Dynamic Pruning of CNN networks

Abstract

A new, radical CNN dynamic pruning approach is presented in this paper, achieved by a new holistic intervention on both the CNN architecture and the training procedure, which targets to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. Our approach formulates a systematic and data-driven method for developing CNNs that are trained to eventually change size and form in real-time during inference, targeting to the smaller possible computational footprint. Results are provided for the optimal implementation on a few modern, high-end mobile computing platforms indicating a significant speed-up.