Slimming of neural networks in machine learning environments

A method comprising:

• learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, wherein learning includes analyzing a plurality of channels associated with one or more layers of the first neural network;
• computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network;
• and
• pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels, wherein the one or more scaling factors comprise one or more numbers to indicate one or more relevance levels of the plurality of channels within the first neural network such that a minimum relevance level is indicated by a minimum threshold number.

At least one non-transitory machine-readable medium comprising instructions that when executed by a computing device, cause the computing device to perform operations comprising:

• learning a first neural network associated with machine learning processes to be performed by a processor of the computing device, wherein learning includes analyzing a plurality of channels associated with one or more layers of the first neural network;
• computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network;
• and
• pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels, wherein the one or more scaling factors comprise one or more numbers to indicate one or more relevance levels of the plurality of channels within the first neural network such that a minimum relevance level is indicated by a minimum threshold number.