🌐 AI Benchmarking: Environmental Impact, Performance, and Energy Usage
Welcome to the first AI x Climate cluster meetup!
The first step in addressing the energy usage and environmental impact of AI is accurate measurement. We'll be hearing from Micah Hill-Smith (Artificial Analysis), David Kanter (MLCommons), and Yi Ding (Purdue University) who are leading the charge in developing benchmarks for AI environmental impact, performance, and energy usage.
This meetup is hosted by Guthrie Gintzler.
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About the speakers:
David Kanter is Co-Founder and Head of MLPerf at MLCommons, a collaborative engineering consortium best known for developing the MLPerf benchmarks, which standardize measurement of ML hardware and software performance — including energy efficiency. His team has focused on measuring the power consumption of both AI inference and training systems pioneering the first-ever power benchmarks for large-scale AI training workloads.
Micah Hill-Smith is Co-Founder & CEO of Artificial Analysis, an independent AI benchmarking and insights company that helps engineers and organizations navigate the AI landscape. The company measures speed, cost, and energy alongside model intelligence. Artificial Analysis has become a go-to resource for developers and enterprises making decisions about AI models and infrastructure.
Yi Ding is an Assistant Professor at Purdue University's Elmore Family School of Electrical and Computer Engineering, where she leads the STYLE Lab focused on sustainable AI and computing systems. Her research goes beyond carbon to tackle the full environmental footprint of AI — including water and biodiversity — and produced FABRIC, the first framework to quantify how computing systems affect global ecosystems from chip fabrication to data center operation. Among her key findings: low carbon doesn't always mean low biodiversity impact.
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