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.

翻译：本文研究了设备-边缘协同推理系统中CNN推理的计算卸载问题。受新兴语义通信范式的启发，我们提出了一种新颖的基于自编码器的CNN架构（AECNN），用于在终端设备上进行高效特征提取。我们设计了基于CNN中通道注意力方法的特征压缩模块，通过选择最重要的特征来压缩中间数据。为进一步降低通信开销，可利用熵编码去除压缩数据中的统计冗余。在接收端，我们设计了一个轻量级解码器，通过学习接收到的压缩数据重构中间数据以提高精度。为加速收敛，我们采用逐步训练策略对基于ResNet-50架构的神经网络进行训练。实验结果表明，AECNN可将中间数据压缩超过256倍，同时仅有约4%的精度损失，优于当前最先进的工作BottleNet++。与将推理任务直接卸载到边缘服务器相比，AECNN能更早完成推理任务，尤其在无线信道条件恶劣的情况下，这凸显了AECNN在时间约束内保证更高精度的有效性。