I. Introduction
As computing becomes ubiquitous from intelligent sensing at the edge to digitalization of systems and with wider adoption of Artificial Intelligence and Machine Learning (AI/ML), it is clear that energy used in computing is expected to increase non-linearly [1], [2]. In continuation of the earlier analysis [3], in this work, additional quantitative analysis to include hardware components from the transistor level to including the Application have been provided, where ‘Application’ is defined to include the entire simulation by the process of computation. Energy for Application is a metric that includes algorithms and software similar to energy/instructions at the hardware system level and energy/bit switching at the transistor level. Specific examples of computational simulations included in this analysis are training of a large language model (LLM) or inference needed for machine learning applications [5], large-scale simulation of a single Covid virion particle [6], and computer mining of a single Crypto coin.