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.

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A novel high-speed column-line driving scheme having output buffer amplifiers embedded with polarity multiplexer switches is proposed for use in large-sized thin-film transistor liquid-crystal displays. The proposed driving scheme does not have explicit output-polarity switches, resulting in lower settling time. Experimental results in a 1.2 μm 13.5 V CMOS process indicated that using the proposed driving scheme the settling times to reach 99% of target voltages for the dot and column inversions were improved by up to 48.6%. This driving scheme can be applied to class AB-or class B-type amplifiers for liquid-crystal display column drivers and output buffers controlled by output switches.

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