ИССЛЕДОВАНИЕ ОГРАНИЧЕНИЙ ПРИМЕНИМОСТИ МИКРОПРОЦЕССОРОВ РЯДА ЭЛЬБРУС ДЛЯ РЕШЕНИЯ ЗАДАЧ ТЕХНИЧЕСКОГО ЗРЕНИЯ

Abstract

One of the key areas in the artificial intelligence is technical vision. For resource-intensivetasks of technical vision high-performance, computing systems are created with use of specializedaccelerators. The use of such accelerators is necessary due to the inability of general-purposemicroprocessors (GPM) to solve such problems in a given time due to a high computational load.However, the microprocessors of Elbrus series are successfully used to solve technical visionproblems in both server and on-board modes, and the appearance of the sixth-generation Elbrusmicroprocessors should further improve performance on such tasks. Due to the high cost, greatercomplexity and limitations in the use of systems with specialized accelerators, the question arisesof determining the conditions under which, it is sufficient to use CPU’s to solve the tasks of technicalvision, for example, with the microprocessors of the Elbrus series without special accelerators.One of the most resource-intensive tasks in the field of technical vision are detection andclassification of objects. For the detection of objects one of the popular methods is the Viola-Jonesmethod. Convolutional neural networks are usually used to solve the classification problem.Mathematical models of computations have been developed for VGG16 and VGG19 neural networksin relation to the actual microprocessors of the Elbrus series. Using the developed models,the theoretical sufficiency of the performance of Elbrus microprocessors for technical vision tasksis substantiated. Also, based on these methods, programs for modeling detection and classificationsobjects in the image and video stream have been developed. The programs are written inC++ using the OpenCV library, OPO Elbrus, the GNS Platform library and the ImageNet competitiondatabase. Using the implemented programs, comparative testing was carried out on a numberof high-performance computing systems with Elbrus and Intel CPU’s and NVidia video card.Based on the results obtained, it is shown that the Elbrus-8S is sufficient to solve the problem ofsearching for objects in the image for input resolutions up to 1920 x 1080, where the processingspeed of the video stream is more than 20 frames per second.