  Current architectures are now equipped with matrix computation units designed
to enhance AI and high-performance computing applications. Within these
architectures, two fundamental instruction types are matrix multiplication and
vector outer product, with the latter being lighter due to its vector inputs.
This characteristic not only allows for the development of flexible algorithms
beyond dense linear algebra computations but also offers greater potential for
implementation optimization. Stencil computations, commonly found in scientific
and engineering applications, involve nested loops. This paper introduces a
novel stencil algorithm leveraging vector outer products. Unlike previous
approaches, this algorithm emerges from the stencil definition in scatter mode
and is initially formulated using vector outer product expressions. The
implementation integrates a series of optimizations to enhance memory reference
patterns, execution pipeline efficiency, and data reuse. These optimizations
consider various algorithmic options and data sharing among input vectors.
Evaluation conducted on a simulator demonstrates that our proposed design
achieves significant speedup compared to vectorized stencil algorithms.
