用AI提升电路级SPICE代码生成准确率，解决硬件知识缺失问题
SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
- 构建基于PySpice的Python数据集与自动化框架
- 实现多种电路配置下SPICE代码自动生成与标准化评估
- 适合硬件设计与AI交叉研究者使用
大型语言模型（LLM）在代码生成方面展现出巨大潜力，但其在电路级SPICE代码生成中仍受限于缺乏硬件专业知识。本文分析并识别了现有LLM在SPICE代码生成中的典型缺陷。为此，我们提出了SPICEPilot——一个基于PySpice生成的Python数据集及其配套框架。该框架实现了不同电路配置下SPICE仿真脚本的自动化创建，引入了标准化基准评估指标以衡量LLM的电路生成能力，并规划了将LLM集成到硬件设计流程的路径。SPICEPilot已开源，许可证为MIT，项目地址：https://github.com/ACADLab/SPICEPilot.git。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown great potential in automating code generation; however, their ability to generate accurate circuit-level SPICE code remains limited due to a lack of hardware-specific knowledge. In this paper, we analyze and identify the typical limitations of existing LLMs in SPICE code generation. To address these limitations, we present SPICEPilot a novel Python-based dataset generated using PySpice, along with its accompanying framework. This marks a significant step forward in automating SPICE code generation across various circuit configurations. Our framework automates the creation of SPICE simulation scripts, introduces standardized benchmarking metrics to evaluate LLM's ability for circuit generation, and outlines a roadmap for integrating LLMs into the hardware design process. SPICEPilot is open-sourced under the permissive MIT license at https://github.com/ACADLab/SPICEPilot.git.
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