Accelerating convolutional neural network computation throughput

An apparatus for a convolutional neural network (CNN), comprising:

• one or more processors, coupled to a memory that includes instructions to execute operations of the one or more processors, configured to:
• extract data features that comprise convolutional data sets via one or more filter kernels from one or more images;
• transform the convolutional data sets into sets of nonlinear output data;
• and
• generate pooling outputs from pooling operations on the sets of nonlinear output data via a pipelining process that operates in parallel with at least one of an extraction of the data features or a transformation of the convolutional data sets, in response to determining that a first set of nonlinear data among the sets of nonlinear output data or a first set of convolutional data of the convolutional data sets is available.

A method for a convolutional neural network (CNN), comprising:

• performing, via one or more processors, convolution operations that extract data from one or more images;
• providing, via the one or more processors, the data to one or more rectified linear units (RELUs) configured to generate non-linear convolution output data;
• generating, via the one or more processors, pooling outputs in parallel with the convolution operations based on a pooling window for a subset of the non-linear convolution output data;
• and
• providing, via the one or more processors, artificial neural network (ANN) outputs of an ANN based on the pooling outputs.