10 citations · 21 across the 7 of their papers we have counts for
7 papers
VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading
Cheng Xu, Xiaofeng Hou, Jiacheng Liu +1
Large-scale vision-language mixture-of-experts (VL-MoE) models provide strong multimodal capability, but efficient deployment on memory-constrained platforms remains difficult. Exi…
Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading
Wenfeng Wang, Jiacheng Liu, Xiaofeng Hou +7
Mixture-of-experts (MoE) models often exceed a single accelerator's memory capacity. Offloading experts to host memory creates a severe PCIe I/O bottleneck because data-dependent r…
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
Xinfeng Xia, Xiaofeng Hou, Jiacheng Liu +5
Mixture-of-Experts (MoE) scales model capacity through sparse activation, and is becoming an important architecture for large language models (LLMs). However, existing MoE serving…
A Survey on Inference Optimization Techniques for Mixture of Experts Models
Jiacheng Liu, Peng Tang, Wenfeng Wang +5
The emergence of large-scale Mixture of Experts (MoE) models represents a significant advancement in artificial intelligence, offering enhanced model capacity and computational eff…
HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
Peng Tang, Jiacheng Liu, Xiaofeng Hou +5
The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference…
MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding Their Hardware-Software Implications
Cheng Xu, Xiaofeng Hou, Jiacheng Liu +9
The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable of in…
DataCLUE: A Benchmark Suite for Data-centric NLP
Liang Xu, Jiacheng Liu, Xiang Pan +2
Data-centric AI has recently proven to be more effective and high-performance, while traditional model-centric AI delivers fewer and fewer benefits. It emphasizes improving the qua…