3 papers
cs.AI2026
KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?
Peiyu Zang, Jian Tao, Jialing Zhang +4
Modern AI systems depend on specialized accelerator kernels, whose development is complicated by increasingly diverse operators and hardware. LLMs and agentic systems promise to au…
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…