  Tensor accelerators have gained popularity because they provide a cheap and
efficient solution for speeding up computational-expensive tasks in Deep
Learning and, more recently, in other Scientific Computing applications.
However, since their features are specifically designed for tensor algebra
(typically dense matrix-product), it is commonly assumed that they are not
suitable for applications with sparse data. To challenge this viewpoint, we
discuss methods and present solutions for accelerating sparse matrix
multiplication on such architectures. In particular, we present a 1-dimensional
blocking algorithm with theoretical guarantees on the density, which builds
dense blocks from arbitrary sparse matrices. Experimental results show that,
even for unstructured and highly-sparse matrices, our block-based solution
which exploits Nvidia Tensor Cores is faster than its sparse counterpart. We
observed significant speed-ups of up to two orders of magnitude on real-world
sparse matrices.
