In the Eleventh edition of EMC2 workshop, we plan to facilitate conversation about the sustainability of large-scale AI computingsystems being developed to meet the ever-increasing demands of generative AI. This involves discussions spanning multiple interre-lated areas. First, we continue to serve as the leading forums for discussing the energy-efficiency aspect of GenAI workloads whichdirectly impact the overall viability and economic value of AI technology. Second, we reassess the scaling laws of AI with theprevalence of agentic, multi-modal, and reasoning-based models in conjunction with novel techniques such as a highly sparse expertarchitecture and disaggregated computation. Finally, we discuss sustainable and high-performance computing paradigms towardsefficient datacenters and hybrid computing models that can cater to the exponential growth in model sizes, application areas, anduser base. This would allow us to explore ideas to build the hardware, software, systems, and scaling infrastructure, as well as modelarchitectures that make AI technology even more prevalent and accessible.

Submission Guidelines

We invite full-length papers describing original, cutting-edge, and even work-in-progress research projects about efficient machine learning. Suggested topics for papers include, but are not limited to the ones listed below:

• Neural network architectures for resource constrained applications
• Efficient hardware designs to implement neural networks including sparsity, locality, and systolic designs
• Power and performance efficient memory architectures suited for neural networks
• Network reduction techniques – approximation, quantization, reduced precision, pruning, distillation, and reconfiguration
• Exploring interplay of precision, performance, power, and energy through benchmarks, workloads, and characterization
• Performance potential, limit studies, bottleneck analysis, profiling, and synthesis of workloads
• Explorations and architctures aimed to promote sustainable computing
• Simulation and emulation techniques, frameworks, tools, and platforms for machine learning
• Optimizations to improve performance of training techniques including on-device and large-scale learning
• Load balancing and efficient task distribution, communication and computation overlapping for optimal performance
• Verification, validation, determinism, robustness, bias, safety, and privacy challenges in AI systems
• Efficient deployment strategies for edge and distributed environments.
• Model compression and optimization techniques that preserve reasoning and problem-solving capabilities
• Architectures and frameworks for multi-agent systems and retrieval-augmented generation (RAG) pipelines.
• Systems-level approaches for scaling future foundation models (e.g., Llama 4, GPT-5 and beyond).

We will follow that same formatting guidelines and duplicate submission policies as HPCA.

Venue

The workshop will be held in conjuction with HPCA-2025 in Las Vegas, NV.

Contact

All questions about submissions should be emailed to sushantkondguli@meta.com