  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.
