EMC2 @ ASPLOS ’26

11th Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMC2)
co-located with ASPLOS 2026
March 22, 2026
Pittsburgh, PA
https://www.emc2-ai.org/

EMC2 workshop aims to facilitate conversation about the sustainability of large-scale AI computing systems being developed to meet the ever-increasing demands of generative AI. This involves discussions spanning multiple interrelated areas.

Topics of interest

• 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 architectures 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).

Important Dates

Paper Submission January 31, 2026 (23:59 PST)
Acceptance and Author Notification February 15, 2026 (23:59 PST)