Abstract
Modern neural network methods make it possible to obtain qualitative results that open the colossal potential for integrating them into industrial applications. Therefore, there is a need for systems like the OpenVINO Deep Learning Workbench to analyze and optimize neural networks' performance on target devices. As the primary tool for inference, the OpenVINO open environment is used, which provides a wide variety of options to speed up network execution on the target hardware.
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