geraudk:

Formulated differently: as a data scientist, are there cases that you cannot efficiently resolve with the current “gemm-heavy” approach, for instance with sparse tensors, or very large problems that are just incredibly slow even with the latest shiny GPU, etc…


Group convolutions with a large number of groups, dilated convolutions, strided convolutions – all these are not used as much as they should be because they bring sparsity and cache-unfriendly memory accesses into play.
See https://github.com/vdumoulin/conv_arithmetic 9 for reference of dilated, strided.