Jingze Shi

2 accepted papers

2026

OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale

Jingze Shi, Zhangyang Peng, Yizhang Zhu, Yifan WU, Guang Liu, Yuyu Luo

ICML 2026poster

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 granularity of expert specialization and hardware execution efficiency. In this paper, we propose OmniMoE, a system-algo…

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2026

Towards Automated Kernel Generation in the Era of LLMs: A Survey

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang +8

IJCAI 2026

The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programm

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