@inproceedings{e818f3282f8646238eaf8ad934c9f43f,
title = "C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?",
abstract = "High Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for refactoring C code into HLS-compatible formats. We present several case studies by using an LLM to rewrite C code for NIST 800-22 randomness tests, a QuickSort algorithm and AES-128 into HLS-synthesizable c. The LLM iteratively transforms the C code guided by user prompts, implementing functions like streaming data and hardware-specific signals. This evaluation demonstrates the LLM's potential to assist hardware design refactoring regular C code into HLS synthesizable C code.",
keywords = "Catapult HLS, Chip Design, Cryptocores, LLM",
author = "Luca Collini and Siddharth Garg and Ramesh Karri",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International LLM-Aided Design Workshop, LAD 2024 ; Conference date: 28-06-2024 Through 29-06-2024",
year = "2024",
doi = "10.1109/LAD62341.2024.10691856",
language = "English (US)",
series = "2024 IEEE LLM Aided Design Workshop, LAD 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE LLM Aided Design Workshop, LAD 2024",
}