Improved vectorization of OpenCV algorithms for RISC-V CPUs
This work addresses performance bottlenecks for users of RISC-V CPUs in computer vision and machine learning, but it is incremental as it builds on existing OpenCV methods.
The paper tackled the problem of accelerating computations on RISC-V processors by improving vectorization of OpenCV algorithms, resulting in speedups of tens of percent on existing prototypes.
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.