PERFORMING DYNAMIC SPARSE COMPUTATION ON DENSE COMPUTATION-EFFICIENT COMPUTING DEVICES

Embodiments of the present disclosure include techniques processing dynamically sparse neural networks as dense computations. A permutation is performed to translate an input tensor from a sparse format into a dense format. Once in a dense format, dense computation can be performed to generate output data that is also in the dense format. A reverse permutation may then be performed to translate the output data back into the sparse format. An analysis of the operator is performed prior to runtime to determine the one or more dimensions of the tensor expression associated with the operator that are permutation invariant. The permutation may permutate the input tensor across dimensions that are permutation invariant.