System and method for pruning filters in deep neural networks

One or more non-transitory computer-readable media storing instructions executable to perform operations for compressing a deep neural network (DNN), the operations comprising:

• accessing the DNN that has been trained, the DNN comprising a plurality of layers;
• generating a sequence of graph representations based on attributes of the plurality of layers, each of the plurality of layers represented by a graph representation in the sequence;
• clustering the plurality of layers into groups of layers based on the sequence of graph representations, each of the groups of layers comprising a subset of the plurality of layers;
• determining a pruning ratio for a group of layers based on the graph representations of the layers in the group, the pruning ratio indicating a percentage of filters to be pruned from the layers in the group;
• pruning filters of the layers in the group based on the pruning ratio to generate compressed layers;
• and
• updating the DNN by replacing the layers in the group with the compressed layers.

An apparatus for compressing a deep neural network (DNN), the apparatus comprising:

• a computer processor for executing computer program instructions;
• and
• a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations comprising:
• accessing the DNN that has been trained, the DNN comprising a plurality of layers,
• generating a sequence of graph representations based on attributes of the plurality of layers, each of the plurality of layers represented by a graph representation in the sequence,
• clustering the plurality of layers into groups of layers based on the sequence of graph representations, each of the groups of layers comprising a subset of the plurality of layers,
• determining a pruning ratio for a group of layers based on the graph representations of the layers in the group, the pruning ratio indicating a percentage of filters to be pruned from the layers in the group,
• pruning filters of the layers in the group based on the pruning ratio to generate compressed layers, and
• updating the DNN by replacing the layers in the group with the compressed layers.