As the computing industry has grown dramatically in the past few decades, so has the environmental footprint created by such technologies as artificial intelligence and cloud computing. The industry is now looking beyond the operational carbon footprint, or the carbon created by a computer system. Instead, it is considering the life cycle carbon footprint, which includes both the operational and the embodied carbon footprint. The embodied carbon footprint is the carbon created in the manufacturing of a smartphone, an AI system, or other hardware. Before you can optimize computing’s carbon footprint, though, you must be able to measure it. And measuring the embodied carbon footprint has been challenging to date.
Measuring the embodied carbon footprint
To solve this dilemma, researchers from Meta and Harvard University have created an architectural carbon modeling tool (ACT) to quantify and optimize the embodied carbon footprint. Traditionally, system architects would design a given computer system to optimize its performance, power, and chip area. With ACT, system architects can design and optimize hardware to minimize carbon yields as well as to optimize for performance and energy efficiency. For example, some smartphones contain hardware components that aren’t needed for all user applications. These extra components add to the carbon footprint of that phone. So when optimizing for application use cases, a system designer can use ACT to predict the additional carbon cost of a particular functionality. In doing so, the system designer could incorporate only those hardware components that are critical for the desired applications, consequently optimizing the life cycle carbon footprint of the phone. Put differently, system designers can now quantify the trade-offs between given features and their contribution to the phone’s overall carbon footprint. And more broadly speaking, the community can use ACT to measure and ultimately reduce the environmental footprint of technologies such as AI, as system architects can design greener AI systems from the start.