constraint Generation},
    author={Guojin Chen, Keren Zhu, Seunggeun Kim, Hanqing Zhu, Yao Lai, Bei Yu,
  David Z. Pan},
    journal={arXiv preprint arXiv:2406.05250},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05250},
    primaryClass={cs.AI cs.AR cs.LG}
}