Power Consumption Variation over Activation Functions

The power 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. The field of deep neural networks has reported strong progress in many problem areas, including natural language processing (NLP), image recognition, and game playing.