The power that machine learning models consume when making predictions can be
affected by a model's architecture. This paper presents various estimates of
power consumption for a range of different activation functions, a core factor
in neural network model architecture design. Substantial differences in
hardware performance exist between activation functions. This difference
informs how power consumption in machine learning models can be reduced