Energon: A Sustainability-Driven Modeling Framework for AI Data Centers

Abstract

Recent exponential investments in data centers, purposefully built for artificial intelligence (AI) workloads, have raised significant concerns around the sustainability of AI. The need for holistic sustainability analysis during the initial design phase of data centers and their components has become increasingly stringent. Such analysis must account for future technological advances in hardware and software, uphold a high level of accuracy, and operate with high speed to explore the large design space of a data center system. Sustainability in this context is a multifaceted concept encompassing various metrics such as performance, power consumption, energy efficiency, physical footprint, cost, and both embodied and operational emissions. This work aims to continue the discussion around such early design phase sustainability analysis by introducing Energon: a uniquely positioned, fast, end-to-end framework for early hardware-software codesign of carbon-efficient AI data centers.