Deep learning optimization through zero tile manipulation

A computer implemented method for generating tile data structure comprised of zero-tiles from a weight-based data structure, the computer-implemented method comprising:

• pruning, by the processor, a weight-based data structure, wherein the weight-based data structure is formatted into a plurality of rows and columns;
• permuting, by the processor, the pruned weight-based data structure into a tile format and augmenting the neural network with one or more permute layers;
• determining, by the processor, if the permuted weight-based data structure contains any zero-tiles;
• generating, by the processor, empty tile index vectors, wherein the empty tile index vectors are used to configure the gating logic in the processing tiles.

A computing system for manipulating zero-tile data structures, the computer structure comprising:

• one or more computer processors;
• one or more computer readable storage devices;
• and
• computer program instructions, the computer program instructions being stored on the one or more computer readable storage devices for execution by the one or more computer processors to perform one or more operations comprising:
• generate a weight-based data structure based on zero weights within the data structure, wherein the weight-based data structure is a matrix comprised of weights associated with a deep learning network;
• generate a plurality of empty tile index vectors, based at least in part on the data processing program;
• and
• generate a data processing program for the permuted data structure, based at least in part on the plurality of tiles and a parallel processing unit;
• execute the processing program on the parallel processing unit, wherein the parallel processing unit receives at least one of the empty tile index vectors that causes the parallel processing unit to switch off a component in a multiply and accumulate unit.