This workshop aims to bring together the systems, HPC, and machine learning communities to explore the growing role of sparsity as a foundational tool for scaling efficiency across modern computing workloads, from scientific computing and HPC to LLMs. By fostering collaboration among researchers, practitioners, and industry experts, the workshop will focus on (a) developing novel architectures and system techniques that exploit sparsity at multiple levels and (b) deploying sparsity-aware models effectively in real-world scientific and AI applications. Topics of interest include unstructured sparsity, quantization, MoE architectures, and other innovations that drive computational efficiency, scalability, and sustainability across the full system stack.

Call For Papers

Sparsity has become a defining feature in modern computing workloads, from scientific simulations on HPC platforms to inference and training in cutting-edge LLMs. Sparsity appears across all layers of the stack: bit-level computations, sparse data structures, irregular memory access patterns, and high-level architectural design such as MoEs and dynamic routing. Although sparsity offers enormous potential to improve computing efficiency, reduce energy consumption, and enable scalability, its integration into modern systems introduces significant architectural, algorithmic, and programming challenges.

We invite submissions that address any aspect of sparsity in computing systems. Topics of interest include, but are not limited to:

• Sparsity in scientific and HPC applications.
• Sparse inference and training techniques in LLMs and foundation models.
• Architectural support for unstructured and structured sparsity.
• MoE and dynamic routing models: performance, systems, and deployment.
• Quantization, pruning, and compression methods that induce or leverage sparsity.
• Compiler, runtime, and scheduling frameworks for sparse workloads.
• Benchmarks, metrics, and tools to evaluate sparse systems.
• Hardware/software co-design for sparsity-aware execution.
• Sparse acceleration in near-memory, neuromorphic, and analog computing.
• Sparsity in edge and low-power AI systems.
• Programming models and abstractions for sparse computing.
• Case studies of sparse systems deployed on a scale.
• Combinatorial algorithms and graph computations over irregular or not rigidly structured data (beyond matrices and tensors).
• Techniques for handling the interaction between dense and sparse data structures (e.g., in property graphs combining tables and graphs).

We welcome complete papers, early stage work, and position papers that inspire discussion and foster community building. We target a soft limit of 4 pages, formatted in double-column style, similar to the main MICRO submission. If you have any questions please feel free to reach out to Bahar Asgari [bahar at umd dot edu] or Ramyad Hadidi [rhadidi at d-matrix dot ai]

Important Info:

• Submission Deadline: August 31, 2025, 11:59pm PST.
• Author Notification: September 9, 2025, 11:59pm PST (before MICRO's early registration deadline).
• Submission Link: hotcrp

Organizers:

• Co-Chairs

• Bahar Asgari | Assistant Professor | Department of Computer Science at the University of Maryland, College Park (UMD)
• Ramyad Hadidi | Senior Staff Engineer | d-Matrix

• Organizer Committee (Alphabetical Order)

• Ben Feinberg | Senior Member of Technical Staff | the Scalable Computer Architecture Group at Sandia National Laboratories
• Christina Giannoula | Incoming Faculty | Max Planck Institute for Software Systems (MPI-SWS)
• Olivia Hsu | Incoming Assistant Professor | Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU)
• Prashant J. Nair | Assistant Professor and Senior Principal Engineer | the University of British Columbia (UBC) and d-Matrix
• Antonino Tumeo | Chief Scientist | The Future Computing Technologies Group at Pacific Northwest National Laboratory (PNNL)
• Farzaneh Zokaee | System-on-Chip Architect | Ampere Computing

• Student Volunteers

• Ubaid Bakhtiar | PhD Student | Department of Electrical and Computer Engineering at the University of Maryland, College Park (UMD)
• Donghyeon Joo | PhD Student | Department of Computer Science at the University of Maryland, College Park (UMD)
• Sanjali Yadav | PhD Student | Department of Computer Science at the University of Maryland, College Park (UMD)
• Amirmahdi Namjoo | PhD Student | Department of Computer Science at the University of Maryland, College Park (UMD)
• Jeonghyun Woo | PhD Student | Department of Electrical and Computer Engineering at the University of British Columbia (UBC)
• Junsu Kim | PhD Student | Department of Electrical and Computer Engineering at the University of British Columbia (UBC)