32 citations · 52 across the 7 of their papers we have counts for
7 papers
Taming Request Imbalance: SLO-Aware Scheduling for Disaggregated LLM Inference
Qipeng Wang
In production environments, large language model (LLM) serving is required to meet stringent service-level objectives (SLOs) amid highly variable request patterns. In practice, req…
Anatomizing Deep Learning Inference in Web Browsers
Qipeng Wang, Shiqi Jiang, Zhenpeng Chen +6
Web applications have increasingly adopted Deep Learning (DL) through in-browser inference, wherein DL inference performs directly within Web browsers. The actual performance of in…
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Mengwei Xu, Wangsong Yin, Dongqi Cai +15
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine lear…
Empowering In-Browser Deep Learning Inference on Edge Devices with Just-in-Time Kernel Optimizations
Fucheng Jia, Shiqi Jiang, Ting Cao +9
Web is increasingly becoming the primary platform to deliver AI services onto edge devices, making in-browser deep learning (DL) inference more prominent. Nevertheless, the heterog…
Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading
Daliang Xu, Mengwei Xu, Qipeng Wang +6
This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal…
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang, Qipeng Wang, Mengwei Xu +4
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is h…