System and method for designing efficient super resolution deep convolutional neural networks by cascade network training, cascade network trimming, and dilated convolutions

An apparatus for generating a convolutional neural network (CNN), the apparatus comprising:

• one or more non-transitory computer-readable media;
• and
• at least one processor which, when executing instructions stored on the one or more non-transitory computer-readable media, performs the steps of:
• performing cascade network trimming of the CNN, the trimming comprising:
• trimming a number of layers of the CNN;
• and
• outputting the CNN based on whether a training error is converging and whether all layers have been trimmed.

An apparatus for generating a convolutional neural network (CNN), the apparatus comprising:

• one or more non-transitory computer-readable media;
• and
• at least one processor which, when executing instructions stored on the one or more non-transitory computer-readable media, performs the steps of:
• performing cascade training on the CNN to produce a CNN output by inserting one or more layers where a training error converges for each stage of the cascade training, until the training error is less than a threshold;
• and
• performing network trimming of the CNN output.

A method of manufacturing a chipset, the method comprising:

• at least one processor which, when executing instructions stored on one or more non-transitory computer-readable media, performs the steps of:
• performing cascade training on a CNN to produce a CNN output by inserting one or more layers where a training error converges for each stage of the cascade training until the training error is less than a threshold;
• and
• performing network trimming of the CNN output.

A method of testing an apparatus, the method comprising:

• testing whether the apparatus has at least one processor which, when executing instructions stored on one or more non-transitory computer-readable media, performs the steps of:
• performing cascade training on a CNN to produce a CNN output by inserting one or more layers where a training error converges for each stage of the cascade training until the training error is less than a threshold;
• and
• performing network trimming of the CNN output.