Memory bandwidth reduction techniques for low power convolutional neural network inference applications

Abstract

Systems, apparatuses, and methods for implementing memory bandwidth reduction techniques for low power convolutional neural network inference applications are disclosed. A system includes at least a processing unit and an external memory coupled to the processing unit. The system detects a request to perform a convolution operation on input data from a plurality of channels. Responsive to detecting the request, the system partitions the input data from the plurality of channels into 3D blocks so as to minimize the external memory bandwidth utilization for the convolution operation being performed. Next, the system loads a selected 3D block from external memory into internal memory and then generates convolution output data for the selected 3D block for one or more features. Then, for each feature, the system adds convolution output data together across channels prior to writing the convolution output data to the external memory.