title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½Ð° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¼ AI-ÑÑÐºÐ¾ÑÐ¸ÑÐµÐ»Ðµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research and application of modern neural network models using an AI accelerator
Creators: name: ÐÐ¾Ð½Ð´Ð°ÑÐµÐ½ÐºÐ¾, ÐÐ»Ð°Ð´Ð¸Ð¼Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð½ÐµÐ¹ÑÐ¾ÑÑÐºÐ¾ÑÐ¸ÑÐµÐ»Ñ
Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ° Ð²Ð¸Ð´ÐµÐ¾Ð´Ð°Ð½Ð½ÑÑ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
rk3588
yolov11
artificial intelligence
neural accelerator
video analytics
computer vision

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description: The given work is devoted to studying the possibilities of practical application of modern neural network models using a specialized computing module (AI accelerator), while solving an applied problem by developing a computer vision module. The research set the following goals: 1) Research and selection of computing devices (AI accelerators) that provide the possibility of practical application of modern neural network models. 2) Review, comparative analysis and selection of neural network models applicable on the selected AI accelerator. 3) Adaptation of the selected neural network model for use on an AI accelerator. 4) Development of an algorithm and software module that ensures the application of the selected neural network model on an AI accelerator. 5) Testing the adapted neural network model on an AI accelerator and analyzing the results obtained. As a result of the work, a study and review of specialized computing devices designed to accelerate the processing of neural network models was conducted, a suitable AI accelerator was selected, a review of modern neural network models applicable to the selected AI accelerator was performed, and a neural network model for computer vision was selected and adapted for use. An algorithm and software module were developed allowing to use of the selected neural network model, a block diagram of the developed algorithm and the results of each step are given. Using the provided software module and adapted model, the testing of the selected neural network model on the AI accelerator was carried out, and estimates of the real performance of the selected AI accelerator and neural network model were obtained. The obtained figures of the real performance allow us to speak about the high efficiency of the selected AI accelerator and the applicability of such devices for solving real world problems on a small-sized and energy-efficient edge device.