This paper introduces partitioning an inference task of a deep neural network
between an edge and a host platform in the IoT environment. We present a DNN as
an encoding pipeline, and propose to transmit the output feature space of an
intermediate layer to the host. The lossless or lossy encoding of the feature
space is proposed to enhance the maximum input rate supported by the edge
platform and/or reduce the energy of the edge platform. Simulation results show
that partitioning a DNN at the end of convolutional (feature extraction) layers
coupled with feature space encoding enables significant improvement in the
energy-efficiency and throughput over the baseline configurations that perform
the entire inference at the edge or at the host.

本文介绍了在物联网环境中，将深度神经网络的推断任务在边缘和主机平台之间进行分区，并提出了通过特征空间编码来提高边缘平台的最大输入速率和 / 或降低边缘平台的能量消耗的方法。模拟结果表明，在卷积层的末端对 DNN 进行划分并配合特征空间编码能够显着提高能效和吞吐量。