Scalable AI Systems: Compression, Efficiency, and Hardware-Software Co-Design

Dear Colleagues,

The rapid growth of AI workloads—spanning large language models, graph neural networks, and data-intensive applications—has placed unprecedented demands on modern computing systems. Achieving scalability while maintaining efficiency requires innovations across the full system stack, from algorithms to hardware.

To address these challenges, there is an increasing demand for scalable and efficient AI systems that integrate innovations across algorithms, system architectures, and hardware-software co-design. Techniques such as model and data compression, communication reduction, and distributed optimization are becoming essential for efficient AI deployment. Equally important are accelerator-aware computing and heterogeneous system design, which bridge the gap between algorithmic demands and hardware capabilities. This Special Issue is organized in collaboration with the workshop "Data Compression for AI and Big Data Application" (DC4AI) at the 55th International Conference on Parallel Processing (ICPP 2026). Extended versions of selected workshop papers, as well as high-quality submissions from the broader research community, are welcome.

We encourage researchers and practitioners from academia and industry to submit original research and review articles on scalable AI systems, efficient machine learning infrastructures, emerging computing platforms, and hardware-software co-design for AI and big data applications. Contributions that bridge algorithms, systems, and architectures for large-scale intelligent computing are of particular interest.

Dr. Zhaorui Zhang
Dr. Xiaodong Yu
Guest Editors

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