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)

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

Invited Speaker

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