Efficient normalization techniques to optimize AI models for deployment in tactical edge
Billy Geerhart, Venkateswara R. Dasari, Peng Wang, David Alexander · 2021
Normalizations used in model reduction can be chosen to emphasize anything from computation reduction to parameter reduction. Choosing a normalization that emphasizes a model with a small number of parameters is useful when deploying a model onto machines with a limited communication rate, while choosing a normalization that emphasizes a model with a small computational cost is useful when deploying a model onto a machine for real-time sensor analysis. As such, we explore the effect of various normalizations used to prune kernel parameters on models trained on the ImageNet database.