IMPLEMENTATION OF CONVENTIONAL NEURAL NETWORKS ON EMBEDDED DEVICES WITH A LIMITED COMPUTING RESOURCE

Abstract

Large amounts of video data captured by sensor sensors in various spectral ranges, the significant size of convolutional neural network architectures create problems with the implementation of neural network algorithms on peripheral devices due to significant limitations of computing resources on embedded computing devices. The article discusses the use of algorithms for automatic search and pattern recognition based on machine learning methods, implemented on embedded devices with a computing resource Graphics Processing Unit. Detection convolutional neural networks «You Only Look Once V3» and «You Only Look Once V3-Tiny» are used as a search and pattern recognition algorithm, which are implemented on embedded computing devices of the NVIDIA Jetson line, located in different price ranges and with different computing resources ... Also, in the work, the estimates ofalgorithms on embedded devices are experimentally calculated for such indicators as power consumption, forward passage time of a convolutional neural network, and detection accuracy. On the basis of solutions implemented, both at the hardware level and in software, presented by NVIDIA, it becomes possible to use deep neural network algorithms based on the convolution operation in real time. Computational optimization methods offered by NVIDIA are considered. Experimental studies of the influence of computations with reduced accuracy on the speed and accuracy of object detection in images of the investigated architectures of convolutional neural networks, which were previously trained on a sample of images consisting of the PASCAL VOC 2007 and PASCAL VOC 2012 datasets, have been carried out.