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.

翻译：随着计算硬件日益专业化，设计环境可持续的计算系统需要同时考虑硬件和软件参数。我们的目标是在保持竞争性性能和运行效率的同时，设计低碳计算系统。尽管已有针对计算系统的碳建模工作，但目前明显缺乏能够同步优化碳、性能、功耗和能量的整体设计策略。在本工作中，我们采用数据驱动的方法，刻画了各类人工智能（AI）和扩展现实（XR）生产级硬件及应用用例的碳影响（以CO2当量单位量化）。我们提出了一个整体设计探索框架，用于优化和设计碳高效的计算系统与硬件。我们的框架识别了在应用特定和通用硬件设计与优化中实现碳效率提升的重大机遇。利用该框架，我们展示了专用AI和XR加速器可实现10倍的碳效率提升（通过关键指标tCDP量化：总CO2当量与总应用执行时间的乘积），因硬件过度配置，现有通用硬件及应用的总生命周期碳排放可节省高达21%，而对于资源受限的XR系统，采用先进3D集成技术可实现高达7.86倍的碳效率提升。