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Make Each Iteration Count

Published: 30 July 2024 Publication History

Abstract

Large Language Models (LLMs) is widely used for code debugging (e.g., C and Python) but face limitations in debugging Register Transfer Level (RTL) code largely due to data scarcity. This paper presents Make Each Iteration Count (MEIC), a novel framework for RTL debugging. Unlike traditional approaches heavily relying on prompt engineering, model tuning, and model training, MEIC employs an iterative process with LLM to address syntax and function errors efficiently. We also introduce an open-source dataset with 178 RTL errors for evaluation. Results demonstrate a 93% fix rate for syntax errors and a 78% fix rate for function errors.

References

[1]
Matthew DeLorenzo 2024. Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS. arXiv preprint arXiv:2402.03289 (2024).
[2]
Kevin Laeufer 2024. RTL-Repair: Fast Symbolic Repair of Hardware Design Code. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3. 867–881.
[3]
Mingjie Liu 2023. Verilogeval: Evaluating large language models for verilog code generation. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). IEEE, 1–8.
[4]
YunDa Tsai 2023. RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models. arXiv preprint arXiv:2311.16543 (2023).
[5]
Ran Wei 2023. DECISIVE: Designing Critical Systems With Iterative Automated Safety Analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2023).
[6]
Xufeng Yao 2024. HDLdebugger: Streamlining HDL debugging with Large Language Models. arXiv preprint arXiv:2403.11671 (2024).
[7]
Yue Zhang 2023. Siren’s song in the ai ocean: A survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023).

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ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024
July 2024
261 pages
ISBN:9798400710117
DOI:10.1145/3674399
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 July 2024

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