3 papers
cs.AI2026
KernelGenBench: A Multi-Source and Multi-Chip Benchmark for LLM-based Kernel Generation
Peiyu Zang, Jian Tao, Jialing Zhang +4
Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specialized and labor-intensive task…
cs.CL2026
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
Jingze Shi, Zhangyang Peng, Yizhang Zhu +3
Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade-off between the…
cs.LG2026
Towards Automated Kernel Generation in the Era of LLMs
Yang Yu, Peiyu Zang, Chi Hsu Tsai +11
The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level ha…