GENERIC MODULAR SPARSE THREE-DIMENSIONAL (3D) CONVOLUTION DESIGN UTILIZING SPARSE 3D GROUP CONVOLUTION

摘要

An apparatus includes one or more processors (102) including a graphics processor (108) to process data; and a memory for storage of data, including feature maps. The one or more processors (102) are to provide for sparse 3D convolution acceleration by applying a shared 3D convolutional kernel/filter to an input feature map to produce an output feature map, including increasing sparsity of the input feature map by partitioning it into multiple disjoint input groups; generation of multiple disjoint output groups corresponding to the input groups by performing a convolution calculation represented by the shared 3D convolutional kernel/filter on all feature values associated with active/valid voxels of each input group to produce corresponding feature values within corresponding output groups; and outputting the output feature map by sequentially stacking the output groups.