  In edge intelligence, deep learning~(DL) models are deployed at an edge
device and an edge server for data processing with low latency in the Internet
of Things~(IoT). In this letter, we propose a new end-to-end learning-based
wireless image recognition scheme using the PyramidNet in edge intelligence. We
split the PyramidNet carefully into two parts for an IoT device and the edge
server, which is to pursue low on-device computation. Also, we apply a
squeeze-and-excitation block to the PyramidNet for the improvement of image
recognition. In addition, we embed compression encoder and decoder at the
splitting point, which reduces communication overhead by compressing the
intermediate feature map. Simulation results demonstrate that the proposed
scheme is superior to other DL-based schemes in image recognition, while
presenting less on-device computation and fewer parameters with low
communication overhead.
