collaborators

7 papers

cs.DC2026

UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing

Xinming Wei, Chao Jin, Tuo Dai +10

Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute straggle…

cs.DC2026

HeRo: Adaptive Orchestration of Agentic RAG on Heterogeneous Mobile SoC

Maoliang Li, Jiayu Chen, Zihao Zheng +5

With the increasing computational capability of mobile devices, deploying agentic retrieval-augmented generation (RAG) locally on heterogeneous System-on-Chips (SoCs) has become a…

cs.AI2026

DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams

Jincheng Lou, Ruohan Xu, Jiapeng Li +6

System-level diagrams encode the architectural blueprint of chip design, specifying module functions, dataflows, and interface protocols. However, non-standardized symbols and the…

cs.DC2026

Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC

Xinming Wei, Jiahao Zhang, Haoran Li +6

Personal LLM agents increasingly combine foreground reactive interactions with background proactive monitoring, forming long-lived, stateful LLM flows that interleave prefill and t…

cs.AI2024

OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

Liwei Ni, Rui Wang, Miao Liu +10

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning~(ML) applications within the logic synthesis process. Previo…

cs.SE2024

VerilogReader: LLM-Aided Hardware Test Generation

Ruiyang Ma, Yuxin Yang, Ziqian Liu +4

Test generation has been a critical and labor-intensive process in hardware design verification. Recently, the emergence of Large Language Model (LLM) with their advanced understan…