RESEARCH OF APPLICABILITY LIMITATIONS FOR ELBRUS MICROPROCESSORS FOR SOLVING TASKS OF TECHNICAL VISION
One of the key areas in the artificial intelligence is technical vision. For resource-intensive tasks of technical vision high-performance, computing systems are created with use of specialized accelerators. The use of such accelerators is necessary due to the inability of general-purpose microprocessors (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 vision problems in both server and on-board modes, and the appearance of the sixth-generation Elbrus microprocessors should further improve performance on such tasks. Due to the high cost, greater complexity and limitations in the use of systems with specialized accelerators, the question arises of determining the conditions under which, it is sufficient to use CPU's to solve the tasks of technical vision, 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 and classification of objects. For the detection of objects one of the popular methods is the Viola-Jones method. Convolutional neural networks are usually used to solve the classification problem. Mathematical models of computations have been developed for VGG16 and VGG19 neural networks in 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 tasks is substantiated. Also, based on these methods, programs for modeling detection and classifications objects in the image and video stream have been developed. The programs are written in C++ using the OpenCV library, OPO Elbrus, the GNS Platform library and the ImageNet competition database. Using the implemented programs, comparative testing was carried out on a number of 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 of searching for objects in the image for input resolutions up to 1920 x 1080, where the processing speed of the video stream is more than 20 frames per second.