# Improved vectorization of OpenCV algorithms for RISC-V CPUs

arXiv: https://arxiv.org/abs/2311.12808 · HTML: https://mlsanity.com/paper/2311.12808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Test-driving RISC-V Vector hardware for HPC](https://mlsanity.com/paper/2304.10319/) (2023)
- [Case Study for Running Memory-Bound Kernels on RISC-V CPUs](https://mlsanity.com/paper/2305.09266/) (2023)

## Abstract (arXiv)

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.