  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$\times$ 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$\times$ carbon efficiency improvement using advanced 3D integration
techniques for resource-constrained XR systems.
