  Handling vast amounts of data is crucial in today's world. The growth of
high-performance computing has created a need for parallelization, particularly
in the area of machine learning algorithms such as ANN (Approximate Nearest
Neighbors). To improve the speed of these algorithms, it is important to
optimize them for specific processor architectures. RISC-V (Reduced Instruction
Set Computer Five) is one of the modern processor architectures, which features
a vector instruction set called RVV (RISC-V Vector Extension). In machine
learning algorithms, vector extensions are widely utilized to improve the
processing of voluminous data. This study examines the effectiveness of
applying RVV to commonly used ANN algorithms. The algorithms were adapted for
RISC-V and optimized using RVV after identifying the primary bottlenecks.
Additionally, we developed a theoretical model of a parameterized vector block
and identified the best on average configuration that demonstrates the highest
theoretical performance of the studied ANN algorithms when the other CPU
parameters are fixed.
