Automatic Filter Pruning Technique For Convolutional Neural Networks

A computer-implemented method for pruning a convolutional neural network, the convolutional neural network includes a plurality of convolutional layers and each convolutional layer includes a plurality of neurons implemented in hardware, comprising:

• configuring a scaling neural subnetwork to extract descriptors from filters of a convolutional layer of the convolutional neural network and infer importance of the filters;
• for at least one convolutional layer in the convolutional neural network, connecting the scaling neural subnetwork to the respective convolutional layer, where an input layer of the scaling neural subnetwork is configured to receive weights of the filters in the respective convolutional layer and an output layer of the scaling neural subnetwork outputs a scale vector, such that elements of the scale vector quantify importance of filters in the respective convolutional layer;
• training the convolutional neural network, including the scaling neural subnetworks connected thereto;
• and
• removing filters from the at least one convolutional layer based on elements of a scale vector output by the respective scaling neural subnetwork, where filters are removed from the at least one convolutional layer after training of the convolutional neural network.