9 citations · 13 across the 10 of their papers we have counts for
11 papers
X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction
Xiaoming Ren, Ru Zhen, Chao Li +11
Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report…
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…
CODO: An Automated Compiler for Comprehensive Dataflow Optimization
Weichuang Zhang, Yiquan Wang, Xinzhou Zhang +6
FPGAs are well-suited for dataflow architectures that process data in a streaming or pipelined manner, thus satisfying the high computational and communication demands of emerging…
PCR: A Prefetch-Enhanced Cache Reuse System for Low-Latency RAG Serving
Wenfeng Wang, Xiaofeng Hou, Peng Tang +5
Retrieval-Augmented Generation (RAG) systems enhance the performance of large language models (LLMs) by incorporating supplementary retrieved documents, enabling more accurate and…
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…
Survey of Disaggregated Memory: Cross-layer Technique Insights for Next-Generation Datacenters
Jing Wang, Chao Li, Taolei Wang +4
The growing scale of data requires efficient memory subsystems with large memory capacity and high memory performance. Disaggregated architecture has become a promising solution fo…
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…
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
Mingzhe Gao, Jieru Zhao, Zhe Lin +5
Recently, the use of large language models (LLMs) for software code generation, e.g., C/C++ and Python, has proven a great success. However, LLMs still suffer from low syntactic an…
FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework
Junyi Mei, Shixuan Sun, Chao Li +9
Dynamic graph random walk (DGRW) emerges as a practical tool for capturing structural relations within a graph. Effectively executing DGRW on GPU presents certain challenges. First…