Parsimonious Inference On Convolutional Neural Networks Learning And Applying Online Kernel Activation Rules

# Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules

**arXiv ID:** 1701.05221
**Authors:** I. Theodorakopoulos, V. Pothos, D. Kastaniotis, N. Fragoulis
**Published:** 2017-01-18T20:03:12Z
**Abstract:**
A new, radical CNN design approach is presented in this paper, considering the reduction of the total computational load during inference. This is 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 or remove the redundant capacity of a CNN architecture. This is accomplished, by the introduction of a new structural element that can be inserted as an add-on to any contemporary CNN architecture, whilst preserving or even improving its recognition accuracy. 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 of up to x3 times.

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## References

- [arXiv:1701.05221](http://arxiv.org/abs/1701.05221v5)