2 papers
cs.SE2026
LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have mad…
cs.DC2026
AscendCraft: Automatic Ascend NPU Kernel Generation via DSL-Guided Transcompilation
Zhongzhen Wen, Shudi Shao, Zhong Li +4
The performance of deep learning models critically depends on efficient kernel implementations, yet developing high-performance kernels for specialized accelerators remains time-co…