Reducing the Computation Load of Convolutional Neural Networks through Gate Classification

Reducing the computational load of Convolutional Neural Networks (CNNs) is of utmost importance to allow their execution in computing systems characterized by constraints on computation and energy (e.g., embedded and cyber-physical systems and Internet-of-Things). To address this problem, which has been rarely addressed in the related literature, this paper introduces the Gate-Classification CNNs. The core of this novel family of CNNs is the presence of Gate-Classification layers that allow to incrementally process the input image through the CNN layers and take a decision as soon as 'enough confidence' about the classification is gained, hence not requiring the processing of the whole CNN when not needed. The Gate-Classification CNNs rely on the ability of CNNs to process features characterized by increasing complexity and meaning and, in particular, the Gate-Classification layers allow to select the path within the CNN according to the information content provided by the input image and the processed features. A wide experimental campaign on public-available datasets supports the effectiveness of the proposed solution.