Year: 2024
Type: article
Abstract: This paper presents a framework that integrates Large Language Models (LLMs) into translation validation, targeting LLVM compiler transformations where formal verification tools fall short. Our framework utilizes the existing tools, like Alive2, to perform initial validation. For transformations deemed unsolvable by traditional methods, our approach leverages fine-tuned LLMs to predict soundness or unsoundness, with subsequent fuzzing applied to identify counterexamples for unsound transformations. Our approach has proven effective in complex scenarios, such as deep-learning accelerator designs, enhancing the reliability of compiler transformations.
Authors: Yanzhao Wang, Fei Xie
Institution: Portland State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402283392