@inproceedings{6c87d02e5f66433b8c1533d3c3869cb8,
title = "SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance",
abstract = "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{\textquoteright}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.",
keywords = "circuit design, LLM-powered code generation, SPICE",
author = "Deepak Vungarala and Sakila Alam and Arnob Ghosh and Shaahin Angizi",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th Annual IEEE International Conference on Rebooting Computing, ICRC 2024 ; Conference date: 16-12-2024 Through 17-12-2024",
year = "2024",
doi = "10.1109/ICRC64395.2024.10937006",
language = "English (US)",
series = "2024 IEEE International Conference on Rebooting Computing, ICRC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE International Conference on Rebooting Computing, ICRC 2024",
address = "United States",
}