Systems and methods for reducing memory requirements in neural networks

A method for processing neural network data, the method comprising:

• determining one or more active neural network layers in a neural network using counters, wherein the counters control a set of input data by counting input pixels into the one or more active neural network layers relative to at least one value to identify the one or more active neural network layers;
• using the one or more active neural network layers to process a subset of the set of input data of a first neural network layer, the subset having a data size that is less than the size of the set of input data;
• outputting a first set of output data from the first neural network layer;
• using the first set of output data in a second neural network layer;
• and
• outputting a second set of output data from the second neural network layer prior to processing all of the set of input data.

A system for processing large amounts of neural network data, the system comprising:

• a processor;
• and
• a non-transitory computer-readable medium comprising instructions that, when executed by the processor, cause steps to be performed, the steps comprising:
• determining one or more active layers in a neural network using counters, wherein the counters control a set of input data by counting at least one of input bytes of data and input pixels into the one or more active neural network layers relative to at least one value to identify the one or more active neural network layers;
• using the one or more active layers to process a subset of the set of input data of a first neural network layer the subset having a data size that is less than the size of the set of input data;
• outputting a first set of output data from the first network layer;
• using first set of output data in a second neural network layer;
• and
• outputting a second set of output data from the second network layer prior to processing all of the set of input data.