Large Language Models (LLMs) spanning language, vision, audio, and other modalities are rapidly transforming the AI landscape, enabling a wide range of downstream applications. As demand for more capable models continues to rise, both model scale and training data volume have expanded substantially. Training, fine-tuning, and serving such models increasingly rely on large-scale high-performance computing (HPC) systems and remain highly resource- and time-intensive.

Data compression has emerged as a promising means of mitigating communication and data-movement overhead in distributed and parallel environments for modern AI and big-data workloads. Because data movement across the Internet, inter-node networks, and system interconnects has become a major determinant of both runtime and energy consumption, efficient mechanisms for data transfer and analysis are increasingly critical.

This workshop addresses key research challenges in reducing data-movement and communication costs for large-scale AI and big-data applications, including model training, fine-tuning, inference, and emerging LLM-based agent and multi-agent systems.

Topics of interest include but are not limited to:

• Data Compression Methods

  ° Compression Techniques for Structured and Unstructured Scientific Data

  ° Image, Video, and Multimedia Data Compression

  ° Time-series Data Compression

  ° Textual Data Compression (Natural Language, Logs)

  ° Quantization and Data Reduction

  ° Predictive Coding and Transform-based Compression

  ° Dictionary-based and Entropy-based Compression

  ° Tensor Decomposition and Low-rank Approximations

  ° Compression-aware Data Mining and Machine Learning

  ° Compression for Accelerating Data Analytics

• Applying Data Compression in AI-Related Applications and Systems

  ° Large-Scale AI Model Training

  ° Large-Scale AI Model Fine-Tuning

  ° Large-Scale AI Inference/Serving

  ° LLMs-Based Agent and Multi-Agent System Designing

  ° Data Compression for Communication Reduction

  ° Data Compression to Reduce Memory and Storage Overhead

• Hardware Co-Design for Applying Data Compression in Emerging AI Applications, Big Data Applications, and Quantum Computing

  ° GPUs

  ° FPGAs

  ° Quantum Computing Platforms

  ° CXL: Compute Express Link

  ° PIM: Process in Memory

  ° RISC-V

  ° ARM

Workshop Organizers

Xiaoyi Lu, University of Florida, USA (xiaoyilu@ufl.edu)

Xiaodong Yu, Stevens Institute of Technology, USA (xyu38@stevens.edu)

Sheng Di, Argonne National Laboratory, USA (sdi1@anl.gov)

Zhaorui Zhang, Hong Kong Polytechnic University, Hong Kong (zhaorui.zhang@polyu.edu.hk)

TPC Members

Xin Liang, Oregon State University

Kai Zhao, Florida State University

Dingwen Tao, Chinese Academy of Sciences

Jiajun Huang, University of South Florida

Robert Underwood, Argonne National Laboratory

Yafan Huang, University of Maryland, College Park

Boyuan Zhang, University of Kentucky

Jiannan Tian, Oakland University

Yu Liu, The Hong Kong Polytechnic University

Shiyu Hu, Nanyang Technological University

Benben Liu, The University of Hong Kong

Web Chair

Zichao Xia, The Hong Kong Polytechnic University