CHANNEL PRUNING OF A CONVOLUTIONAL NETWORK BASED ON GRADIENT DESCENT OPTIMIZATION

摘要

Techniques and mechanisms for determining the pruning of one or more channels from a convolutional neural network (CNN) based on a gradient descent analysis of a performance loss. In an embodiment, a mask layer selectively masks one or more channels which communicate data between layers of the CNN. The CNN provides an output, and calculations are performed to determine a relationship between the masking and a loss of the CNN. The various masking of different channels is based on respective random variables and on probability values each corresponding to a different respective channel. In another embodiment, the masking is further based on a continuous mask function which approximates a binary step function.