As computing hardware becomes more specialized, designing environmentally sustainable computing systems requires accounting for both hardware and software parameters. Our goal is to design low carbon computing systems while maintaining a competitive level of performance and operational efficiency. Despite previous carbon modeling efforts for computing systems, there is a distinct lack of holistic design strategies to simultaneously optimize for carbon, performance, power and energy. In this work, we take a data-driven approach to characterize the carbon impact (quantified in units of CO2e) of various artificial intelligence (AI) and extended reality (XR) production-level hardware and application use-cases. We propose a holistic design exploration framework to optimize and design for carbon-efficient computing systems and hardware. Our frameworks identifies significant opportunities for carbon efficiency improvements in application-specific and general purpose hardware design and optimization. Using our framework, we demonstrate 10× carbon efficiency improvement for specialized AI and XR accelerators (quantified by a key metric, tCDP: the product of total CO2e and total application execution time), up to 21% total life cycle carbon savings for existing general-purpose hardware and applications due to hardware over-provisioning, and up to 7.86× carbon efficiency improvement using advanced 3D integration techniques for resource-constrained XR systems.
@article{arxiv.2305.01831,
title = {Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems},
author = {Mariam Elgamal and Doug Carmean and Elnaz Ansari and Okay Zed and Ramesh Peri and Srilatha Manne and Udit Gupta and Gu-Yeon Wei and David Brooks and Gage Hills and Carole-Jean Wu},
journal= {arXiv preprint arXiv:2305.01831},
year = {2023}
}