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

Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms