11 citations · 17 across the 7 of their papers we have counts for
6 papers · 1 filter
EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models
Kunxiong Zhu, Zhihao Shu, Hangyu Zheng +4
Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multimodal LLMs (MLLMs), which add a…
Mask-Aware Execution for Efficient JEPA Training
Md Musfiqur Rahman Sanim, Zhihao Shu, Bahram Afsharmanesh +3
Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models across vision, video, audio, bra…
TierKV: Long-Context On-Device LLMs via Predictive Multi-Tier KV Caching
Zhihao Shu, Md Musfiqur Rahman Sanim, Jie Hu +4
Large language models (LLMs) are moving onto mobile devices for increasingly diverse workloads over text, images, video, and audio. These applications often require long contexts,…
Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training
Yangming Zhang, Jian Xu, Chaojian Li +7
3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis with high-quality rendering through continuous aggregations of millions of 3D Gaussian primitives. However, it…
FlashMem: Supporting Modern DNN Workloads on Mobile with GPU Memory Hierarchy Optimizations
Zhihao Shu, Md Musfiqur Rahman Sanim, Hangyu Zheng +4
The increasing size and complexity of modern deep neural networks (DNNs) pose significant challenges for on-device inference on mobile GPUs, with limited memory and computational r…
From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
Kaiyuan Deng, Hangyu Zheng, Minghai Qing +11
Deploying models, especially large language models (LLMs), is becoming increasingly attractive to a broader user base, including those without specialized expertise. However, due t…