Power Consumption Variation over Activation Functions

Abstract

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.

Leon Derczynski

Associate professor

My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.