Enhancing Deep Learning Transpilation with LLMs: Automation and Semantic Consistency
Xingpei Li, Zhijie Jiang, Zhouyang Jia, Yuanliang Zhang, Si Zheng, Haifang Zhou, Shanshan Li · 2025
Deep learning (DL) transpilers perform source-to-source model conversion between DL frameworks, simplifying deployment. However, they often fail to bridge operator semantic inconsistencies, resulting in API incompatibilities (e.g., incompatible parameters, missing operators), which cause conversion failures or performance drops