Analytic techniques for improved super tiling machine learning processing

A non-transitory program storage device comprising instructions stored thereon to cause one or more processors to:

• determine an amount of memory used to process layers of a machine learning network having multiple layers;
• smooth the amount of memory used to process the layers of the machine learning network based on a number of layers;
• identify change layers where the smoothed amount of memory used changes more than a memory change threshold amount;
• group the layers of the machine learning network into a first layer grouping based on the identified change layers;
• and
• output the first layer grouping.

A device, comprising:

• a memory;
• and
• one or more processors operatively coupled to the memory, wherein the one or more processors are configured to execute non-transitory instructions causing the one or more processors to:
• determine an amount of memory used to process layers of a machine learning network having multiple layers;
• smooth the amount of memory used to process the layers of the machine learning network based on a number of layers;
• identify change layers where the smoothed amount of memory used changes more than a memory change threshold amount;
• group the layers of the machine learning network into a first layer grouping based on the identified change layers;
• and
• output the first layer grouping.