EMC2 - Energy Efficient Machine Learning and Cognitive Computing

description Workshop Objective

In the Twelfth edition of EMC2 workshop, we plan to discuss the enormous impact of agentic AI on diverse industries driving productivity but also creating unprecedented demands for computing capacity and energy. Assessing the sustainability of large-scale AI computing systems involves discussions spanning multiple interrelated areas. First, we continue to serve as the leading forums for discussing the energy-efficiency aspect of GenAI workloads which directly impact the overall viability and economic value of AI technology. Second, we examine the evolving scaling laws of AI with the prevalence of agentic and reasoning-based models in conjunction with novel techniques such as sparse architectures, speculative compute, and multi-architecture disaggregation. Finally, we discuss sustainable and high-performance computing paradigms towards efficient datacenters and hybrid computing models that can cater to the exponential growth in model sizes, application areas, and user base. This would allow us to explore ideas to build the hardware, software, systems, and scaling infrastructure, as well as model architectures that make AI technology even more prevalent and accessible.

format_list_bulleted Topics for the Workshop

• Energy Efficient LLMs/GenAI: In this era of AI, where a trillion is the norm (whether in model parameters, dataset size, or revenue expectations), there is a lot of misdirection and media attention — especially around generative AI and Large Language Models. While the focus often centers on building bigger systems, an equally important question is whether we can afford to run them at scale, both in terms of total cost of ownership (TCO) and return on investment (ROI). We address the fundamentals of language understanding and discuss future directions and ideas for building efficient models and systems to tackle more complex language problems and challenges.
• Algorithm-System Co-design for Reasoning: The capabilities of new AI systems have spread beyond plain language — be it multi-modal interaction or step-by-step reasoning for complex problem solving. One can expand this to also include multi-agent systems and retrieval-augmented generation (RAG) pipelines, as well as deployment strategies, particularly at the edge. These directions open up new challenges in model compression, efficiency, and optimization, all while striving to preserve reasoning capabilities and system robustness. We invite discussion about forward-looking projections: what is it going to take to scale the current systems to meet the compute demands of the next few generations of models (Kimi K4, GPT-6, and beyond)? And if current scaling laws suffice to meet the needs of a system that is capable of AGI, or whether we need to start thinking about this systems-level problem differently.
• Determinism and Consistency: Recent advances in large language models (LLMs) and generative diffusion models have unlocked remarkable capabilities in text, image, and 3D scene synthesis. However, a persistent challenge remains: these models often lack consistency and determinism, especially in tasks requiring spatial or temporal coherence. For example, 3D diffusion models may generate scenes that change unpredictably when the camera is rotated, resulting in non-identical outputs for what should be equivalent viewpoints. We will discuss methods that improve the reliability, repeatability, and physical plausibility of generative models. The topics will include architectural innovations, training strategies, evaluation metrics, and applications where consistency is critical, such as graphics, simulation, and virtual reality. The goal is to foster collaboration and spark new ideas for making generative AI more robust and trustworthy in real world scenarios.
• Sustainable High Performance Computing: The widespread adoption of Large Language Models (LLMs) in the industry has led to an exponential increase in computing demands and energy consumption, posing sustainability challenges. Efforts have been made in academia and industry to enhance efficiency via novel architectures and optimization techniques, but their success and viability remain open to debate. We discuss what sustainability means for the industry and how to develop new workflows for AI that prioritize environmental impact alongside performance and revenue.