Papers by Seung Hun Eddie Han
CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark (2026.acl-long)
Copied to clipboard
Ahmed Heakl, Gustavo Bertolo Stahl, Sarim Hashmi, Seung Hun Eddie Han, Mukul Ranjan, Arina Kharlamova, Salman Khan, Abdulrahman Mahmoud
| Challenge: | Cross-architecture GPU code translation is essential for unlocking low-level hardware portability, yet no scalable solution exists. |
| Approach: | They propose a dataset and model suite for source- and assembly-level GPU code translation that trains domain-specific translation models that achieve 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 . |
| Outcome: | The proposed model achieves 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 outperforming commercial baselines including GPT-5.1, Claude-4.5, and Hipify by wide margins. |