Neural network unit with memory layout to perform efficient 3-dimensional convolutions

Abstract

A neural network unit convolves an H×W×C input with F R×S×C filters to generate F Q×P outputs. N processing units (PU) each have a register receiving a respective word of an N-word row of a second memory and multiplexed-register selectively receiving a respective word of an N-word row of a first memory or word rotated from an adjacent PU multiplexed-register. H first memory rows hold input blocks of B words each of channels of respective 2-dimensional input row slices. R×S×C second memory rows hold filter blocks of B words each holding P copies of a filter weight. B is the smallest factor of N greater than W. The PU blocks multiply-accumulate input blocks and filter blocks in column-channel-row order; they read a row of input blocks and rotate it around the N PUs while performing multiply-accumulate operations so each PU block receives each input block before reading another row.