Literature Database Entry

zhang2026crisp

Congwei Zhang, Youming Tao, Yifei Zou, Yu Yang, Ruirui Zhang, Xiaoyu Zhang, Falko Dressler, Xiuzhen Cheng and Dongxiao Yu, "CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers," Proceedings of 32nd ACM International Conference on Mobile Computing and Networking (MobiCom 2026), Austin, TX, October 2026. (to appear)

Abstract

Mixture-of-Experts (MoE) inference on memory-constrained edge platforms is often dominated by expert-weight movement rather than computation. Although each token activates only a small subset of experts, routing is input-dependent, so all experts must remain accessible and memory movement dominates latency. Existing approaches such as uniform pruning and expert dropping overlook two properties of MoE execution: expert combinations are highly skewed in frequency, and experts contribute asymmetrically within each combination. We present CRISP, a combination-aware framework that co-designs pruning and runtime scheduling for efficient MoE serving on memory-constrained edge platforms. CRISP reorders neurons by importance so one stored expert can support multiple execution widths, assigns differentiated execution to hot, warm, and cold combinations based on frequency and expert role, and exploits heterogeneous widths to overlap expert loading with computation. Across Mixtral-8x7B and DeepSeek-MoE-16B, CRISP improves throughput by up to 2.1× at the same memory budget while limiting accuracy loss to 3.6% relative to full-width inference. Our results highlight that efficient edge MoE serving requires jointly optimizing which expert capacity is preserved and how expert execution is scheduled.

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Congwei Zhang
Youming Tao
Yifei Zou
Yu Yang
Ruirui Zhang
Xiaoyu Zhang
Falko Dressler
Xiuzhen Cheng
Dongxiao Yu

BibTeX reference

@inproceedings{zhang2026crisp,
    author = {Zhang, Congwei and Tao, Youming and Zou, Yifei and Yang, Yu and Zhang, Ruirui and Zhang, Xiaoyu and Dressler, Falko and Cheng, Xiuzhen and Yu, Dongxiao},
    note = {to appear},
    title = {{CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers}},
    publisher = {ACM},
    address = {Austin, TX},
    booktitle = {32nd ACM International Conference on Mobile Computing and Networking (MobiCom 2026)},
    month = {10},
    year = {2026},
   }
   
   

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Last modified: 2026-08-31