NSF Award
2324854
Artificial Intelligence (AI) has recently emerged as a prominent field of research in computing, with enormous potential to positively influence society and it is projected to grow considerably in the coming years. The AI market is anticipated to expand tenfold into a sector worth hundreds of billions of dollars within the next five years. However, the rapid expansion of AI technologies has resulted in considerable environmental impact and raised concerns about their associated operational and embodied carbon footprints. The former is associated with their ongoing operation and maintenance while the latter with the entire life cycle of the devices. This project aims to substantially improve the sustainability of modern edge data centers by mitigating the environmental impact of AI and boosting the efficiency of data processing. It will advance beyond AI, towards the development of sustainable edge data centers. It will also promote the adoption of sustainable practices in the computing industry as a whole. As a result, it will contribute to a broader paradigm shift towards embracing more eco-friendly practices in the computing domain and it will strengthen efforts in driving the digital infrastructure’s transition towards a greener and more sustainable future.<br/><br/>The central aim of this project is to improve the sustainability of edge data centers used for AI computing through extensive, multi-layered optimizations. Unlike conventional centralized data centers, edge data centers are designed to be closer to end-users and can harness renewable energy for their operations. This minimizes the need for extensive data transmission and enables a more localized computing infrastructure. This project moves beyond traditional optimization methods and targets vertical solutions as multiple parameters affect operational and embodied carbon footprints. More specifically, the operational carbon footprint can be lessened by harnessing renewable energy sources, enhancing power usage efficiency, and strategically distributing workload across various computing components. On the other hand, the embodied carbon footprint can be decreased by downscaling energy-efficient hardware accelerators, implementing low-footprint circuit designs, and enhancing system reliability to extend its lifespan. To cover all these aspects, this project will follow a carbon-first vertical approach that will initially develop robust circuit-level and architectural-level hardware accelerators for edge data centers, then will improve the system’s utilization, and will eventually efficiently manage edge data centers at the run-time level with renewable energy sources. Overall, the development of reliable hardware accelerators and efficient utilization of computing resources will lead to more energy-efficient data centers, by reducing their carbon footprint and improving their overall sustainability. Furthermore, integrating renewable energy sources in the management of edge data centers will promote the use of clean energy and reduce reliance on non-renewable sources.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
