Information and communication technologies account for a growing portion of global environmental impacts. While emerging technologies, such as emerging non-volatile memories (eNVM), offer a promising solution to energy efficient computing, their end-to-end footprint is not well understood. Understanding the environmental impact of hardware systems over their life cycle is the first step to realizing sustainable computing. This work conducts a detailed study of one example eNVM device: hafnium-zirconium-oxide (HZO)-based ferroelectric field-effect transistors (FeFETs). We present COFFEE, the first carbon modeling framework for HZO-based FeFET eNVMs across life cycle, from hardware manufacturing (embodied carbon) to use (operational carbon). COFFEE builds on data gathered from a real semiconductor fab and device fabrication recipes to estimate embodied carbon, and architecture level eNVM design space exploration tools to quantify use-phase performance and energy. Our evaluation shows that, at 2 MB capacity, the embodied carbon per unit area overhead of HZO-FeFETs can be up to 11% higher than the CMOS baseline, while the embodied carbon per MB remains consistently about 4.3x lower than SRAM across different memory capacity. A further case study applies COFFEE to an edge ML accelerator, showing that replacing the SRAM-based weight buffer with HZO-based FeFET eNVMs reduces embodied carbon by 42.3% and operational carbon by up to 70%.

翻译：信息与通信技术在全球环境影响中所占比重日益增长。虽然新兴非易失性存储器等新兴技术为高能效计算提供了有前景的解决方案，但其端到端的环境足迹尚未得到充分认知。理解硬件系统全生命周期的环境影响是实现可持续计算的第一步。本研究针对一种典型eNVM器件——基于铪锆氧化物的铁电场效应晶体管进行了详细分析。我们提出了COFFEE，这是首个面向HZO基FeFET eNVMs的全生命周期碳建模框架，涵盖从硬件制造到使用阶段的碳排放核算。COFFEE基于真实半导体产线数据与器件制造工艺估算隐含碳排放，并借助架构级eNVM设计空间探索工具量化使用阶段的性能与能耗。评估结果表明：在2MB容量下，HZO-FeFET的单位面积隐含碳开销可比CMOS基线最高增加11%，而单位MB的隐含碳在不同存储容量下均比SRAM稳定降低约4.3倍。进一步的案例研究将COFFEE应用于边缘ML加速器，表明用HZO基FeFET eNVMs替代SRAM权重缓存可使隐含碳降低42.3%，运行碳最高减少70%。