Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, and Cryptocurrency Mining have been estimated. In contrast to the bit-level switching, in which transistors achieved energy efficiency due to geometrical scaling, higher energy is expended both at the at the instructions and simulations levels of an application. Additionally, the analysis based on AI/ML Accelerators indicate that changes in architectures using an older semiconductor technology node have comparable energy efficiency with a different architecture using a newer technology. Further comparisons of the energy in computing systems with the thermodynamic and biological limits, indicate that there is a 27-36 orders of magnitude higher energy requirements for total simulation of an application. These energy estimates underscore the need for serious considerations of energy efficiency in computing by including energy as a design parameter, enabling growing needs of compute-intensive applications in a digital world.

翻译：本文确定并分析了从设备到算法的计算层级能量消耗。基于先前研究[3]，我们估算了单个设备与系统所需能量，涵盖人工智能/机器学习在自然语言处理、科学模拟及加密货币挖矿三大大规模计算应用。与通过几何缩放实现能效提升的位级晶体管开关不同，在应用指令层与模拟层上能耗显著增加。此外，基于AI/ML加速器的分析表明，采用较老半导体工艺节点的架构变更与使用较新工艺的不同架构具有相当的能效。将计算系统能量与热力学及生物学极限进行进一步比较后发现，应用完整模拟的能耗需求高出27-36个数量级。这些能量估算凸显了将能效作为设计参数纳入计算的必要性，以应对数字世界中计算密集型应用日益增长的需求。