3 papers
cs.DC2026
Ascend to Science: Exploration of AI Chips for Scientific Computing
Weicheng Xue, Kai Yang, Yongxiang Liu +5
The rapid rise of AI-oriented accelerators has reshaped compute systems around low-precision tensor engines, raising a practical question for the HPC community: under what conditio…
cs.DC2026
SGEMM-cube: Precision-Recovery FP32 GEMM Approximation on Ascend NPUs with FP16 Matrix Engines
Weicheng Xue, Baisong Xu, Kai Yang +4
Modern AI accelerators provide high-throughput low-precision matrix engines, but often lack efficient support for FP32 GEMM. This paper presents SGEMM-cube, an FP32-accuracy GEMM a…
cs.AI2026
AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units
Xinzi Cao, Jianyang Zhai, Pengfei Li +17
To meet the ever-increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. However, unlocking their full pote…