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We consider complexity of Deep Neural Networks (DNNs) and their associatedmassive over-parameterization. Such over-parametrization may entailsusceptibility to adversarial attacks, loss of interpretability and adverseSize, Weight and Power - Cost (SWaP-C) considerations. We ask if there aremethodical ways (regularization) to reduce complexity and how can we interprettrade-off between desired metric and complexity of DNN. Reducing complexity isdirectly applicable to scaling of AI applications to real world problems(especially for off-the-cloud applications). We show that presence andevaluation of the knee of the tradeoff curve. We apply a form of L0regularization to MNIST data and signal modulation classifications. We showthat such regularization captures saliency in the input space as well.

L0 Regularization Based Neural Network Design and Compression