  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.
