  The development of an open and free RISC-V architecture is of great interest
for a wide range of areas, including high-performance computing and numerical
simulation in mathematics, physics, chemistry and other problem domains. In
this paper, we discuss the possibilities of accelerating computations on
available RISC-V processors by improving the vectorization of several computer
vision and machine learning algorithms in the widely used OpenCV library. It is
shown that improved vectorization speeds up computations on existing prototypes
of RISC-V devices by tens of percent.
