Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems
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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 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.
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