NEON Instruction Set Efficiency and their Use in Coding Theory Problems

A large number of algorithms solving Coding Theory problems involve operations on vectors over finite fields. The use of extended CPU registers and instructions is suitable for the optimization of these algorithms. Current work presents the Neon instruction set for the ARM architectures used in Apple's M series of processors. A method for their application in algorithms for finding weight characteristics is considered. Some basic similarities and differences with extended vector instructions available in other architectures are presented. Their performance is compared with non-vectorized methods.

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Publication Details

Journal
Innovative STEM Education
Published
2024-12-26
DOI
https://doi.org/10.55630/stem.2024.0628
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

NEON Instruction Set Efficiency and their Use in Coding Theory Problems

Maria Pashinska-Gadzheva, Iliya Bouyukliev
Innovative STEM Education
Neural Networks and Applications
article

NEON Instruction Set Efficiency and their Use in Coding Theory Problems

Maria Pashinska-Gadzheva, Iliya Bouyukliev
article en

Abstract

A large number of algorithms solving Coding Theory problems involve operations on vectors over finite fields. The use of extended CPU registers and instructions is suitable for the optimization of these algorithms. Current work presents the Neon instruction set for the ARM architectures used in Apple's M series of processors. A method for their application in algorithms for finding weight characteristics is considered. Some basic similarities and differences with extended vector instructions available in other architectures are presented. Their performance is compared with non-vectorized methods.

Innovative STEM EducationVol. 6(1)
Bulgarian Academy of Sciences (BG), Institute of Mathematics and Informatics (BG)
Affordable and clean energy
Openalex Percentile: Top 78%
Neural Networks and Applications
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