Reducing Datacenter Compute Carbon Footprint by Harnessing the Power of Specialization: Principles, Metrics, Challenges and Opportunities

Abstract

Computing is an indispensable tool in addressing climate change, but it also contributes to a significant and steadily increasing carbon footprint, partly due to the exponential growth in energy-demanding workloads, such as artificial intelligence (AI). While hardware specialization has become the primary driver of operational energy efficiency improvements, it introduces new challenges including increased embodied emission, and a rise in complexity of operations of heterogeneous and dynamic datacenters. We posit that while specialization is necessary for sustainable computing, to fully harness its power, the academic and technical community must address the specific challenges arising from embracing it. We enumerate and analyze key challenges that specialization introduces across software, system design, and operations, and their potential impact on carbon cost, and propose a way forward for each identified area. Furthermore, we argue that intricate relationships exist across the life-cycle of compute systems, which must be understood, modeled, and analyzed to identify the most beneficial Pareto frontiers for carbon life-cycle efficiency. We analyze these trade-offs and offer an approach to address them using a unified metric and framework.