DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency. An effective pruning scheme should be able to systematically remove connections and/or neurons that are unnecessary or redundant, reducing the DNN size without any loss in accuracy. In this paper we show that prior pruning schemes require an extremely time-consuming iterative process that requires retraining the DNN many times to tune the pruning hyperparameters. We propose a DNN pruning scheme based on Principal Component Analysis and relative importance of each neuron's connection that automatically finds the optimized DNN in one shot without requiring hand-tuning of multiple parameters.
@article{arxiv.1906.02535,
title = {(Pen-) Ultimate DNN Pruning},
author = {Marc Riera and Jose-Maria Arnau and Antonio Gonzalez},
journal= {arXiv preprint arXiv:1906.02535},
year = {2019}
}