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CNN在设备- 前沿共发系统中的计算卸载CNN 参数推论。 在新兴范例语义通信的启发下, 我们提出一个新的基于自动编码的CNN架构( ACNNN), 以便在终端设备中有效提取特性。 我们设计了一个基于CNN频道关注方法的功能压缩模块, 以便通过选择最重要的功能压缩中间数据。 为了进一步降低通信间接费用, 我们可以使用昆虫编码来消除压缩数据中的统计冗余。 在接收器中, 我们设计了一个轻量解码器, 以便通过从接收的压缩数据中学习来重建中间数据, 以提高准确性。 为了加快趋同, 我们使用渐进式方法来培训基于 ResNet- 50 架构获得的神经网络。 实验结果显示, ACNNN可以将中间数据压缩256x以上, 其精度损失只有4%左右, 从而超越了最先进的工作, BottleNetNet++。 比较了直接从下卸载任务到边缘服务器, AECNNNN 的精度, 在较弱的状态下, 保证了无线的精确度。