Parallelizing techniques for in-memory compute architecture

A compute tile, comprising:

• a plurality of compute engines, each of the plurality of compute engines including a compute-in-memory (CIM) hardware module, the CIM hardware module storing a plurality of weights and configured to perform at least a portion of a convolution;
• and
• a general-purpose (GP) processor coupled with the plurality of compute engines and configured to provide control instructions and data to the plurality of compute engines, wherein:
• the compute tile is configured to implement a model including a plurality of convolutions and a plurality of activation functions in a forward pass, the plurality of convolutions corresponding to the plurality of compute engines, the model further including a reschedule operation for a convolution of the plurality of convolutions, the reschedule operation being based on a profile of the model and the compute tile, the compute tile being configured to perform the reschedule operation in the forward pass, the reschedule operation providing a plurality of tensors based on an input tensor for the convolution, the plurality of tensors being configured to undergo at least a portion of the convolution in accordance with a temporal distribution.

A compute program product, embodied in a non-transitory computer readable medium and comprising computer instructions for:

• profiling a learning network including at least one compute tile and a model, a compute tile including a plurality of compute engines and a general-purpose (GP) processor, each of the plurality of compute engines including a compute-in-memory (CIM) hardware module, the model including a plurality of convolutions and a plurality of activation functions, the plurality of convolutions corresponding to the plurality of compute engines;
• determining, based on the profiling, a reschedule operation for a convolution of the plurality of convolutions, the reschedule operation providing a plurality of tensors based on an input tensor for the convolution, the plurality of tensors being configured to undergo at least a portion of the convolution in accordance with a temporal distribution;
• and
• wherein the learning network performs the reschedule operation as part of performing a forward pass.