  This paper studies the computational offloading of CNN inference in
device-edge co-inference systems. Inspired by the emerging paradigm semantic
communication, we propose a novel autoencoder-based CNN architecture (AECNN),
for effective feature extraction at end-device. We design a feature compression
module based on the channel attention method in CNN, to compress the
intermediate data by selecting the most important features. To further reduce
communication overhead, we can use entropy encoding to remove the statistical
redundancy in the compressed data. At the receiver, we design a lightweight
decoder to reconstruct the intermediate data through learning from the received
compressed data to improve accuracy. To fasten the convergence, we use a
step-by-step approach to train the neural networks obtained based on ResNet-50
architecture. Experimental results show that AECNN can compress the
intermediate data by more than 256x with only about 4% accuracy loss, which
outperforms the state-of-the-art work, BottleNet++. Compared to offloading
inference task directly to edge server, AECNN can complete inference task
earlier, in particular, under poor wireless channel condition, which highlights
the effectiveness of AECNN in guaranteeing higher accuracy within time
constraint.
