Advanced technologies of Sensorics and Internet of Things (IoT) enable real-time data analytics based on multiple sensors covering the target industrial production system and its manufacturing processes. The rolling bearings fault diagnosis is one of the most urgent problems and can be solved by using convolution neural networks and edge artificial intelligence (edge AI) devices. The limitations of the hardware platform must be taken into account to achieve maximum performance. In this paper, we analyze efficient CNN architecture for bearings fault diagnosis that is able to process data in real-time on edge AI devices. We observe that the accuracy of the proposed CNN is unsatisfactory for practical use, and better accuracy is possible with increasing the number of bearings in the training dataset.