Abstract:
AI is becoming an integral part of our lives—helping with everything from drafting emails to diagnosing diseases. But behind the scenes, AI tools consume massive amounts of electricity. Training just one large AI model can use as much energy as thousands of U.S. homes do in a year. And as AI adoption grows, so does its appetite for power.

This surge in energy demand drives up operational costs, puts pressure on already-congested power infrastructure, and makes it harder to bring AI to places without reliable electricity. These barriers are becoming major bottlenecks for deploying and scaling AI systems.

Through the ML Energy Initiative, we’re working to address these challenges. We’ve built tools to measure energy use, reduce waste, and design AI systems that are more efficient—without sacrificing the accuracy of AI models. In this talk, I’ll share how smarter energy use can make AI more affordable and accessible.

Meet Mosharaf Chowdhury:
Mosharaf Chowdhury is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His research focuses on making AI/ML workloads more efficient, with a particular emphasis on reducing their energy consumption through the ML Energy Initiative. Major open-source projects from his team include Infiniswap, the first scalable memory disaggregation solution; FedScale, a planetary-scale AI/ML platform; TPP, a tiered memory manager integrated into the Linux kernel (v5.18+); and Zeus, the first energy-optimal generative AI stack. Previously, Mosharaf invented the concept of coflows and was one of the original creators of Apache Spark. He has received numerous individual honors, including fellowships and paper awards from NSDI, OSDI, ATC, and MICRO.