Vijay Gadepally, a senior team member at MIT Lincoln Laboratory, leads a number of tasks at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient. Here, Gadepally talks about the increasing usage of generative AI in daily tools, its concealed environmental impact, and some of the manner ins which Lincoln Laboratory and the greater AI community can decrease emissions for a greener future.

Q: What patterns are you seeing in terms of how generative AI is being used in computing?

A: Generative AI uses machine knowing (ML) to create brand-new content, like images and text, based on information that is inputted into the ML system. At the LLSC we develop and construct a few of the biggest scholastic computing platforms in the world, and over the previous couple of years we’ve seen an explosion in the variety of tasks that require access to high-performance computing for generative AI. We’re also seeing how generative AI is altering all sorts of fields and domains - for instance, ChatGPT is already affecting the classroom and the work environment faster than policies can seem to keep up.

We can imagine all sorts of uses for generative AI within the next years or so, like powering highly capable virtual assistants, developing new drugs and materials, and even improving our understanding of fundamental science. We can’t forecast whatever that generative AI will be used for, however I can certainly say that with a growing number of complicated algorithms, their calculate, energy, and environment impact will continue to grow very quickly.

Q: What strategies is the LLSC using to reduce this environment impact?

A: We’re always looking for ways to make computing more efficient, as doing so assists our information center take advantage of its resources and permits our scientific colleagues to push their fields forward in as effective a way as possible.

As one example, we’ve been minimizing the amount of power our hardware consumes by making basic modifications, comparable to dimming or shutting off lights when you leave a space. In one experiment, we lowered the energy consumption of a group of graphics processing systems by 20 percent to 30 percent, with minimal effect on their efficiency, by implementing a power cap. This also reduced the hardware operating temperature levels, making the GPUs easier to cool and longer enduring.

Another method is altering our habits to be more climate-aware. At home, a few of us may choose to use sustainable energy sources or smart scheduling. We are utilizing comparable techniques at the LLSC - such as training AI designs when temperatures are cooler, or when local grid energy demand is low.

We also realized that a great deal of the energy invested in computing is typically wasted, like how a water leak increases your bill but with no advantages to your home. We developed some new methods that allow us to keep an eye on computing workloads as they are running and after that end those that are unlikely to yield great results. Surprisingly, in a number of cases we found that the bulk of calculations could be terminated early without compromising the end result.

Q: What’s an example of a task you’ve done that decreases the energy output of a generative AI program?

A: We recently constructed a climate-aware computer vision tool. Computer vision is a domain that’s focused on using AI to images