ICDS News

Associate Director talks AI growth and sustainability

EDITOR’S NOTE: A version of this article was originally published in Penn State News.

UNIVERSITY PARK, Pa.  — Mahmut Kandemir’s, Penn State Institute for Computational and Data Sciences (ICDS) associate director and co-hire, work optimizing computer systems for efficiency and speed has lent a hand in finding a connection between his research and the environmental impact of artificial intelligence.

In an article with Penn State News, Kandemir notes that the rapid growth and expansion of AI systems could drive higher water usage, emissions and e-waste, which causes urgent sustainability concerns.

Kandemir, who is also a distinguished professor in the Department of Computer Science and Engineering, highlighted the need for proactive solutions like streamlining AI models, developing greener infrastructure and fostering collaboration across disciplines.

The concerns focus not only on environmental sustainability, but also on reducing carbon footprints and making AI models more efficient. These models require a lot of training, adjusting and billions of parameters through repeated computations which require a lot of processing power or compute power. The trainings involve thousands of graphics processing units, or GPUs, running continuously for months, according to Kandemir. Smaller institutions with limited resources would take significantly longer to train these models, leading to even more energy consumption. The process needs high-performance computing infrastructure, and the models will need retraining over time.

Data centers could account for 20% of global electricity use by 2030-35, causing strain on power grids, Kandemir said.

According to Kandemir, the environmental impact extends beyond high electricity use. The models consume large amounts of fossil-fuel-based electricity, which contributes to greenhouse gas emissions. And the need for advanced cooling systems in data centers also leads to excessive water consumption.

In the Q&A with Penn State News, Kandemir suggests several strategies to reduce AI’s environmental footprint while maintaining technological advancements including optimizing AI models to use fewer resources without compromising performance; developing domain specific AI models customized for particular fields; developing AI-specific accelerators or hardware beyond GPUs; transitioning data centers to renewable energy sources like solar and wind; and distribute AI computations across different time zones.

Universities and research organizations play a role in making AI more sustainable, too.

“They can conduct precise footprint assessments of AI workloads to better understand and mitigate the energy impact of AI technologies. Encouraging sustainability through research strategic plans and policy recommendations can push the industry towards greener solutions and influence regulatory decisions,” Kandemir said in the article.

Researchers can also seek out interdisciplinary collaborations with policymakers, computer scientists and environmental researchers to create programs, workshops and other discussions to take a proactive role and drive change.

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