  Convolutional Neural Networks (CNNs) exhibit remarkable performance in
various machine learning tasks. As sensor-equipped internet of things (IoT)
devices permeate into every aspect of modern life, it is increasingly important
to run CNN inference, a computationally intensive application, on resource
constrained devices. We present a technique for fast and energy-efficient CNN
inference on mobile SoC platforms, which are projected to be a major player in
the IoT space. We propose techniques for efficient parallelization of CNN
inference targeting mobile GPUs, and explore the underlying tradeoffs.
Experiments with running Squeezenet on three different mobile devices confirm
the effectiveness of our approach. For further study, please refer to the
project repository available on our GitHub page:
https://github.com/mtmd/Mobile_ConvNet
