  With the development of Internet of Things (IoT), data is increasingly
appearing on the edge of the network. Processing tasks on the edge of the
network can effectively solve the problems of personal privacy leaks and server
overload. As a result, it has attracted a great deal of attention and made
substantial progress. This progress includes efficient convolutional neural
network (CNN) models such as MobileNet and ShuffleNet. However, all of these
networks appear as a common network model and they usually need to identify
multiple targets when applied. So the size of the input is very large. In some
specific cases, only the target needs to be classified. Therefore, a small
input network can be designed to reduce computation. In addition, other
efficient neural network models are primarily designed for mobile phones.
Mobile phones have faster memory access, which allows them to use group
convolution. In particular, this paper finds that the recently widely used
group convolution is not suitable for devices with very slow memory access.
Therefore, the EdgeCNN of this paper is designed for edge computing devices
with low memory access speed and low computing resources. EdgeCNN has been run
successfully on the Raspberry Pi 3B+ at a speed of 1.37 frames per second. The
accuracy of facial expression classification for the FER-2013 and RAF-DB
datasets outperforms other proposed networks that are compatible with the
Raspberry Pi 3B+. The implementation of EdgeCNN is available at
https://github.com/yangshunzhi1994/EdgeCNN
